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Le Weng, Le Weng, Chao Feng Le Weng, Zhi-Yuan Shi Chao Feng, Ying-Min Zhang Zhi-Yuan Shi, and Lian-Fen Huang Ying-Min Zhang. "FASSFuzzer—An Automated Vulnerability Detection System for Android System Services." 電腦學刊 33, no. 2 (2022): 189–200. http://dx.doi.org/10.53106/199115992022043302017.
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<p>As the core component of Android framework, Android system services provide a large number of basic and core function services for Android system. It has a lot of resources and very high system permissions. And for the Android system, it is a very important attack surface. Attackers can use Android system service vulnerabilities to steal user privacy, cause Android applications or Android system denial of service, remote malicious code execution and other malicious behaviors, which will seriously affect the security of Android users. Based on fuzzy testing technology, this paper designed and implemented a vulnerability mining system for Android system services, optimized and improved the fuzzy testing method, so as to improve the speed and effectiveness of vulnerability mining, and timely submitted the discovered vulnerabilities to the corresponding manufacturers and security agencies, to help Android manufacturers repair the vulnerabilities in time. The main work of this paper is as follows: Aiming at the null pointer reference vulnerability of Android system services, we designed and implemented an automatic fast mining system FASSFuzzer. FASSFuzzer uses ADB to quickly detect null pointer reference vulnerabilities in Android services. At the same time, FASSFuzzer added automatic design to automatically perceive the generation of vulnerabilities and ensure the full automation of the whole vulnerability mining process, and automatically generate a vulnerability mining report after the completion of vulnerability mining.</p> <p>&nbsp;</p>
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Le Weng, Le Weng, Chao Feng Le Weng, Zhi-Yuan Shi Chao Feng, Ying-Min Zhang Zhi-Yuan Shi, and Lian-Fen Huang Ying-Min Zhang. "FASSFuzzer—An Automated Vulnerability Detection System for Android System Services." 電腦學刊 33, no. 2 (2022): 189–200. http://dx.doi.org/10.53106/199115992022043302017.
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<p>As the core component of Android framework, Android system services provide a large number of basic and core function services for Android system. It has a lot of resources and very high system permissions. And for the Android system, it is a very important attack surface. Attackers can use Android system service vulnerabilities to steal user privacy, cause Android applications or Android system denial of service, remote malicious code execution and other malicious behaviors, which will seriously affect the security of Android users. Based on fuzzy testing technology, this paper designed and implemented a vulnerability mining system for Android system services, optimized and improved the fuzzy testing method, so as to improve the speed and effectiveness of vulnerability mining, and timely submitted the discovered vulnerabilities to the corresponding manufacturers and security agencies, to help Android manufacturers repair the vulnerabilities in time. The main work of this paper is as follows: Aiming at the null pointer reference vulnerability of Android system services, we designed and implemented an automatic fast mining system FASSFuzzer. FASSFuzzer uses ADB to quickly detect null pointer reference vulnerabilities in Android services. At the same time, FASSFuzzer added automatic design to automatically perceive the generation of vulnerabilities and ensure the full automation of the whole vulnerability mining process, and automatically generate a vulnerability mining report after the completion of vulnerability mining.</p> <p>&nbsp;</p>
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Luo, Fucai, Jingyi Xie, Jingdong Guo, Wenliang Liu, Jindong He, and Hang Zhang. "Vulnerability Detection System for Power Information Based on Network Traffic Identification Technology." Journal of Physics: Conference Series 2401, no. 1 (2022): 012042. http://dx.doi.org/10.1088/1742-6596/2401/1/012042.
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Abstract A power information vulnerability detection system has the problem of a low network survival rate. Therefore, a power information vulnerability detection system based on network traffic identification technology is designed. In the hardware part, the memory is configured as a synchronous interface and 4 DRAMs, and the level input and output power of the chip are kept in a state compatible with LVTTL levels; in the software part, the power information network indicators are obtained, the importance of network elements is reflected through a mesoscope, the failure characteristics of components are extracted using network traffic identification technology, a critical area is delineated, and the port protocols and association libraries of the passages are matched to optimize the system vulnerability detection function. Through analysis of the simulation results, it can be obtained that the network survival rate of the power information vulnerability detection system in this paper is 17.657% and 17.931% higher than that obtained by the other two power information vulnerability detection systems, respectively, indicating that the designed power information vulnerability detection system is more effective when fully integrated with network traffic identification techniques.
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Shiraishi, M., Y. Fujinuma, T. Ishikawa, K. Ishige, and H. Doki. "An Ultrasonic Double-Sheet Detection System for Collators." Journal of Engineering for Industry 114, no. 4 (1992): 489–93. http://dx.doi.org/10.1115/1.2900702.
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A new ultrasonic method of detecting double sheets in collators has been developed that overcomes several shortcomings of conventional detection techniques. An air curtain efficiently reduces the ultrasonic detector’s vulnerability to ambient temperature fluctuations. The accuracy of detection is enhanced by utilizing the low-level component of the received ultrasonic signal. A gain adjustment technique is introduced which enables detection for a wide range of paper stocks using a single threshold level.
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Hou, Jin-bing, Tong Li, and Cheng Chang. "Research for Vulnerability Detection of Embedded System Firmware." Procedia Computer Science 107 (2017): 814–18. http://dx.doi.org/10.1016/j.procs.2017.03.181.
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Jeo John, Precious, and Sumit Surendran. "Vulnerabilities Detection by Matching with known Vulnerabilities." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 01 (2024): 1–10. http://dx.doi.org/10.55041/ijsrem28422.
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Vulnerability Matcher is a tool designed to identify and prioritize security vulnerabilities in software systems. This intelligent system leverages advanced machine learning algorithms to analyze and match identified vulnerabilities with known security threats and exploits. The primary function of Vulnerability Matcher is to provide developers and security professionals with a comprehensive understanding of the security risks associated with their software. It does this by scanning the codebase and identifying potential security vulnerabilities that could be exploited by malicious actors. Vulnerability Matcher's speciality is its ability to prioritize identified vulnerabilities. By using machine learning techniques, it can determine which vulnerabilities pose the greatest risk and should be addressed first. This prioritization is based on factors such as the severity of the vulnerability, the likelihood of exploitation, and the potential impact on the system. In addition to identifying and prioritizing vulnerabilities, Vulnerability Matcher also provides actionable recommendations on how to remediate each vulnerability. These recommendations are tailored to the specific vulnerability and can range from simple code changes to more complex system modifications. Moreover, Vulnerability Matcher is continuously learning and improving. It uses feedback from its users to refine its algorithms and improve its accuracy. This continuous learning process ensures that Vulnerability Matcher remains up-to-date with the latest security threats and can provide the most accurate and effective vulnerability matching and prioritization. Vulnerability Matcher is a highly effective tool for managing security vulnerabilities in software systems. It uses advanced AI and machine learning techniques to identify, prioritize, and provide actionable recommendations for remediation of vulnerabilities. This tool is invaluable for developers and security professionals who want to ensure the security of their software systems.
