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Journal articles on the topic 'Malicious attacks'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Wang, Jing Lei. "Research on the Detection Method of the Malicious Attacks on Campus Network." Applied Mechanics and Materials 644-650 (September 2014): 3291–94. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.3291.

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The problem of malicious attacks detection on campus network is studied to improve the accuracy of detection. When detecting malicious attacks on campus network, a conventional manner is usually conducted in malicious attack detection of campus network. If a malicious signature is mutated into a new feature, the conventional detection method cannot recognize the new malicious signature, resulting in a relative low detection accuracy rate of malicious attacks. To avoid these problems, in this paper, the malicious attacks detection method for campus network based on support vector machine algori

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Glancy, Fletcher, David P. Biros, Nan Liang, and Andy Luse. "Classification of malicious insiders and the association of the forms of attacks." Journal of Criminal Psychology 10, no. 3 (2020): 233–47. http://dx.doi.org/10.1108/jcp-03-2020-0012.

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Purpose The authors argue that the current studies about malicious insiders confuse the fact that malicious attacks belong to two different categories, namely, those that launch instrumental attacks and expressive attacks. The authors collect malicious insider data from publicly available sources and use text-mining techniques to analyze the association between malicious insiders’ characteristics and the different types of attack. Design/methodology/approach The authors investigated the relationship between personality characteristics and different types of malicious attacks. For the personali

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Jiang, Yufan, Maryam Zarezadeh, Tianxiang Dai, and Stefan Köpsell. "AlphaFL: Secure Aggregation with Malicious2 Security for Federated Learning against Dishonest Majority." Proceedings on Privacy Enhancing Technologies 2025, no. 4 (2025): 348–68. https://doi.org/10.56553/popets-2025-0134.

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Federated learning (FL) proposes to train a global machine learning model across distributed datasets. However, the aggregation protocol as the core component in FL is vulnerable to well-studied attacks, such as inference attacks, poisoning attacks [71] and malicious participants who try to deviate from the protocol [24]. Therefore, it is crucial to achieve both malicious security and poisoning resilience from cryptographic and FL perspectives, respectively. Prior works either achieve incomplete malicious security [76], address issues by using expensive cryptographic tools [22, 59] or assume t

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Kumari, Ankita, Sandip Dutta, and Soubhik Chakraborty. "A comparative study of different security issues in MANET." International Journal of Experimental Research and Review 31, Spl Volume (2023): 168–86. http://dx.doi.org/10.52756/10.52756/ijerr.2023.v31spl.016.

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In a MANET (Mobile Ad-Hoc Network), an intruder can attempt to gain unlawful access to the network to obtain sensitive information. These attacks can occur at various network layers, and different attacks can be carried out. To mitigate the risks of such attacks, several solutions have been proposed. It can be characterized by dynamic topology, meaning that the network is formed by a group of nodes communicating wirelessly and without centralized control. This feature makes MANETs highly vulnerable to attacks, especially when malicious nodes are introduced into the network. These malicious nod

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S., Tamil Selvi, and Visalakshi P. "Connect attack in IoT-WSN detect through cyclic analysis based on forward and backward elimination." PeerJ Computer Science 10 (June 28, 2024): e2130. http://dx.doi.org/10.7717/peerj-cs.2130.

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IoT-wireless sensor networks (WSN) have extensive applications in diverse fields such as battlegrounds, commercial sectors, habitat monitoring, buildings, smart homes, and traffic surveillance. WSNs are susceptible to various types of attacks, such as malicious attacks, false data injection attacks, traffic attacks, and HTTP flood attacks. CONNECT attack is a novel attack in WSN. CONNECT attack plays a crucial role through disrupting packet transmission and node connections and significantly impacts CPU performance. Detecting and preventing CONNECT attacks is imperative for enhancing WSN effic

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Lee, Kyungroul, Jaehyuk Lee, and Kangbin Yim. "Classification and Analysis of Malicious Code Detection Techniques Based on the APT Attack." Applied Sciences 13, no. 5 (2023): 2894. http://dx.doi.org/10.3390/app13052894.

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According to the Fire-eye’s M-Trends Annual Threat Report 2022, there are many advanced persistent threat (APT) attacks that are currently in use, and such continuous and specialized APT attacks cause serious damages attacks. As APT attacks continue to be active, there is a need for countermeasures to detect new and existing malicious codes. An APT attack is a type of intelligent attack that analyzes the target and exploits its vulnerabilities. It attempts to achieve a specific purpose, and is persistent in continuously attacking and threatening the system. With this background, this paper ana

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Pohane, Miss Mayuri Arvind. "Implementation Paper on Detection of Malicious URLs Using Machine Learning Techniques." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (2022): 296–98. http://dx.doi.org/10.22214/ijraset.2022.41084.

