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Journal articles on the topic 'False positive attacks'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Su, Man Nam, and Ho Cho Tae. "A METHOD FOR DETECTING FALSE POSITIVE AND FALSE NEGATIVE ATTACKS USING SIMULATION MODELS IN STATISTICAL ENROUTE FILTERING BASED WSNS." Advances in Vision Computing: An International Journal (AVC) 3, no. 3 (2016): 09–16. https://doi.org/10.5281/zenodo.3518809.

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In wireless sensor networks, adversaries compromise sensor nodes to damage the network though potential threats such as false positive and false negative attacks. The false positive attacks cause energy drain and false alarms, and false negative attacks generate information loss. To address the false positive attacks in the sensor network, a statistical en-route filtering (SEF) detects the false report in intermediate nodes. Even though the scheme detects the false report against the false positive attack, it is difficult to detect false MACs in a legitimate report against the false negative a

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Su, Man Nam, and Ho Cho Tae. "A METHOD FOR DETECTING FALSE POSITIVE AND FALSE NEGATIVE ATTACKS USING SIMULATION MODELS IN STATISTICAL ENROUTE FILTERING BASED WSNS." Advances in Vision Computing: An International Journal (AVC) 3, no. 3 (2016): 09–16. https://doi.org/10.5281/zenodo.3689099.

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In wireless sensor networks, adversaries compromise sensor nodes to damage the network though potential threats such as false positive and false negative attacks. The false positive attacks cause energy drain and false alarms, and false negative attacks generate information loss. To address the false positive attacks in the sensor network, a statistical en-route filtering (SEF) detects the false report in intermediate nodes. Even though the scheme detects the false report against the false positive attack, it is difficult to detect false MACs in a legitimate report against the false negative a

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3

Shang, Yilun. "False Positive and False Negative Effects on Network Attacks." Journal of Statistical Physics 170, no. 1 (2017): 141–64. http://dx.doi.org/10.1007/s10955-017-1923-7.

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Wang, Chao, Yunxiao Sun, Wenting Wang, Hongri Liu, and Bailing Wang. "Hybrid Intrusion Detection System Based on Combination of Random Forest and Autoencoder." Symmetry 15, no. 3 (2023): 568. http://dx.doi.org/10.3390/sym15030568.

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To cope with the rising threats posed by network attacks, machine learning-based intrusion detection systems (IDSs) have been intensively researched. However, there are several issues that need to be addressed. It is difficult to deal with unknown attacks that do not appear in the training set, and as a result, poor detection rates are produced for these unknown attacks. Furthermore, IDSs suffer from high false positive rate. As different models learn data characteristics from different perspectives, in this work we propose a hybrid IDS which leverages both random forest (RF) and autoencoder (

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Bhavani, Yerram, Vinjamuri Janaki, and Rangu Sridevi. "IP Traceback using Flow Based Classification." Recent Advances in Computer Science and Communications 13, no. 3 (2020): 482–90. http://dx.doi.org/10.2174/2213275912666190328200635.

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Background:Distributed Denial of Service (DDoS) attack is a major threat over the internet. The IP traceback mechanism defends against DDoS attacks by tracing the path traversed by attack packets. The existing traceback techniques proposed till now are found with few short comings. The victim required many number of packets to trace the attack path. The requirement of a large number of packets resulted in more number of combinations and more false positives.Methods:To generate a unique value for the IP address of the routers in the attack path Chinese Remainder theorem is applied. This helped

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Shahriar, Hossain, Hisham Haddad, and Vamshee Krishna Devendran. "Request and Response Analysis Framework for Mitigating Clickjacking Attacks." International Journal of Secure Software Engineering 6, no. 3 (2015): 1–25. http://dx.doi.org/10.4018/ijsse.2015070101.

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This paper addresses the detection of clickjacking attacks, which is an emerging web application security issue. The authors propose a web application request and response page analysis framework to detect clickjacking attacks. Their framework considers not only inspects visual features related to frame, JavaScript code pattern in details to match with known attack signatures. The proposed approach is able to detect advanced clickjacking attacks such as cursorjacking, double click, and history object-based attacks. The authors evaluate the proposed approach with a set of legitimate and malicio

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Pulls, Tobias, and Rasmus Dahlberg. "Website Fingerprinting with Website Oracles." Proceedings on Privacy Enhancing Technologies 2020, no. 1 (2020): 235–55. http://dx.doi.org/10.2478/popets-2020-0013.

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AbstractWebsite Fingerprinting (WF) attacks are a subset of traffic analysis attacks where a local passive attacker attempts to infer which websites a target victim is visiting over an encrypted tunnel, such as the anonymity network Tor. We introduce the security notion of a Website Oracle (WO) that gives a WF attacker the capability to determine whether a particular monitored website was among the websites visited by Tor clients at the time of a victim’s trace. Our simulations show that combining a WO with a WF attack—which we refer to as a WF+WO attack—significantly reduces false positives f

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Zeinalpour, Alireza, and Hassan A. Ahmed. "Addressing the Effectiveness of DDoS-Attack Detection Methods Based on the Clustering Method Using an Ensemble Method." Electronics 11, no. 17 (2022): 2736. http://dx.doi.org/10.3390/electronics11172736.