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Kalyan Manohar, Immadisetti, Dadisetti Vishnu Datta, and Lekshmi S. Raveendran. "Website Vulnerability Scanning System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem43079.
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With the increasing reliance on web applications for business and personal use, ensuring website security has become a critical concern. Cyber threats such as SQL injection, cross-site scripting (XSS), malware infections, and unauthorized access pose significant risks to websites, leading to data breaches and service disruptions. This project aims to develop a comprehensive website security scanner that systematically identifies vulnerabilities and potential security risks.The proposed system integrates automated vulnerability scanning, penetration testing techniques, and real-time monitoring to detect security loopholes. Using machine learning and heuristic-based analysis, the scanner can identify malicious scripts, outdated software versions, weak authentication mechanisms, and misconfigured security policies. The system also performs network security assessments, analyzing potential DDoS (Distributed Denial-of-Service) attack risks and firewall configurations. The scanner generates detailed security reports, providing actionable insights and recommendations for website owners and administrators to mitigate risks effectively. Designed for continuous monitoring and proactive defense, the tool enhances cybersecurity resilience against evolving threats. This project contributes to web security advancements by offering an intelligent, automated, and scalable solution for safeguarding websites from cyberattacks. Keywords: Website Security | Vulnerability Scanner | Cyber Threats | SQL Injection | Cross-Site Scripting (XSS) | Penetration Testing | Machine Learning | Malware Detection | DDoS Protection | Authentication Security | Firewall Analysis | Web Application Security | Risk Assessment | Cybersecurity Resilience
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Azhari, Firman. "Quick detection of NFC vulnerability." Information Management & Computer Security 22, no. 2 (2014): 134–40. http://dx.doi.org/10.1108/imcs-09-2013-0067.
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Purpose – The purpose of this research is to explain particular implementation weaknesses of near field communication (NFC) systems done by several institutions which apply for critical purposes and provide practical solutions. Design/methodology/approach – This research is done by literature studies of previous findings in NFC security, observations of some existing implemented systems and experimentations to provide practical solutions. Findings – Unintentional lack of security protection of the NFC cards and tags by some card issuers make them a vulnerable target. The outcomes of this research are proposed solutions on methods to quickly detect vulnerability in NFC tags using an Android-based mobile application. Another solution involves the assembly of a detection device using the portable, low power and powerful Raspberry Pi to analyze the NFC tags or cards and NFC reader vulnerabilities. Research limitations/implications – This research is conducted in Indonesia; therefore, the results and solutions may lack generalizability. However, the findings may occur in other countries which newly apply NFC technology. Practical implications – System implementer should become more aware about the security issue of old NFC tags like MIFARE Classic. Price should be considered after tag security. People also need to be aware of identity or money theft using NFC-enabled smartphones, as many identity cards and electronic money are now relying on NFC technology. Social implications – People also need to be aware of identity or money theft using NFC-enabled smartphones, as many identity cards and electronic money are now relying on NFC technology. Originality/value – This research fulfills an identified need to evaluate the security aspect of a system that uses NFC as one of the main technologies. The results and solutions also provides cheap, easy and practical tools to analyze NFC security.
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Guo, Ran, Weijie Chen, Lejun Zhang, Guopeng Wang, and Huiling Chen. "Smart Contract Vulnerability Detection Model Based on Siamese Network (SCVSN): A Case Study of Reentrancy Vulnerability." Energies 15, no. 24 (2022): 9642. http://dx.doi.org/10.3390/en15249642.
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Blockchain technology is currently evolving rapidly, and smart contracts are the hallmark of the second generation of blockchains. Currently, smart contracts are gradually being used in power system networks to build a decentralized energy system. Security is very important to power systems and attacks launched against smart contract vulnerabilities occur frequently, seriously affecting the development of the smart contract ecosystem. Current smart contract vulnerability detection tools suffer from low correct rates and high false positive rates, which cannot meet current needs. Therefore, we propose a smart contract vulnerability detection system based on the Siamese network in this paper. We improved the original Siamese network model to perform smart contract vulnerability detection by comparing the similarity of two sub networks with the same structure and shared parameters. We also demonstrate, through extensive experiments, that the model has better vulnerability detection performance and lower false alarm rate compared with previous research results.
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Sun, Meng, Qi Wang, Jue He, et al. "Research on automatic scanning method of network vulnerabilities in power system." Journal of Physics: Conference Series 2290, no. 1 (2022): 012036. http://dx.doi.org/10.1088/1742-6596/2290/1/012036.
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Abstract Power system network is an important guarantee for the smooth operation of power enterprises. Considering the current automatic network vulnerability scanning method of power system, the detection rate of network vulnerability scanning is low due to its poor scheduling ability. Therefore, this paper designs a new automatic scanning method for network vulnerabilities in power system. According to the infrastructure of power system network vulnerability scanner, the power system web page interaction behavior recognition model is constructed to complete the power system web page interaction behavior recognition. On this basis, the power system network scanning scheduling algorithm is designed. Combined with genetic algorithm, the variation process of power system network vulnerabilities is determined, the power system network security situation is determined, the power system network scanning scheduling and vulnerability mining are realized, and the design of power system network leakage automatic scanning method is completed. The experimental link is constructed to verify this method. The verification shows that this method can effectively improve the detection rate of network vulnerability scanning and the efficiency of vulnerability scanning to a certain extent.
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Thannoun, Rayan Gh, and Omar Abdullah Ismaeel. "Flood Risk Vulnerability Detection based on the Developing Topographic Wetness Index Tool in Geographic Information System." IOP Conference Series: Earth and Environmental Science 1300, no. 1 (2024): 012012. http://dx.doi.org/10.1088/1755-1315/1300/1/012012.
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Abstract Finding vulnerability to flooding locations is a crucial part of sensible urban development and effective natural disaster management. Globally, there has been a noticeable rise in the frequency of floods in recent years, which affects human habitation and several economic sectors. This calls for the employment of various prevention measures, wherein the assessment of vulnerability to flooding is crucial. The main objective of the present study is to introduce the best procedure for the identification of flood risk vulnerability detection using geographical information systems techniques and decision-making, based on a comparative evaluation of various scenarios. In this context, The current study will develop a Topographic Wetness Index (TWI) tool for the detection of these risks which can deal with the stream orders, calculate the length of the valley, and then show the outputs by thematic maps. The procedure with the developed adaptive tool has been applied to identify Flood Risk Vulnerability in Erbil city and some surrounding areas. The results of this paper indicated the existence of different levels of the TWI, which were classified into five classes. The procedure of this study has an advantage over other traditional methods since it takes into account mainly statistics data that are linked to the TWI which can be easily customized in detecting risk Vulnerability.
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Song, Guang Jun, Chun Lan Zhao, and Ming Li. "Study on Software Vulnerability Dynamic Discovering System." Applied Mechanics and Materials 151 (January 2012): 673–77. http://dx.doi.org/10.4028/www.scientific.net/amm.151.673.
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Developed a new system model of software vulnerability discovering, which was based on fuzzing, feature matching of API sequences and data mining. Overcame the disadvantages of old techniques, this new method effectively improves the detection of potential unknown security vulnerabilities in software. Besides, this method is more automated and performs better in finding new security vulnerabilities.