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Abstract: Detecting and preventing the user from the malicious site attacks are significant tasks. A huge number of attacks have been observed in last few years. Malicious attack detection and prevention system plays an immense role against these attacks by protecting the system’s critical information. The internet security software and fire walls are not enough to provide full protection to the system. Hence efficient detection systems are essential for web security. These existing methods have some drawbacks results into numbers of victims to increase. Hence we developed a system which helps

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Isah, Abdulkadir Onivehu, John Kolo Alhassan, Idris Ismaila, and Olawale Surajudeen Adebayo. "Tracking of Malicious Attacks on Data Online: A Systematic Review." Transactions on Networks and Communications 8, no. 4 (2020): 31–44. http://dx.doi.org/10.14738/tnc.84.9463.

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Tracking of computer network system attacks is a proactive measure to protect against attacks on data, that are basically encrypted for confidential security reasons, while in transit on the computer information channel. Cyber security threat continues to increase in direct proportion to the rate at which internet based services are deployed. In this systematic review, 53 research papers from reputable publishers were downloaded out of which 41 papers that are closely related to tracking of malicious attackers on encrypted data online were review under the consideration of attacks on encrypted

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Boyanov, Petar. "A TAXONOMY OF THE CYBER ATTACKS." Journal scientific and applied research 3, no. 1 (2013): 114–24. http://dx.doi.org/10.46687/jsar.v3i1.73.

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In this paper is made a sophisticated taxonomy of the malicious cyber attacks. The cyber attacks are summarized into several mainly types with additional subtypes for everyone attack. Thanks to the achieved comparative results in this paper many users can find and analyze different flaws and vulnerabilities in their computer and network systems and thereby they could detect and prevent future malicious cyber attacks.

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Aridoss, Manimaran. "Defensive Mechanism Against DDoS Attack to Preserve Resource Availability for IoT Applications." International Journal of Handheld Computing Research 8, no. 4 (2017): 40–51. http://dx.doi.org/10.4018/ijhcr.2017100104.

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The major challenge of Internet of Things (IoT) generated data is its hypervisor level vulnerabilities. Malicious VM deployment and termination are so simple due to its multitenant shared nature and distributed elastic cloud features. These features enable the attackers to launch Distributed Denial of Service attacks to degrade cloud server performance. Attack detection techniques are applied to the VMs that are used by malicious tenants to hold the cloud resources by launching DDoS attacks at data center subnets. Traditional dataflow-based attack detection methods rely on the similarities of

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Xie, Gang, Xincheng Zhou, and Jinchun Gao. "Adaptive Trust Threshold Model Based on Reinforcement Learning in Cooperative Spectrum Sensing." Sensors 23, no. 10 (2023): 4751. http://dx.doi.org/10.3390/s23104751.

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In cognitive radio systems, cooperative spectrum sensing (CSS) can effectively improve the sensing performance of the system. At the same time, it also provides opportunities for malicious users (MUs) to launch spectrum-sensing data falsification (SSDF) attacks. This paper proposes an adaptive trust threshold model based on a reinforcement learning (ATTR) algorithm for ordinary SSDF attacks and intelligent SSDF attacks. By learning the attack strategies of different malicious users, different trust thresholds are set for honest and malicious users collaborating within a network. The simulation

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PANG, Ming, and Zhi-Hua ZHOU. "Unorganized malicious attacks detection." SCIENTIA SINICA Informationis 48, no. 2 (2018): 177–86. http://dx.doi.org/10.1360/n112017-00112.

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McClure, Ben. "Thwarting malicious Java attacks." Computers & Security 17, no. 4 (1998): 326. http://dx.doi.org/10.1016/s0167-4048(98)80018-7.

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Arti Deshpande. "Leveraging Signature Patterns and Machine Learning for Detecting HTTP Header Manipulation Attacks." Journal of Information Systems Engineering and Management 10, no. 9s (2025): 636–50. https://doi.org/10.52783/jisem.v10i9s.1290.