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The curse of dimensionality, due to lots of network-traffic attributes, has a negative impact on machine learning algorithms in detecting distributed denial of service (DDoS) attacks. This study investigated whether adding the filter and wrapper methods, preceded by combined clustering algorithms using the Vote classifier method, was effective in lowering the false-positive rates of DDoS-attack detection methods. We examined this process to address the curse of dimensionality of machine learning algorithms in detecting DDoS attacks. The results of this study, using ANOVA statistical analyses,

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Wang, Wentao, Xuan Ke, and Lingxia Wang. "A HMM-R Approach to Detect L-DDoS Attack Adaptively on SDN Controller." Future Internet 10, no. 9 (2018): 83. http://dx.doi.org/10.3390/fi10090083.

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A data center network is vulnerable to suffer from concealed low-rate distributed denial of service (L-DDoS) attacks because its data flow has the characteristics of data flow delay, diversity, and synchronization. Several studies have proposed addressing the detection of L-DDoS attacks, most of them are only detect L-DDoS attacks at a fixed rate. These methods cause low true positive and high false positive in detecting multi-rate L-DDoS attacks. Software defined network (SDN) is a new network architecture that can centrally control the network. We use an SDN controller to collect and analyze

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10

Jeong, Yeonseon, Hyunghoon Kim, and Hyo Jin Jo. "ASD: ARP Spoofing Detector Using OpenWrt." Security and Communication Networks 2022 (March 29, 2022): 1–9. http://dx.doi.org/10.1155/2022/2196998.

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The address resolution protocol (ARP) is one of the most important communication protocols in a local area network (LAN). However, since there is no authentication procedure, the ARP is vulnerable to cyberattack such as ARP spoofing. Since ARP spoofing can be connected to critical attacks, including a man-in-the-middle (MITM) attack, detecting ARP spoofing initially without returning false-positive alarms is important. In general, however, existing works for ARP spoofing are unable to distinguish between ARP spoofing and connections from virtual machine (VM) guests, which results in false-posi

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Dalvi, Sailee, Gilad Gressel, and Dr Krishnashree Achuthan. "Tuning the False Positive Rate / False Negative Rate with Phishing Detection Models." International Journal of Engineering and Advanced Technology 9, no. 1s5 (2019): 7–13. http://dx.doi.org/10.35940/ijeat.a1002.1291s519.

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Phishing attacks have risen by 209% in the last 10 years according to the Anti Phishing Working Group (APWG) statistics [19]. Machine learning is commonly used to detect phishing attacks. Researchers have traditionally judged phishing detection models with either accuracy or F1-scores, however in this paper we argue that a single metric alone will never correlate to a successful deployment of machine learning phishing detection model. This is because every machine learning model will have an inherent trade-off between it’s False Positive Rate (FPR) and False Negative Rate (FNR). Tuning the tra

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Ajdani, Mahdi. "Deep Learning-Based Intrusion Detection Systems." International Journal of Information Security and Privacy 19, no. 1 (2025): 1–15. https://doi.org/10.4018/ijisp.383299.

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Given the increasing growth of cyber-attacks, the need for intrusion detection systems (IDS) with higher accuracy and efficiency is critical. This paper presents a novel approach using Generative Adversarial Networks (GANs) for intrusion detection. The proposed model leverages deep learning to extract complex features and uses GANs to generate synthetic data, improving IDS accuracy and efficiency. This approach reduces false positive and negative rates while increasing the accuracy of detecting unknown attacks. Experimental results on the NSL-KDD and CICIDS2017 datasets show 98.2% accuracy, a

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Borah, Abinash, and Anirudh Paranjothi. "Enhancing VANET Security: An Unsupervised Learning Approach for Mitigating False Information Attacks in VANETs." Electronics 14, no. 1 (2024): 58. https://doi.org/10.3390/electronics14010058.

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Vehicular ad hoc networks (VANETs) enable communication among vehicles and between vehicles and infrastructure to provide safety and comfort to the users. Malicious nodes in VANETs may broadcast false information to create the impression of a fake event or road congestion. In addition, several malicious nodes may collude to collectively launch a false information attack to increase the credibility of the attack. Detection of these attacks is critical to mitigate the potential risks they bring to the safety of users. Existing techniques for detecting false information attacks in VANETs use diff

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Aladaileh, Mohammad Adnan, Mohammed Anbar, Ahmed J. Hintaw, Iznan H. Hasbullah, Abdullah Ahmed Bahashwan, and Shadi Al-Sarawi. "Renyi Joint Entropy-Based Dynamic Threshold Approach to Detect DDoS Attacks against SDN Controller with Various Traffic Rates." Applied Sciences 12, no. 12 (2022): 6127. http://dx.doi.org/10.3390/app12126127.