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Wang, Xin, Runpu Wu, Jinxin Ma, Gang Long, and Jedeng Han. "Research on Vulnerability Detection Technology for WEB Mail System." Procedia Computer Science 131 (2018): 124–30. http://dx.doi.org/10.1016/j.procs.2018.04.194.
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V, Bharathi, and Vinoth Kumar C N S. "Vulnerability Detection in Cyber-Physical System Using Machine Learning." Scalable Computing: Practice and Experience 25, no. 1 (2024): 577–91. http://dx.doi.org/10.12694/scpe.v25i1.2405.
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The cyber-physical system is a specific type of IoT communication environment that deals with communication through innovative healthcare (medical) devices. The traditional medical system has been partially replaced by this application, improving healthcare through efficiency, accessibility, and personalization. The intelligent healthcare industry utilizes wireless medical sensors to gather patient health information and send it to a distant server for diagnosis or treatment. The healthcare industry must increase electronic device accuracy, reliability, and productivity. Artificial intelligence (AI) has been applied in various industries, but cybersecurity for cyber-physical systems (CPS) is still a recent topic. This work presents a method for intelligent threat recognition based on machine learning (ML) that enables run-time risk assessment for better situational awareness in CPS security monitoring. Several machine learning techniques, including Nave Bayes (65.4\%), Support Vector Machine (64.1%), Decision Tree (89.6%), Random Forest (92.5%), and Ensemble crossover (EC) XG boost classifier (99.64), were used to classify the malicious activities on real-world testbeds. The outcomes demonstrate that the Ensemble crossover XG boost enabled the best classification accuracy. When used in industrial reference applications, the model creates a safe environment where the patient is only made aware of risks when categorization optimism exceeds a specific limit, minimizing security managers' pressure and efficiently assisting their choices.
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Walkowski, Michał, Jacek Oko, and Sławomir Sujecki. "Vulnerability Management Models Using a Common Vulnerability Scoring System." Applied Sciences 11, no. 18 (2021): 8735. http://dx.doi.org/10.3390/app11188735.
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Vulnerability prioritization is an essential element of the vulnerability management process in data communication networks. Accurate prioritization allows the attention to be focused on the most critical vulnerabilities and their timely elimination; otherwise, organizations may face severe financial consequences or damage to their reputations. In addition, the large amounts of data generated by various components of security systems further impede the process of prioritizing the detected vulnerabilities. Therefore, the detection and elimination of critical vulnerabilities are challenging tasks. The solutions proposed for this problem in the scientific literature so far—e.g., PatchRank, SecureRank, Vulcon, CMS, VDNF, or VEST—are not sufficient because they do not consider the context of the organization. On the other hand, commercial solutions, such as Nessus, F-Secure, or Qualys, do not provide detailed information regarding the prioritization procedure, except for the scale. Therefore, in this paper, the authors present an open-source solution called the Vulnerability Management Center (VMC) in order to assist organizations with the vulnerability prioritization process. The VMC presents all calculated results in a standardized way by using a Common Vulnerability Scoring System (CVSS), which allows security analysts to fully understand environmental components’ influences on the criticality of detected vulnerabilities. In order to demonstrate the benefits of using the the open-source VMC software developed here, selected models of a vulnerability management process using CVSS are studied and compared by using three different, real testing environments. The open-source VMC suite developed here, which integrates information collected from an asset database, is shown to accelerate the process of removal for the critical vulnerabilities that are detected. The results show the practicability and efficacy of the selected models and the open-source VMC software, which can thus reduce organizations’ exposure to potential threats.
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Ojasvini, Nitesh, Piyush, Narina Thakur, and Arvind Rehalia. "Intrusion Detection System using Artificial Immune Systems: A Case Study." International Journal of Advanced Research in Computer Science and Software Engineering 8, no. 2 (2018): 19. http://dx.doi.org/10.23956/ijarcsse.v8i2.571.
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Networks are working at their apical efficiency and are increasing in size by every second; emergence of various threats becomes hindrance in the growth and privacy of the users. The network is vulnerable to security breaches, due to malicious nodes. Intrusion detection systems aim at removing this vulnerability. In this paper, intrusion detection mechanisms for large-scale dynamic networks are investigated. Artificial immune system is a concept that works to protect a network the way immune systems of vertebrates work in nature. This paper also illustrates this artificial immune system, the integration of bio-inspired algorithms, and its functionality with the computer networks.
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Ekene, Ozioko Frank, and Mba Chioma Juliet. "The Application of Deep Neural Network to Vulnerability Management on Cyber Physical System – A Systematic Review." International Journal of Research and Innovation in Applied Science X, no. IV (2025): 1276–85. https://doi.org/10.51584/ijrias.2025.10040102.
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Vulnerability management plays a pivotal role in securing Cyber-Physical Systems (CPS) from emerging risks by identifying, assessing, and mitigating potential threats. This paper provides a comprehensive review of existing vulnerability management techniques, highlighting their challenges and limitations when applied to CPS. Specifically, the work examined the role of machine learning, particularly Deep Neural Networks (DNN), in enhancing vulnerability detection and prediction models. DNNs have shown promising results in detecting complex, high-dimensional patterns within large datasets, making them ideal for securing CPS environments. Based on the findings, the paper proposes future research directions that focus on refining DNN-based models to tackle scalability, interpretability, and adaptive security challenges in CPS. By leveraging these advancements, we aim to facilitate more robust, proactive vulnerability management solutions, ultimately contributing to the overall resilience of Cyber-Physical Systems in the face of increasingly sophisticated cyber threats.
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Liu, Hongkun, Nianci Wang, and Sirong Liang. "Wireless communication network security intelligent monitoring system based on machine learning." Journal of Physics: Conference Series 2083, no. 3 (2021): 032045. http://dx.doi.org/10.1088/1742-6596/2083/3/032045.
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Abstract Aiming at the problems of traditional wireless communication network security vulnerability monitoring systems such as low monitoring accuracy and time-consuming, a machine learning-based intelligent monitoring system for wireless communication network security vulnerabilities is proposed. In the hardware design of the monitoring system, based on the overall architecture of the wireless communication network and the data characteristics of the wireless communication network, it is divided into a vulnerability data collection module, a vulnerability data scanning module, and a network security vulnerability intelligent monitoring module. In the vulnerability data collection module, the wireless data collector is used to collect vulnerability data in the vulnerability database, and according to the attributes of the vulnerability data, the XSS vulnerability detection plug-in is connected to the vulnerability scanner to scan for wireless communication network vulnerabilities; When the communication network vulnerability data signal is traced, the system session operation of monitoring the vulnerability data. The software part introduces the neural network algorithm in the machine learning intelligent algorithm to process the hidden data in the security vulnerability data. The experimental results show that the wireless communication network security vulnerability intelligent monitoring system based on machine learning can effectively improve the system monitoring accuracy and the efficiency of wireless communication network security vulnerability monitoring.