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Hypertext Transfer Protocol (HTTP) injection is a security vulnerability in which attackers manipulate HTTP Headers for malicious intent which facilitate various types of attacks like Downgrade-attack, Session fixation, Session hijacking, Cross-site scripting (XSS), Script injection, Referer forgery, Host header injection and Cache poisoning. These HTTP header manipulations can also be used for phishing and malware attacks. This study proposes leveraging signature attack patterns enhanced with Machine Learning (ML) and Deep Learning (DL) for detection of malicious header. HTTP request headers

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Liu, Xin, Xiaomeng Liu, Ruiling Zhang, Dan Luo, Gang Xu, and Xiubo Chen. "Securely Computing the Manhattan Distance under the Malicious Model and Its Applications." Applied Sciences 12, no. 22 (2022): 11705. http://dx.doi.org/10.3390/app122211705.

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Manhattan distance is mainly used to calculate the total absolute wheelbase of two points in the standard coordinate system. The secure computation of Manhattan distance is a new geometric problem of secure multi-party computation. At present, the existing research secure computing protocols for Manhattan distance cannot resist the attack of malicious participants. In the real scene, the existence of malicious participants makes it necessary to study a solution that can resist malicious attacks. This paper first analyzes malicious attacks of the semi-honest model protocol of computing Manhatta

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Psychogyios, Konstantinos, Terpsichori-Helen Velivassaki, Stavroula Bourou, Artemis Voulkidis, Dimitrios Skias, and Theodore Zahariadis. "GAN-Driven Data Poisoning Attacks and Their Mitigation in Federated Learning Systems." Electronics 12, no. 8 (2023): 1805. http://dx.doi.org/10.3390/electronics12081805.

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Federated learning (FL) is an emerging machine learning technique where machine learning models are trained in a decentralized manner. The main advantage of this approach is the data privacy it provides because the data are not processed in a centralized device. Moreover, the local client models are aggregated on a server, resulting in a global model that has accumulated knowledge from all the different clients. This approach, however, is vulnerable to attacks because clients can be malicious or malicious actors may interfere within the network. In the first case, these types of attacks may re

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Aksoy, Ahmet, Luis Valle, and Gorkem Kar. "Automated Network Incident Identification through Genetic Algorithm-Driven Feature Selection." Electronics 13, no. 2 (2024): 293. http://dx.doi.org/10.3390/electronics13020293.

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The cybersecurity landscape presents daunting challenges, particularly in the face of Denial of Service (DoS) attacks such as DoS Http Unbearable Load King (HULK) attacks and DoS GoldenEye attacks. These malicious tactics are designed to disrupt critical services by overwhelming web servers with malicious requests. In contrast to DoS attacks, there exists nefarious Operating System (OS) scanning, which exploits vulnerabilities in target systems. To provide further context, it is essential to clarify that NMAP, a widely utilized tool for identifying host OSes and vulnerabilities, is not inheren

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Chiu, Hon Sun, King-Shan Lui, and Kwan L. Yeung. "DelPHIX: A Simple and Efficient Mechanism for Wormhole Detection in Ad Hoc Networks." Journal of Communications Software and Systems 2, no. 2 (2017): 131. http://dx.doi.org/10.24138/jcomss.v2i2.296.

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Data transmission in a mobile ad hoc network is performed within an untrusted wireless environment. It is subjected to many kinds of security attacks. In a wormhole attack, two malicious nodes work together to tunnel packets from one to the other, making other nodes perceive a path to have a smaller hop count. We identify two types of wormhole attacks. In the first type, malicious nodes do not expose themselves in route finding process and legitimate nodes do not know their existence. In the second type, malicious nodes do create route advertisements and legitimate nodes are aware of the exist

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Zhang, Hongfeng, Xinyu Wang, Lan Ban, and Molin Sun. "A Novel Detection and Identification Mechanism for Malicious Injection Attacks in Power Systems." Symmetry 15, no. 12 (2023): 2104. http://dx.doi.org/10.3390/sym15122104.

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The integration of advanced sensor technology and control technology has gradually improved the operational efficiency of traditional power systems. Due to the undetectability of these attacks using traditional chi-square detection techniques, the state estimation of power systems is vulnerable to cyber–physical attacks, For this reason, this paper presents a novel detection and identification framework for detecting malicious attacks in power systems from the perspective of cyber–physical symmetry. To consider the undetectability of cyber–physical attacks, a physical dynamics detection model

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Wang, Shuhan. "Survey of Malicious PDF Attacks." Frontiers in Computing and Intelligent Systems 5, no. 2 (2023): 104–7. http://dx.doi.org/10.54097/fcis.v5i2.13109.