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Odeh, Najla, and Sherin Hijazi. "Detecting and Preventing Common Web Application Vulnerabilities: A Comprehensive Approach." International Journal of Information Technology and Computer Science 15, no. 3 (2023): 26–41. http://dx.doi.org/10.5815/ijitcs.2023.03.03.
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Web applications are becoming very important in our lives as many sensitive processes depend on them. Therefore, it is critical for safety and invulnerability against malicious attacks. Most studies focus on ways to detect these attacks individually. In this study, we develop a new vulnerability system to detect and prevent vulnerabilities in web applications. It has multiple functions to deal with some recurring vulnerabilities. The proposed system provided the detection and prevention of four types of vulnerabilities, including SQL injection, cross-site scripting attacks, remote code execution, and fingerprinting of backend technologies. We investigated the way worked for every type of vulnerability; then the process of detecting each type of vulnerability; finally, we provided prevention for each type of vulnerability. Which achieved three goals: reduce testing costs, increase efficiency, and safety. The proposed system has been validated through a practical application on a website, and experimental results demonstrate its effectiveness in detecting and preventing security threats. Our study contributes to the field of security by presenting an innovative approach to addressing security concerns, and our results highlight the importance of implementing advanced detection and prevention methods to protect against potential cyberattacks. The significance and research value of this survey lies in its potential to enhance the security of online systems and reduce the risk of data breaches.
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Li, Zhen, Deqing Zou, Jing Tang, Zhihao Zhang, Mingqian Sun, and Hai Jin. "A Comparative Study of Deep Learning-Based Vulnerability Detection System." IEEE Access 7 (2019): 103184–97. http://dx.doi.org/10.1109/access.2019.2930578.
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Tondarkar, Abhishek A. "AI Driven Vulnerability Analysis Systems." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem50022.
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Abstract— With the increasing frequency and sophistication of cyber threats, the need for intelligent and adaptive security solutions has become more critical than ever. Artificial Intelligence (AI) is playing a transformative role in cybersecurity by enhancing the detection of system vulnerabilities, anticipating potential threats, and enabling automated incident response. This research introduces VulneraX, an AI-driven vulnerability analysis system built using the Flask framework. The system is trained on data from the National Vulnerability Database (NVD) covering the years 2020 to 2023. VulneraX offers real-time vulnerability assessment, assigns severity scores, and provides clear remediation guidance. These insights are delivered through an intuitive user interface featuring advanced data visualizations. The paper also examines future development paths, ethical concerns, and the regulatory landscape surrounding the deployment of AI in cybersecurity.
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Liang, Bei Hai, Bin Bin Qu, Sheng Jiang, and Chu Tian Ye. "Research on Vulnerability Detection for Software Based on Taint Analysis." Applied Mechanics and Materials 347-350 (August 2013): 3715–20. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.3715.
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At present, Cross Site Scripting (XSS) vulnerability exists in most web sites. The main reason is the lack of effective validation and filtering mechanisms for user input data from web request. This paper explores vulnerability detection method which based on taint dependence analysis and implements a prototype system for Java Web program. We treat all user input as tainted data, and track the flow of Web applications, then we judge whether it will trigger an attack or not. The taint dependent analysis algorithm mentioned in this paper is used to construct the taint dependency graph. Next the value representation method of the string tainted object based on finite state automata is discussed. Finally, we propose the vulnerability detection method for the program. The experiment result shows that the prototype system can detect reflection cross-site scripting vulnerability well in those programs which dont have effective treatment for the user input data.
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Yevhenii, Kubiuk, and Kyselov Gennadiy. "Comparative analysis of approaches to source code vulnerability detection based on deep learning methods." Technology audit and production reserves 3, no. 2(59) (2021): 19–23. https://doi.org/10.15587/2706-5448.2021.233534.
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<em>The object of research of this work is the methods of deep learning for source code vulnerability detection. One of the most problematic areas is the use of only one approach in the code analysis process: the approach based on the AST (abstract syntax tree) or the approach based on the program dependence graph (PDG).</em> <em>In this paper, a comparative analysis of two approaches for source code vulnerability detection was conducted: approaches based on AST and approaches based on the PDG.</em> <em>In this paper, various topologies of neural networks were analyzed. They are used in approaches based on the AST and PDG. As the result of the comparison, the advantages and disadvantages of each approach were determined, and the results were summarized in the corresponding comparison tables. As a result of the analysis, it was determined that the use of BLSTM (Bidirectional Long Short Term Memory) and BGRU (Bidirectional Gated Linear Unit) gives the best result in terms of problems of source code vulnerability detection. As the analysis showed, the most effective approach for source code vulnerability detection systems is a method that uses an intermediate representation of the code, which allows getting a language-independent tool.</em> <em>Also, in this work, our own algorithm for the source code analysis system is proposed, which is able to perform the following operations: predict the source code vulnerability, classify the source code vulnerability, and generate a corresponding patch for the found vulnerability. A detailed analysis of the proposed system’s unresolved issues is provided, which is planned to investigate in future researches. The proposed system could help speed up the software development process as well as reduce the number of software code vulnerabilities. Software developers, as well as specialists in the field of cybersecurity, can be stakeholders of the proposed system.</em>
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Liu, Lili, Zhen Li, Yu Wen, and Penglong Chen. "Investigating the impact of vulnerability datasets on deep learning-based vulnerability detectors." PeerJ Computer Science 8 (May 11, 2022): e975. http://dx.doi.org/10.7717/peerj-cs.975.
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Software vulnerabilities have led to system attacks and data leakage incidents, and software vulnerabilities have gradually attracted attention. Vulnerability detection had become an important research direction. In recent years, Deep Learning (DL)-based methods had been applied to vulnerability detection. The DL-based method does not need to define features manually and achieves low false negatives and false positives. DL-based vulnerability detectors rely on vulnerability datasets. Recent studies found that DL-based vulnerability detectors have different effects on different vulnerability datasets. They also found that the authenticity, imbalance, and repetition rate of vulnerability datasets affect the effectiveness of DL-based vulnerability detectors. However, the existing research only did simple statistics, did not characterize vulnerability datasets, and did not systematically study the impact of vulnerability datasets on DL-based vulnerability detectors. In order to solve the above problems, we propose methods to characterize sample similarity and code features. We use sample granularity, sample similarity, and code features to characterize vulnerability datasets. Then, we analyze the correlation between the characteristics of vulnerability datasets and the results of DL-based vulnerability detectors. Finally, we systematically study the impact of vulnerability datasets on DL-based vulnerability detectors from sample granularity, sample similarity, and code features. We have the following insights for the impact of vulnerability datasets on DL-based vulnerability detectors: (1) Fine-grained samples are conducive to detecting vulnerabilities. (2) Vulnerability datasets with lower inter-class similarity, higher intra-class similarity, and simple structure help detect vulnerabilities in the original test set. (3) Vulnerability datasets with higher inter-class similarity, lower intra-class similarity, and complex structure can better detect vulnerabilities in other datasets.
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Wang, Chong, Guang Jun Song, and Chun Lan Zhao. "Study on Software Vulnerability Discovering Based on Linux Sequence of System Call." Applied Mechanics and Materials 151 (January 2012): 537–43. http://dx.doi.org/10.4028/www.scientific.net/amm.151.537.
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Considering the efficiency problem of software vulnerability discovering in Linux system, a new software vulnerability discovering in Linux system program with data mining algorithm is proposed in this paper. An improved REL algorithm based on one-dimensional linked list is proposed, and mining on Linux sequence data of system call with REL algorithm, then we do analysis and detection of software vulnerabilities. A model of software vulnerability discovering analysis system with LRE algorithm was designed. Finally, experimental results show the validity of mining on Linux sequence data of system call with REL algorithm in terms of reducing the false alarm rate, and improving the efficiency of software security vulnerability discovering.
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Preethi K, Senthamil, and Murugan A. "Analysis of Vulnerability Detection Tool for Web Services." International Journal of Engineering & Technology 7, no. 3.12 (2018): 773. http://dx.doi.org/10.14419/ijet.v7i3.12.16499.
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The demand of the web services requirement is increasing day by day, because of this the security of the web services was under risk. To prevent from distinct types of attacks the developer needs to select the vulnerability detection tools, since many tools are available in the market the major challenging task for the developer to find the best tool which suitable for his application requirements. The recent study shows that many vulnerability detection tools provide a low coverage as far as vulnerability detection and higher false positive rate. In this paper, proposed a benchmarking method to accessing and comparing the efficiency of vulnerability detection tools in the web service environment. This method was used to illustrate the two benchmarks for SQL injection and cross site scripting. The first one is depending on predefined set of web services and next one permits user to identify the workload (User defined web services). Proposed system used the open source and commercial tools to test the application with benchmarking standards. Result shows that the benchmarks perfectly depict the efficiency of vulnerability detection tools.
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Li, Xingzheng, Bingwen Feng, Guofeng Li, Tong Li, and Mingjin He. "A Vulnerability Detection System Based on Fusion of Assembly Code and Source Code." Security and Communication Networks 2021 (July 29, 2021): 1–11. http://dx.doi.org/10.1155/2021/9997641.
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Software vulnerabilities are one of the important reasons for network intrusion. It is vital to detect and fix vulnerabilities in a timely manner. Existing vulnerability detection methods usually rely on single code models, which may miss some vulnerabilities. This paper implements a vulnerability detection system by combining source code and assembly code models. First, code slices are extracted from the source code and assembly code. Second, these slices are aligned by the proposed code alignment algorithm. Third, aligned code slices are converted into vector and input into a hyper fusion-based deep learning model. Experiments are carried out to verify the system. The results show that the system presents a stable and convergent detection performance.
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Sushama, R. Borhade* Sandip A. Kahate. "DETECTION OF BACKDOOR ATTCKS WITH GENERATING ALERTS OVER MOBILE NETWORKS." International Journal OF Engineering Sciences & Management Research 3, no. 6 (2016): 37–42. https://doi.org/10.5281/zenodo.55202.
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In the today’s business environment, experts must do everything to prevent network breaches. Sometimes it is very difficult to identify the attacks coming from nearly all the sides that every vector and point of entry is protected. Attack is a action that exploits vulnerability in controlled system. Recently, there has been an increase in active and passive attacks. An applications that allow for remote access to computers which is known as backdoors that are often used for targeted attacks.And to avoid these types of attack here is a new technique used in Intrusion Detection System that is JAXB technology is used to create the hashmap of complex data.
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Seara, João Pedro, and Carlos Serrão. "Automation of System Security Vulnerabilities Detection Using Open-Source Software." Electronics 13, no. 5 (2024): 873. http://dx.doi.org/10.3390/electronics13050873.
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Cybersecurity failures have become increasingly detrimental to organizations worldwide, impacting their finances, operations, and reputation. This issue is worsened by the scarcity of cybersecurity professionals. Moreover, the specialization required for cybersecurity expertise is both costly and time-consuming. In light of these challenges, this study has concentrated on automating cybersecurity processes, particularly those pertaining to continuous vulnerability detection. A cybersecurity vulnerability scanner was developed, which is freely available to the community and does not necessitate any prior expertise from the operator. The effectiveness of this tool was evaluated by IT companies and systems engineers, some of whom had no background in cybersecurity. The findings indicate that the scanner proved to be efficient, precise, and easy to use. It assisted the operators in safeguarding their systems in an automated fashion, as part of their security audit strategy.
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Mrs.G.Anitha,, K.Bindu sree, B.Sai Lavanya, and B.Akshaya. "ML-Powered Insight Into Code Software Vulnerability." International Journal of Information Technology and Computer Engineering 13, no. 2 (2025): 1416–23. https://doi.org/10.62647/ijitce2025v13i2pp1416-1423.
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As software systems grow increasingly complex,ensuring their security becomes paramount.Vulnerabilities in software can lead to devastatingconsequences, including data breaches, systemcompromise, and financial losses. Traditionalmethods of detecting vulnerabilities rely heavily onmanual code inspection, which is time-consumingand error-prone. In recent years, machine learning(ML) algorithms have emerged as promising toolsfor automating the detection of softwarevulnerabilities.This research proposes a novel software vulnerabilitydetection tool that leverages machine learningalgorithms. The tool utilizes supervised learningtechniques to analyze code repositories and identifypotential vulnerabilities. By training on labeleddatasets of known vulnerabilities, the system learnsto recognize patterns indicative of security flaws. Email: kolturbindusree@gmail.com
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Zhu, You Chan, and Hui Li Liang. "The SQL Injection Vulnerability Detection of the Web Application." Applied Mechanics and Materials 198-199 (September 2012): 1457–61. http://dx.doi.org/10.4028/www.scientific.net/amm.198-199.1457.
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the SQL injection is one of the common security vulnerabilities of the Web application. This paper studies how to find out the possible SQL injection vulnerabilities. The scheme this paper put forward is the technology of black-box test. The main steps are that firstly construct specific user input in the test period of the Web application system, and inject it into the application system, then get the vulnerability detection report according to the analysis of the test logs.
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Kubiuk, Yevhenii, and Gennadiy Kyselov. "Comparative analysis of approaches to source code vulnerability detection based on deep learning methods." Technology audit and production reserves 3, no. 2(59) (2021): 19–23. http://dx.doi.org/10.15587/2706-5448.2021.233534.
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The object of research of this work is the methods of deep learning for source code vulnerability detection. One of the most problematic areas is the use of only one approach in the code analysis process: the approach based on the AST (abstract syntax tree) or the approach based on the program dependence graph (PDG). In this paper, a comparative analysis of two approaches for source code vulnerability detection was conducted: approaches based on AST and approaches based on the PDG. In this paper, various topologies of neural networks were analyzed. They are used in approaches based on the AST and PDG. As the result of the comparison, the advantages and disadvantages of each approach were determined, and the results were summarized in the corresponding comparison tables. As a result of the analysis, it was determined that the use of BLSTM (Bidirectional Long Short Term Memory) and BGRU (Bidirectional Gated Linear Unit) gives the best result in terms of problems of source code vulnerability detection. As the analysis showed, the most effective approach for source code vulnerability detection systems is a method that uses an intermediate representation of the code, which allows getting a language-independent tool. Also, in this work, our own algorithm for the source code analysis system is proposed, which is able to perform the following operations: predict the source code vulnerability, classify the source code vulnerability, and generate a corresponding patch for the found vulnerability. A detailed analysis of the proposed system’s unresolved issues is provided, which is planned to investigate in future researches. The proposed system could help speed up the software development process as well as reduce the number of software code vulnerabilities. Software developers, as well as specialists in the field of cybersecurity, can be stakeholders of the proposed system.