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In recent years, malicious documents have gained widespread attention as one of the primary vectors for Advanced Persistent Threat (APT) attacks. These malicious document attacks employ various sophisticated techniques, including stream object attacks, embedded JavaScript, information entropy attacks, and machine learning. Therefore, it is essential to pay attention to the existing research findings and trends. Stream object attacks leverage stream objects in PDF or Office documents to hide malicious code, bypassing traditional detection methods. Embedded JavaScript executes malicious actions,

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Xiao, Hongyong, Xutong Mu, and Ke Cheng. "FedRMA: A Robust Federated Learning Resistant to Multiple Poisoning Attacks." Journal of Networking and Network Applications 4, no. 1 (2024): 31–38. http://dx.doi.org/10.33969/j-nana.2024.040104.

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Federated learning allows clients to collaboratively train models without disclosing local data, yet it faces the threat of poisoning attacks from malicious clients. Existing research has proposed various robust federated learning schemes, but these often consider only a single type of poisoning attack and are inadequate for scenarios where multiple poisoning attacks occur simultaneously. To address this problem, this paper proposes FedRMA, a robust federated learning scheme resistant to multiple poisoning attacks. FedRMA eliminates the need for unrealistic prior knowledge and defends against

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Wang, Qingya, Yi Wu, Haojun Xuan, and Huishu Wu. "FLARE: A Backdoor Attack to Federated Learning with Refined Evasion." Mathematics 12, no. 23 (2024): 3751. http://dx.doi.org/10.3390/math12233751.

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Federated Learning (FL) is vulnerable to backdoor attacks in which attackers inject malicious behaviors into the global model. To counter these attacks, existing works mainly introduce sophisticated defenses by analyzing model parameters and utilizing robust aggregation strategies. However, we find that FL systems can still be attacked by exploiting their inherent complexity. In this paper, we propose a novel three-stage backdoor attack strategy named FLARE: A Backdoor Attack to Federated Learning with Refined Evasion, which is designed to operate under the radar of conventional defense strate

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Li, Muhai, and Ming Li. "An Adaptive Approach for Defending against DDoS Attacks." Mathematical Problems in Engineering 2010 (2010): 1–15. http://dx.doi.org/10.1155/2010/570940.

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In various network attacks, the Distributed Denial-of-Service (DDoS) attack is a severe threat. In order to deal with this kind of attack in time, it is necessary to establish a special type of defense system to change strategy dynamically against attacks. In this paper, we introduce an adaptive approach, which is used for defending against DDoS attacks, based on normal traffic analysis. The approach can check DDoS attacks and adaptively adjust its configurations according to the network condition and attack severity. In order to insure the common users to visit the victim server that is being

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Liang, Jiaqi, Yibei Wu, Jun’e Li, Xiong Chen, Heqin Tong, and Ming Ni. "Security Risk Analysis of Active Distribution Networks with Large-Scale Controllable Loads under Malicious Attacks." Complexity 2021 (February 19, 2021): 1–12. http://dx.doi.org/10.1155/2021/6659879.

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With the development of distributed networks, the remote controllability of the distributed energy objects and the vulnerability of user-side information security protection measures make distributed energy objects extremely vulnerable to malicious control by attackers. Hence, the large-scale loads may produce abnormal operation performance, such as load casting/dropping synchronously or frequent and synchronous casting and dropping, and hence, it can threaten the security and stable operation of the distribution networks. First, we analyze the security threats faced by industrial controllable

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Meamari, Ehsan, Khadijeh Afhamisisi, and Hadi Shahriar Shahhoseini. "Game Theory-Based Analysis on Interactions Among Secondary and Malicious Users in Coordinated Jamming Attack in Cognitive Radio Systems." Journal of Circuits, Systems and Computers 25, no. 08 (2016): 1650097. http://dx.doi.org/10.1142/s0218126616500973.

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IEEE 802.22 Standard utilizes cognitive radio (CR) techniques to allow sharing unused spectrum band. CR is vulnerable to various attacks such as jamming attacks. This paper has focused on coordinated jamming attacks. A simple strategy for secondary users is to change their bands and switch to other appropriate bands when the jamming attack has occurred. Also, the malicious users should switch to other bands in order to jam the secondary users. To address this problem, a game theoretical method is proposed to analyze coordinated jamming attacks in CR. Then, using Nash equilibrium on the propose

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Zikratov, I. A., T. V. Zikratova, and E. A. Novikov. "Swarm Robotics System Algorithm for Defense against Coordinated Behavior Strategy Attacks." Proceedings of Telecommunication Universities 10, no. 3 (2024): 75–86. http://dx.doi.org/10.31854/1813-324x-2024-10-3-75-86.