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Gürfidan, Remzi. "VULREM: Fine-Tuned BERT-Based Source-Code Potential Vulnerability Scanning System to Mitigate Attacks in Web Applications." Applied Sciences 14, no. 21 (2024): 9697. http://dx.doi.org/10.3390/app14219697.
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Software vulnerabilities in web applications are one of the sensitive points in data and application security. Although closing a vulnerability after it is detected in web applications seems to be a solution, detecting vulnerabilities in the source code before the vulnerability is detected effectively prevents malicious attacks. In this paper, we present an improved and automated Bidirectional Encoder Representations from Transformers (BERT)-based approach to detect vulnerabilities in web applications developed in C-Sharp. For the training and testing of the proposed VULREM (Vulnerability Remzi) model, a dataset of eight different CVE (Common Vulnerabilities and Exposures)-numbered critical vulnerabilities was created from the source code of six different applications specific to the study. In the VULREM model, fine-tuning was performed within the BERT model to obtain maximum accuracy from the dataset. To obtain the optimum performance according to the number of source-code lines, six different input lengths were tested with different batch sizes. Classification metrics were used for the testing and performance evaluation of the model, and an average F1-score of 99% was obtained for the best sequence length according to eight different vulnerability classifications. In line with the findings obtained, this will play an important role in both vulnerability detection in web applications of the C-Sharp language and in detecting and correcting critical vulnerabilities in the developmental processes of web applications, with an accuracy of 99%.
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Tian, Junfeng, Wenjing Xing, and Zhen Li. "BVDetector: A program slice-based binary code vulnerability intelligent detection system." Information and Software Technology 123 (July 2020): 106289. http://dx.doi.org/10.1016/j.infsof.2020.106289.
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Kumawat, Anjali, Anil Kumar Sharma, and Sunita Kumawat. "Identification of Cryptographic Vulnerability and Malware Detection in Android." International Journal of Information Security and Privacy 11, no. 3 (2017): 15–28. http://dx.doi.org/10.4018/ijisp.2017070102.
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Android based Smartphones are nowadays getting more popular. While using Smartphone, user is always concerned about security and malicious attacks, cryptographic vulnerability of the applications. With increase in the number of Android mobiles, Android malwares are also increasing very rapidly. So the authors have proposed the “Identification of cryptographic vulnerability and malware detection in Android” system. They have designed a user friendly android application, through which user and developer can easily test the application whether it is benign or vulnerable. The application will be tested firstly using static analysis and then the dynamic analysis will be carried out. The authors have implemented static and dynamic analysis of android application for vulnerable and malicious app detection. They have also created a web page. User can either use the application or the web page.
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Yang, Shuo, Jian Guo, and Xue Rui. "Formal Analysis and Detection for ROS2 Communication Security Vulnerability." Electronics 13, no. 9 (2024): 1762. http://dx.doi.org/10.3390/electronics13091762.
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Robotic systems have been widely used in various industries, so the security of communication between robots and their components has become an issue that needs to be focused on. As a framework for developing robotic systems, the security of ROS2 (Robot Operating System 2) can directly affect the security of the upper-level robotic systems. Therefore, it is a worthwhile research topic to detect and analyze the security of ROS2. In this study, we adopted a formal approach to analyze the security of the communication mechanism of ROS2. First, we used a state transition system to model the potential vulnerabilities of ROS2 based on the ROS2 communication mechanism and the basic process of penetration testing. Secondly, we introduced a CIA model based on the established vulnerability model and used linear temporal logic to define its security properties. Then, we designed and implemented a vulnerability detection tool for ROS2 applications based on the vulnerability model and security properties. Finally, we experimentally tested some ROS2-based applications, and the results show that ROS2 has vulnerabilities without additional protection safeguards.
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Moreira, Diogo, João Pedro Seara, João Pedro Pavia, and Carlos Serrão. "Intelligent Platform for Automating Vulnerability Detection in Web Applications." Electronics 14, no. 1 (2024): 79. https://doi.org/10.3390/electronics14010079.
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In a world increasingly dependent on technology and in an era where connectivity is omnipresent, Web applications have become an essential part of our everyday life. The evolution of these applications, combined with the exponential increase in the number of users, has brought with it not only convenience but also significant challenges in terms of security. Ensuring the security of Web applications and their data is increasingly a priority for companies, although many companies lack the know-how, time, and money to do so. This research project studied and developed a system with the aim of automating the process of detecting vulnerabilities in Web applications by exploiting the benefits of the interoperability of the two forms of automation of the tool selected to carry out this analysis. The developed solution is low-cost and requires very little user intervention. In order to validate and evaluate the developed platform, experiments were carried out on applications with different types of vulnerabilities known in advance and on real applications. It is essential to guarantee the security of Web applications, and the developed system proved capable of automating the detection of vulnerability risks and returning the results in a relatively simple way for the user.
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Bennouk, Khalid, Nawal Ait Aali, Younès El Bouzekri El Idrissi, Bechir Sebai, Abou Zakaria Faroukhi, and Dorra Mahouachi. "A Comprehensive Review and Assessment of Cybersecurity Vulnerability Detection Methodologies." Journal of Cybersecurity and Privacy 4, no. 4 (2024): 853–908. http://dx.doi.org/10.3390/jcp4040040.
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The number of new vulnerabilities continues to rise significantly each year. Simultaneously, vulnerability databases have challenges in promptly sharing new security events with enough information to improve protections against emerging cyberattack vectors and possible exploits. In this context, several organizations adopt strategies to protect their data, technologies, and infrastructures from cyberattacks by implementing anticipatory and proactive approaches to their system security activities. To this end, vulnerability management systems play a crucial role in mitigating the impact of cyberattacks by identifying potential vulnerabilities within an organization and alerting cyber teams. However, the effectiveness of these systems, which employ multiple methods and techniques to identify weaknesses, relies heavily on the accuracy of published security events. For this reason, we introduce a discussion concerning existing vulnerability detection methods through an in-depth literature study of several research papers. Based on the results, this paper points out some issues related to vulnerability databases handling that impact the effectiveness of certain vulnerability identification methods. Furthermore, after summarizing the existing methodologies, this study classifies them into four approaches and discusses the challenges, findings, and potential research directions.
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Jyoti, Snehi, Bhandari Abhinav, Baggan Vidhu, and Snehi Ritu Manish. "Diverse Methods for Signature based Intrusion Detection Schemes Adopted." International Journal of Recent Technology and Engineering (IJRTE) 9, no. 2 (2020): 44–49. https://doi.org/10.35940/ijrte.A2791.079220.
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Intrusion Detection Systems (IDS) is used as a tool to detect intrusions on IT networks, providing support in network monitoring to identify and avoid possible attacks. Most such approaches adopt Signature-based methods for detecting attacks which include matching the input event to predefined database signatures. Signature based intrusion detection acts as an adaptable device security safeguard technology. This paper discusses various Signature-based Intrusion Detection Systems and their advantages; given a set of signatures and basic patterns that estimate the relative importance of each intrusion detection system feature, system administrators may help identify cyber-attacks and threats to the network and Computer system. Eighty percent of incidents can be easily and promptly detected using signature-based detection methods if used as a precautionary phase for vulnerability detection and twenty percent rest by anomaly-based intrusion detection system that involves comparing definitions of normal activity or event behavior with observed events in identifying the significant deviations and deciding the traffic to flag.
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B. Kalaiselvi, B. Kalaiselvi, Mannepalle Sai Chandu, Maridhu Narendra, and Mannepalle Deekshith Kumar. "SQL-Injection Vulnerability Scanning Tool for Automatic Creation of SQL-Injection Attacks." International Journal of Advances in Engineering and Management 7, no. 1 (2025): 577–87. https://doi.org/10.35629/5252-0701577587.
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This research introduces an advanced automated scanning tool for detecting and analyzing SQL injection vulnerabilities in web applications, addressing the critical need for robust security testing mechanisms in modern web development. The proposed tool employs sophisticated dynamic analysis techniques combined with machine learning algorithms to automatically generate, execute, and validate SQL injection attack vectors. By implementing a multilayered detection approach, the system first identifies potential injection points through comprehensive input parameter analysis, followed by intelligent payload generation based on database fingerprinting and contextual analysis. The tool incorporates both syntactic and semantic analysis of database responses to effectively distinguish between successful and failed injection attempts, significantly reducing false positives. Advanced features include automated bypass techniques for common defensive mechanisms, support for multiple database management systems (MySQL, PostgreSQL, Oracle, and MS-SQL), and intelligent error pattern recognition. Experimental evaluation conducted across 100 diverse web applications demonstrated a 95% detection rate for known vulnerabilities and an 85% success rate in identifying previously undiscovered SQL injection vulnerabilities. The tool's automated approach significantly reduces the time and expertise required for security testing, making it valuable for both security professionals and development teams implementing secure coding practices. Additionally, the system generates detailed vulnerability reports with remediation recommendations, facilitating efficient security patch implementation. Performance analysis shows that the tool can scan complex web applications with minimal impact on system resources while maintaining high accuracy in vulnerability detection.
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Hu, Jinchang, Jinfu Chen, Sher Ali, et al. "A Detection Approach for Vulnerability Exploiter Based on the Features of the Exploiter." Security and Communication Networks 2021 (May 21, 2021): 1–14. http://dx.doi.org/10.1155/2021/5581274.
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With the wide application of software system, software vulnerability has become a major risk in computer security. The on-time detection and proper repair for possible software vulnerabilities are of great importance in maintaining system security and decreasing system crashes. The Control Flow Integrity (CFI) can be used to detect the exploit by some researchers. In this paper, we propose an improved Control Flow Graph with Jump (JCFG) based on CFI and develop a novel Vulnerability Exploit Detection Method based on JCFG (JCFG-VEDM). The detection method of the exploit program is realized based on the analysis results of the exploit program. Then the JCFG is addressed through combining the features of the exploit program and the jump instruction. Finally, we implement JCFG-VEDM and conduct the experiments to verify the effectiveness of the proposed method. The experimental results show that the proposed detection method (JCFG-VEDM) is feasible and effective.
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Umar, Umar, Kamaluddeen Usman .., Mohd Fadzil Hassan, Aminu Aminu Muazu, and M. S. Liew. "An IoT Device-Level Vulnerability Control Model Through Federated Detection." Journal of Intelligent Systems and Internet of Things 12, no. 2 (2024): 89–98. http://dx.doi.org/10.54216/jisiot.120207.
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In the rapidly expanding Internet of Things (IoT) landscape, the security of IoT devices is a major concern. The challenge lies in the lack of intrusion detection systems (IDS) models for these devices. This is due to resource limitations, resulting in, single point of failure, delayed threat detection and privacy issues when centralizing IDS processing. To address this, a LiteDLVC model is proposed in this paper, employing a multi-layer perceptron (MLP) in a federated learning (FL) approach to minimize the vulnerabilities in IoT system. This model manages smaller datasets from individual devices, reducing processing time and optimizing computing resources. Importantly, in the event of an attack, the LiteDLVC model targets only the compromised device, protecting the FL aggregator and other IoT devices. The model's evaluation using the BoT-IoT dataset on TensorFlow Federated (TFF) demonstrates higher accuracy and better performance with full features subset of 99.99% accuracy on test set and achieved average of 1.11sec in detecting bot attacks through federated detection. While on 10-best subset achieved 99.99 on test with 1.14sec as average detection time. Notably, this highlights that LiteDLVC model can potential secure IoT device from device level very efficiently. To improve the global model convergence, we are currently exploring the use genetic algorithm. This could lead to better performance on diverse IoT data distributions, and increased overall efficiency in FL scenes with non-IID data.
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Zhao, Jiazhen, Yuliang Lu, Kailong Zhu, Zehan Chen, and Hui Huang. "Cefuzz: An Directed Fuzzing Framework for PHP RCE Vulnerability." Electronics 11, no. 5 (2022): 758. http://dx.doi.org/10.3390/electronics11050758.
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Current static detection technology for web application vulnerabilities relies highly on specific vulnerability patterns, while dynamic analysis technology has the problem of low vulnerability coverage. In order to improve the ability to detect unknown web application vulnerabilities, this paper proposes a PHP Remote Command/Code Execution (RCE) vulnerability directed fuzzing method. Our method is a combination of static and dynamic methods. First, we obtained the potential RCE vulnerability information of the web application through fine-grained static taint analysis. Then we performed instrumentation for the source code of the web application based on the potential RCE vulnerability information to provide feedback information for fuzzing. Finally, a loop feedback web application vulnerability automatic verification mechanism was established in which the vulnerability verification component provides feedback information, and the seed mutation component improves the vulnerability test seed based on the feedback information. On the basis of this method, the prototype system Cefuzz (Command/Code Execution Fuzzer) is implemented. Thorough experiments show that, compared with the existing web application vulnerability detection methods, Cefuzz significantly improves the verification effect of RCE vulnerabilities, discovering 13 unknown vulnerabilities in 10 popular web CMSes.
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Popereshnyak, S. V., R. O. Skoryk, D. V. Kuptsov, and R. V. Kravchenko. "Human face recognition system in video stream." PROBLEMS IN PROGRAMMING, no. 2-3 (September 2024): 296–304. https://doi.org/10.15407/pp2024.02-03.296.