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Problem statement: designing the defense mechanism against coordinated behavior strategy attacks for mobile multiagent robotic systems. Possible attacks of that kind may be carried out by use message interception, creating and transmitting disinformation, and other actions, that does not have identifiable characteristics of saboteur intrusion, and lead to making incorrect or non-optimal decision by group of robots. The purpose of the work: the increase of probability of detection coordinated behavior strategy attacks on mobile multiagent robotic systems. Methods used: proposed algorithm is fur

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Hsieh, Chih-Hsiang, Wei-Kuan Wang, Cheng-Xun Wang, Shi-Chun Tsai, and Yi-Bing Lin. "Efficient Detection of Link-Flooding Attacks with Deep Learning." Sustainability 13, no. 22 (2021): 12514. http://dx.doi.org/10.3390/su132212514.

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The DDoS attack is one of the most notorious attacks, and the severe impact of the DDoS attack on GitHub in 2018 raises the importance of designing effective defense methods for detecting this type of attack. Unlike the traditional network architecture that takes too long to cope with DDoS attacks, we focus on link-flooding attacks that do not directly attack the target. An effective defense mechanism is crucial since as long as a link-flooding attack is undetected, it will cause problems over the Internet. With the flexibility of software-defined networking, we design a novel framework and im

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Wang, Xingyu. "Classification of Malicious Document Detection Based on Artificial Intelligence." International Journal of Computer Science and Information Technology 2, no. 2 (2024): 108–13. https://doi.org/10.62051/ijcsit.v2n2.10.

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Malicious document attacks are one of the severe threats in the current field of cybersecurity. This paper compares traditional and artificial intelligence methods in detecting malicious documents and proposes an application and technical framework of artificial intelligence in malicious document detection (AI-DDNet). First, it introduces various methods of malicious file attacks, including malicious code attacks, object embedding attacks, document vulnerability attacks, and remote link attacks. Then, it systematically elaborates on traditional static and dynamic analysis methods, as well as s

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Thakur, Mandeep, and Amninder Kaur Gill. "Detection and Isolation Technique for Blackhole Attack in Wireless Sensor Network." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 8 (2017): 25. http://dx.doi.org/10.23956/ijarcsse.v7i8.12.

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A wireless sensor network comprises of countless spread over a particular territory where we need to take care of at the progressions going ahead there. A sensor hub, for the most part, comprises of sensors, actuators, memory, a processor and they do have correspondence capacity. These sorts of networks are much powerless against security attacks. Many kinds of active and passive attacks are conceivable in the sensor network. Among all the conceivable active attacks, sinkhole attack is the most widely recognized and destructive attack. This attack debases network execution and prompts denial o

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Almomani, Ammar, Mohammad Alauthman, Firas Albalas, O. Dorgham, and Atef Obeidat. "An Online Intrusion Detection System to Cloud Computing Based on Neucube Algorithms." International Journal of Cloud Applications and Computing 8, no. 2 (2018): 96–112. http://dx.doi.org/10.4018/ijcac.2018040105.

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This article describes how as network traffic grows, attacks on traffic become more complicated and harder to detect. Recently, researchers have begun to explore machine learning techniques with cloud computing technologies to classify network threats. So, new and creative ways are needed to enhance intrusion detection system. This article addresses the source of the above issues through detecting an intrusion in cloud computing before it further disrupts normal network operations, because the complexity of malicious attack techniques have evolved from traditional malicious attack technologies

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Taylor, O. E., P. S. Ezekiel, and V. T. Emmah. "A Mult-Task System for Detecting and Classifying Malware Signatures Using Random Forest Classifier." Advances in Multidisciplinary and scientific Research Journal Publication 29 (December 15, 2021): 73–84. http://dx.doi.org/10.22624/aims/abmic2021-v2-p6.

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The rapid increase in the use of information technology has made cyber-attacks a major concern in the use of internet by users globally. These attacks are carried out in different forms, some are carried out as phishing, man in the middle, malicious applications and so on. In this study we will focus on malware attack. Malicious applications have been a major challenge in the use of applications on windows operating system. These malicious attacks are being carried out in different forms. Some of these attacks are trojan, ransom, keylogger etc. The need to detect and classifier these malicious

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Lakshmi, Vimitha R. Vidhya. "VADIA-Verkle Tree-based Approach for Dealing Data Integrity Attacks in Opportunistic Mobile Social Networks." Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications 15, no. 1 (2024): 154–71. http://dx.doi.org/10.58346/jowua.2024.i1.011.

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Opportunistic Mobile Social Networks (OMSN) are prone to data integrity attacks that jeopardize the integrity of the routing data inside the network. Among the several techniques that cope with these attacks in OMSN, tree-based approaches have proven to be the most effective due to its ease of data verification and ensurance in data integrity. This paper evaluates two tree-based data structures, Merkle tree and Verkle tree in terms of their effectiveness in detecting and preventing such attacks. The evaluation considers tree-generation time and proof-checking time, and the results demonstrate

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Vaddadi, Srinivas A., Sanjaikanth E. Vadakkethil Somanathan Pillai, Santosh Reddy Addula, Rohith Vallabhaneni, and Bhuvanesh Ananthan. "An efficient convolutional neural network for adversarial training against adversarial attack." Indonesian Journal of Electrical Engineering and Computer Science 36, no. 3 (2024): 1769. http://dx.doi.org/10.11591/ijeecs.v36.i3.pp1769-1777.

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Convolutional neural networks (CNN) are widely used by researchers due to their extensive advantages over various applications. However, images are highly susceptible to malicious attacks using perturbations that are unrecognized even under human intervention. This causes significant security perils and challenges to CNN-related applications. In this article, an efficient adversarial training model against malevolent attacks is demonstrated. This model is highly robust to black-box malicious examples, it is processed with different malicious samples. Initially, malicious training models like f

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Srinivas, A. Vaddadi Sanjaikanth E. Vadakkethil Somanathan Pillai Santosh Reddy Addula Rohith Vallabhaneni Bhuvanesh Ananthan. "An efficient convolutional neural network for adversarial training against adversarial attack." Indonesian Journal of Electrical Engineering and Computer Science 36, no. 3 (2024): 1769–77. https://doi.org/10.11591/ijeecs.v36.i3.pp1769-1777.

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Convolutional neural networks (CNN) are widely used by researchers due to their extensive advantages over various applications. However, images are highly susceptible to malicious attacks using perturbations that are unrecognized even under human intervention. This causes significant security perils and challenges to CNN-related applications. In this article, an efficient adversarial training model against malevolent attacks is demonstrated. This model is highly robust to black-box malicious examples, it is processed with different malicious samples. Initially, malicious training models like f

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Choi, Sunoh, and Jaehyuk Cho. "Novel Feature Extraction Method for Detecting Malicious MQTT Traffic Using Seq2Seq." Applied Sciences 12, no. 23 (2022): 12306. http://dx.doi.org/10.3390/app122312306.

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Owing to their wide application, Internet of Things systems have been the target of malicious attacks. These attacks included DoS, flood, SlowITe, malformed, and brute-force attacks. A dataset that includes these attacks was recently released. However, the attack detection accuracy reported in previous studies has not been satisfactory because the studies used too many features that are not important in detecting malicious message queue telemetry transport (MQTT) traffic. Therefore, this study aims to analyze these attacks. Herein, a novel feature extraction method is proposed that includes th

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Kim, Dohoon, Donghee Choi, and Jonghyun Jin. "Method for Detecting Core Malware Sites Related to Biomedical Information Systems." Computational and Mathematical Methods in Medicine 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/756842.

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Most advanced persistent threat attacks target web users through malicious code within landing (exploit) or distribution sites. There is an urgent need to block the affected websites. Attacks on biomedical information systems are no exception to this issue. In this paper, we present a method for locating malicious websites that attempt to attack biomedical information systems. Our approach uses malicious code crawling to rearrange websites in the order of their risk index by analyzing the centrality between malware sites and proactively eliminates the root of these sites by finding the core-hu

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Almuhairi, Thani, Ahmad Almarri, and Khalid Hokal. "An Artificial Intelligence-based Intrusion Detection System." Journal of Cybersecurity and Information Management 07, no. 02 (2021): 95–111. http://dx.doi.org/10.54216/jcim.07.02.04.

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Intrusion detection systems have been used in many systems to avoid malicious attacks. Traditionally, these intrusion detection systems use signature-based classification to detect predefined attacks and monitor the network's overall traffic. These intrusion detection systems often fail when an unseen attack occurs, which does not match with predefined attack signatures, leaving the system hopeless and vulnerable. In addition, as new attacks emerge, we need to update the database of attack signatures, which contains the attack information. This raises concerns because it is almost impossible t

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Et. al., Leelavathy S,. "A Secure Methodology to Detect and Prevent Ddos and Sql Injection Attacks." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (2021): 341–46. http://dx.doi.org/10.17762/turcomat.v12i2.722.