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In the work, an analysis of detection methods and faces in the video stream and their effectiveness in real time was carried out. Modern algorithms and pre-trained models have been found to be able to recognize faces with high accuracy, but their significant drawback is, in particular, vulnerability to attacks using fake faces. Therefore, the work also analyzed approaches to detecting living faces and the possibility of their implementation in the system. Using an object-oriented approach, a tool for face capture, receiving a video stream from various sources, detecting unknown and previously captured faces in a video stream, and recognizing live faces was designed and developed. The system has been adapted to work in real time using the GPU. The work improved the architecture of a convolutional neural network for recognizing living faces with the creation of a dataset from a combination of own footage and open datasets. Also, a user interface for the face recognition system was developed. The work improved identification procedures and simplified detection of persons on video for employees of the security department of enterprises by implementing liveness detection face recognition methods. As a result of the research, a system was designed, which is intended for detection, recognition and detection of living faces in a video stream. After analyzing the known successful software products, niches that need a new solution were identified. Based on them, functional and non-functional requirements were developed. The process of recognizing faces in the video stream has been modified by implementing our own Liveness Detection model.
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Zheng, Xiaokun. "Computer Deep Learning Network Security Vulnerability Detection Based on Virtual Reality Technology." Advances in Multimedia 2022 (May 5, 2022): 1–9. http://dx.doi.org/10.1155/2022/6039690.
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In order to detect the computer network security technology vulnerabilities due to various factors, the normal operation of the computer network must be ensured, the user’s confidential information must be protected, and it is proposed that the analysis and research on security vulnerability detection must be strengthened. This study introduces the working principle of the network security monitoring system, analyzes the key technologies involved in the system development process and network programming technology, gives the overall architecture of the system, and designs the processing flow of the monitoring function. The test and analysis of the system show that the design of the system has achieved the expected design goal. The design of the system has achieved the expected design goal. The five test points can meet the standard time specified in the demand analysis process. The time difference of all module test points in the test is less than 3 s. The system can realize the remote acquisition and real-time monitoring of the network access, file system operation, system operation status, and other information of the controlled computer. Through the test and analysis of the system, it is shown that the system has achieved the expected design goal, the working state problem, and can meet the functional requirements of internal network security monitoring. It can be applied to enterprises, institutions, and departments that have higher requirements for intranet information security.
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Gupta, Manoj R. "Eternal Blue Vulnerability." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (2023): 1054–60. http://dx.doi.org/10.22214/ijraset.2023.53795.
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Abstract: Many organizations have experienced the damage caused by cyberattacks exploiting Windows vulnerabilities. For operational reasons, the parameters of Windows are still used, especially in the enterprise management system (ICS). In this case, attackers can torture them to spread the disease. Specifically, the vulnerability in MS17-010 was used in attacks to spread malware such as WannaCry ransomware and other malware. Many systems for example, electronic newspapers, payment centres and car manufacturers are used around the world and there is a security vulnerability in Windows that causes serious problems. Since tools like Eternal Blue or Eternal Romance are published on the internet, attackers can easily exploit these vulnerabilities. This tool attacks legitimate processes running on Windows systems. It can be difficult for employees to see the signs of a struggle. Attacks can be mitigated using security updates; however, security updates are sometimes difficult to implement due to their long lifetime and stringent requirements. There are many ways to identify attacks that cause vulnerabilities, such as intrusion detection systems (IDS), but they are sometimes difficult to use because they require prior service. In this research, we propose a method to identify the attack that exploited the vulnerability in MS17-010 by analysing Windows built-in event Logs. This method can detect attacks against almost all supported versions of Windows. It can also be easily integrated into the production environment as it only uses the standard Windows operating system.
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Zhang, Guoqing, Wengen Gao, Yunfei Li, Xinxin Guo, Pengfei Hu, and Jiaming Zhu. "Detection of False Data Injection Attacks in a Smart Grid Based on WLS and an Adaptive Interpolation Extended Kalman Filter." Energies 16, no. 20 (2023): 7203. http://dx.doi.org/10.3390/en16207203.
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An accurate power state is the basis of the normal functioning of the smart grid. However, false data injection attacks (FDIAs) take advantage of the vulnerability in the bad data detection mechanism of the power system to manipulate the process of state estimation. By attacking the measurements, then affecting the estimated state, FDIAs have become a serious hidden danger that affects the security and stable operation of the power system. To address the bad data detection vulnerability, in this paper, a false data attack detection method based on weighted least squares (WLS) and an adaptive interpolation extended Kalman filter (AIEKF) is proposed. On the basis of applying WLS and AIEKF, the Euclidean distance is used to calculate the deviation values of the two-state estimations to determine whether the current moment is subjected to a false data injection attack in the power system. Extensive experiments were conducted to simulate an IEEE-14-bus power system, showing that the adaptive interpolation extended Kalman filter can compensate for the deficiency in the bad data detection mechanism and successfully detect FDIAs.
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Al salmawi, Haneen mohammed adhab. "Critical Evaluation of SQL Injection Security Measures in Web Applications." Wasit Journal for Pure sciences 4, no. 1 (2025): 104–19. https://doi.org/10.31185/wjps.566.
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Given that SQL injection attacks continue to pose a substantial threat to the security of web applications, this paper critically assesses sophisticated security measures intended to mitigate these vulnerabilities. We investigate the Agent-based Vulnerability Response System (AVRS), which improves traditional intrusion detection systems by incorporating mobile agents that provide increased autonomy and mobility. This system integrates a comprehensive vulnerability database and machine learning techniques to enable real-time threat detection and response. The VIWeb vulnerability scanner is introduced in the study, which evaluates three machine learning models—Decision Trees, Support Vector Machines (SVMs), and Artificial Neural Networks (ANNs)—for malware detection. The scanner employs algorithms such as the Reverse Resemblance Algorithm and Malicious String-Matching Algorithm. According to performance metrics, ANN surpasses SVM and Decision Tree approaches in its ability to classify threats, achieving the highest accuracy (86.50%) accurately. The results emphasize the potential of integrating machine learning with conventional security measures to fortify defenses against SQL injection assaults, thereby establishing the groundwork for future research and implementation strategies.
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Faiz Mulla, Yahaiya. "Zapper: Vulnerability and Port Scanner Tool." International Scientific Journal of Engineering and Management 04, no. 06 (2025): 1–9. https://doi.org/10.55041/isjem04061.
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Abstract Zapper is a multi-functional port and vulnerability scanner tool designed to identify security weaknesses and potential vulnerabilities in networks and web applications. This paper outlines the architecture, methodology, and implementation of Zapper, emphasizing its significance in penetration testing and cybersecurity. By integrating features like firewall detection, port scanning, and vulnerability analysis, Zapper provides a robust solution for enhancing system security. Developed by Crypto26, Zapper addresses limitations of traditional tools through efficiency, adaptability, and ease of use.
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Bhilare, Shruti, Vivek Kanhangad, and Narendra Chaudhari. "A study on vulnerability and presentation attack detection in palmprint verification system." Pattern Analysis and Applications 21, no. 3 (2017): 769–82. http://dx.doi.org/10.1007/s10044-017-0606-y.
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