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As most of the applications host on cloud, Security is a major concern for the data owners. The cloud environment has to be secure and protect data owner data from cloud attacks. In this project work, we study about securing firewall against client side attacks namely Denial of firewall and SQL injection attacks. Denial of firewall is nothing but overloading the firewall by bursting n number of requests through vulnerable scripts. SQL injection attack is defined as bypassing the security protocols by malicious scripts. Thus we proposed to design and develop a web application to detect and prev

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APOSTOL, Mihai, Bogdan PALINIUC, Rareș MORAR, and Florin VIDU. "Malicious Strategy: Watering Hole Attacks." Romanian Cyber Security Journal 4, no. 1 (2022): 29–37. http://dx.doi.org/10.54851/v4i1y202204.

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Lee, Jaehyun, Youngho Cho, Ryungeon Lee, et al. "A Novel Data Sanitization Method Based on Dynamic Dataset Partition and Inspection Against Data Poisoning Attacks." Electronics 14, no. 2 (2025): 374. https://doi.org/10.3390/electronics14020374.

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Deep learning (DL) technology has shown outstanding performance in various fields such as object recognition and classification, speech recognition, and natural language processing. However, it is well known that DL models are vulnerable to data poisoning attacks, where adversaries modify or inject data samples maliciously during the training phase, leading to degraded classification accuracy or misclassification. Since data poisoning attacks keep evolving to avoid existing defense methods, security researchers thoroughly examine data poisoning attack models and devise more reliable and effect

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Liu, Zihao, Tianhao Wang, Mengdi Huai, and Chenglin Miao. "Backdoor Attacks via Machine Unlearning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 13 (2024): 14115–23. http://dx.doi.org/10.1609/aaai.v38i13.29321.

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As a new paradigm to erase data from a model and protect user privacy, machine unlearning has drawn significant attention. However, existing studies on machine unlearning mainly focus on its effectiveness and efficiency, neglecting the security challenges introduced by this technique. In this paper, we aim to bridge this gap and study the possibility of conducting malicious attacks leveraging machine unlearning. Specifically, we consider the backdoor attack via machine unlearning, where an attacker seeks to inject a backdoor in the unlearned model by submitting malicious unlearning requests, s

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Li, Lixiang, Jürgen Kurths, Yixian Yang, and Guole Liu. "Prevention and Trust Evaluation Scheme Based on Interpersonal Relationships for Large-Scale Peer-To-Peer Networks." Mathematical Problems in Engineering 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/189213.

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In recent years, the complex network as the frontier of complex system has received more and more attention. Peer-to-peer (P2P) networks with openness, anonymity, and dynamic nature are vulnerable and are easily attacked by peers with malicious behaviors. Building trusted relationships among peers in a large-scale distributed P2P system is a fundamental and challenging research topic. Based on interpersonal relationships among peers of large-scale P2P networks, we present prevention and trust evaluation scheme, called IRTrust. The framework incorporates a strategy of identity authentication an

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Govindaraj, Mareeswari. "Assorted Attack Detection for IoT." International Journal of Research in Engineering, Science and Management 3, no. 9 (2020): 52–55. http://dx.doi.org/10.47607/ijresm.2020.285.

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While IoT has huge social impact, it comes with a number of key security Challenges. Smart devices are vulnerable to variety of attacks. In this project, we propose the solution to detect all types of attacks in an IoT environment. For this, we initially create the wireless network and then implement mobility and energy in every node of the network. Later we intentionally create some malicious in the network for detecting the attacks. Then, we Perform data transmission between the nodes as a regular manner. For data transmission we used AODV protocol. Finally, we identified the malicious behav

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Huang, Wenli, Liang Chen, and Junli Li. "A Critical Candidate Node-Based Attack Model of Network Controllability." Entropy 26, no. 7 (2024): 580. http://dx.doi.org/10.3390/e26070580.

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The controllability of complex networks is a core issue in network research. Assessing the controllability robustness of networks under destructive attacks holds significant practical importance. This paper studies the controllability of networks from the perspective of malicious attacks. A novel attack model is proposed to evaluate and challenge network controllability. This method disrupts network controllability with high precision by identifying and targeting critical candidate nodes. The model is compared with traditional attack methods, including degree-based, betweenness-based, closenes

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Raj Panakadan, Rusheel, Chitluri Dhanush, and Dr Carmel Mary Belinda. "An effective solution for DDOS attack." International Journal of Engineering & Technology 7, no. 1.7 (2018): 194. http://dx.doi.org/10.14419/ijet.v7i1.7.10650.

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Distributed Denial of Service (DDoS) attacks constitute one of the major threats and among the hardest security problems in today’s internet. Defense against these attacks is complicated by spoofed source IP addresses, which gives us a tough task to find out the packets origin. So this paper represents a solution for a DDoS attack. We’ll be using wireshark tool to analyze the network traffic of any interface and find malicious activity by hackers. An algorithm is written at the server side so that if any malicious user sends asynchronous requests at a rate of (>=30 requests per second) then

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A. Sathiya Priya and A. Sandhiya. "Machine learning based Cyber Attack detection on Internet Traffic." International Journal of Science and Research Archive 11, no. 2 (2024): 619–24. http://dx.doi.org/10.30574/ijsra.2024.11.2.0459.

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Cyber attacks on the internet have become increasingly sophisticated and frequent, posing significant challenges to cybersecurity. Traditional rule-based methods for detecting these attacks often struggle to keep pace with the evolving tactics of malicious actors. In this context, machine learning (ML) techniques have emerged as a promising approach for cyber attack detection due to their ability to analyze large volumes of data and identify patterns indicative of malicious behavior. The proposed framework for utilizing machine learning in cyber attack detection on the internet. The framework

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Pravylo, Valerii, and Yevhenii Averkiiev. "ANALYSING MALICIOUS SOFTWARE SUPPORTING DDOS ATTACKS ON IOT NETWORKS." Information and Telecommunication Sciences, no. 1 (June 28, 2024): 50–54. http://dx.doi.org/10.20535/2411-2976.12024.50-54.

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Background. With the proliferation of Internet of Things (IoT) networks in the digital age, the risk of cyberattacks, especially DDoS attacks, is also increasing. IoT devices such as smart refrigerators, thermostats, webcams and other Internet-connected home appliances are being targeted by attackers who can use them as part of a botnet to conduct DDoS attacks. These devices often have inadequate network security and are rarely updated, making them vulnerable. DDoS attacks can result in significant losses such as lost revenue, reputational damage and costs to restore services. So, the vulnerab

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Popov, Georgiy Aleksandrovich, Nadezhda Valerievna Daviduk, Kirill Dmitrievich Kuzovlev, Shamil Shavketovich Iksanov, and Albert Salmanovich Safaraliev. "Analysis of the nearest attack moment in critical infrastructure objects with a gradual change in the intensity of attacks over time." Vestnik of Astrakhan State Technical University. Series: Management, computer science and informatics 2025, no. 1 (2025): 46–55. https://doi.org/10.24143/2072-9502-2025-1-46-55.

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Based on the apparatus of regenerating processes, a model is analyzed that describes the process of malicious attacks on a protected object of critical information infrastructure (CII). It is assumed that the intervals between the successive moments of preparation and implementation of malicious attacks are completely independent. These intervals are quite large for each of the attack sources, and the sources do not communicate with each other. The probability of a successful attack from a single source is quite low, so the moments of a successful attack for each of the attack sources are far

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Wang, Hai Tao, Hui Chen, Xue Ping Zhang, and Li Yan. "A Novel Cluster Based Survivable Routing Protocol for Wireless Sensor Network." Applied Mechanics and Materials 556-562 (May 2014): 5577–81. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.5577.

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Sensor nodes easily suffer from failure, attack or capture because of the limited energy, storage, communication ability, complex and severe network environment when WSN is applied to emergency or battlefield environment. Thus, the basic scout mission is influenced. In this paper, a survivability route protocol named SRPC in cluster-based WSN is put forward. The protocol uses key negotiation and identity authentication mechanism to resist the attacks of malicious nodes; when the main cluster head is destroyed, monitoring data will be transmitted to the base station by the backup cluster head c

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Trivedi, Munesh C., and Sachin Malhotra. "Identification and Prevention of Joint Gray Hole and Black Hole Attacks." International Journal of Ambient Computing and Intelligence 10, no. 2 (2019): 80–90. http://dx.doi.org/10.4018/ijaci.2019040106.

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Ad-hoc networks consist of a set of autonomous communicating devices that can communicate with each other by establishing multi-hop radio connections, and these connections are maintained in a localized manner. In these types of networks, especially where the nature of communicating nodes is mobile, e.g., MANETs, maintaining security remains a serious challenge due to their wireless, open, and shared communication medium, reliance on cooperative algorithms, dynamically adaptable topologies, an absence of centralized watching points, etc. Most of the existing protocols, utilized for routing in

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