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The increasing incidence of distributed denial-of-service (DDoS) attacks has made software-defined networking (SDN) more vulnerable to the depletion of controller resources. DDoS attacks prevent the SDN controller from processing all incoming data efficiently, potentially disrupting a network or denying legitimate users access to network services. Thus, the protection of the SDN controller is crucial, especially from the ones that exploit the SDN characteristics. In this paper, the authors propose an efficient detection approach for low- and high-rate DDoS attacks on the controller with a high

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15

Wang, Chuan Sheng, Fu Hau Hsu, Shih Jen Chen, Yan Ling Hwang, and Min Hao Wu. "Detecting Phishing Sites Using URLs Collected from Emails." Applied Mechanics and Materials 479-480 (December 2013): 916–22. http://dx.doi.org/10.4028/www.scientific.net/amm.479-480.916.

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Phishing is the malicious behavior of stealing personal information from computer users. It is a very popular account-theft-method among cyber criminals. Hence, developing a new approach to solve phishing attacks becomes an important issue. This paper proposes a router-based solution, called Shark, to phishing attacks. The router-based property allows Shark to protect a whole network without the need to install or maintain any software in any internal host. Based on the URLs collected from emails and the properties of the web pages pointed by these URLs, Shark quickly and automatically identif

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Guo, Guang Feng. "The Study of the Ontology and Context Verification Based Intrusion Detection Model." Applied Mechanics and Materials 644-650 (September 2014): 3338–41. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.3338.

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During the 30-year development of the Intrusion Detection System, the problems such as the high false-positive rate have always plagued the users. Therefore, the ontology and context verification based intrusion detection model (OCVIDM) was put forward to connect the description of attack’s signatures and context effectively. The OCVIDM established the knowledge base of the intrusion detection ontology that was regarded as the center of efficient filtering platform of the false alerts to realize the automatic validation of the alarm and self-acting judgment of the real attacks, so as to achiev

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17

Prasetio, Dimaz Arno, Kusrini Kusrini, and M. Rudyanto Arief. "Cross-site Scripting Attack Detection Using Machine Learning with Hybrid Features." JURNAL INFOTEL 13, no. 1 (2021): 1–6. http://dx.doi.org/10.20895/infotel.v13i1.606.

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This study aims to measure the classification accuracy of XSS attacks by using a combination of two methods of determining feature characteristics, namely using linguistic computation and feature selection. XSS attacks have a certain pattern in their character arrangement, this can be studied by learners using n-gram modeling, but in certain cases XSS characteristics can contain a certain meta and synthetic this can be learned using feature selection modeling. From the results of this research, hybrid feature modeling gives good accuracy with an accuracy value of 99.87%, it is better than prev

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18

Shahriar, Hossain, and Hisham M. Haddad. "Client-Side Detection of Clickjacking Attacks." International Journal of Information Security and Privacy 9, no. 1 (2015): 1–25. http://dx.doi.org/10.4018/ijisp.2015010101.

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Clickjacking attacks are emerging threat for web application users where click operations performed by victims lead to security breaches such as compromising webcams and posting unintended messages. Effective client-side defense technique could prevent the possible victims. This paper presents a client side approach to detect clickjacking attacks. The authors' approach examines web page requests and responses; the proposed approach is designed to detect advanced attack types such as cursorjacking, double click, and history object-based attacks. They evaluate the proposed approach with a set of

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19

Sharma, Richa, Teek Parval Sharma, and Ajay Kumar Sharma. "Detecting and Preventing Misbehaving Intruders in the Internet of Vehicles." International Journal of Cloud Applications and Computing 12, no. 1 (2022): 1–21. http://dx.doi.org/10.4018/ijcac.295242.

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The advent of new vehicular advances and accessibility of new network access mediums have evolved service providers with heterogeneous-vehicular collaboration. The performance of heterogeneous-vehicular collaboration depends on the possibility of accurate, up-to-date vehicular information shared by Cooperative- Awareness Messages (CAMs) among neighboring vehicles. Although exchanging wrong mobility coordinates leading to disruption on the Internet of Vehicles (IoVs) applicability. To address these issues, a misbehavior detection approach is proposed which acts as a second wall of defense. Our

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20

Oluwole Ogini, Nicholas, Wilfred Adigwe, and Noah Oghenefego Ogwara. "Distributed Denial of Service Attack Detection and Prevention Model for IoT based Computing Environment using Ensemble Machine Learning Approach." International Journal of Network Security & Its Applications 14, no. 4 (2022): 39–53. http://dx.doi.org/10.5121/ijnsa.2022.14403.

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Defending against Distributed Denial of Service (DDoS) in the Internet of Things (IoT) computing environment is a challenging task. DDoS attacks are type of collective attack in which attackers work together to compromise internet security and services. The resource-constrained devices used in IoT deployments have made it even easier for an attacker to break, because of the vast number of vulnerable IoT devices with significant compute power. This paper proposed an ensemble machine learning (ML) model using the bagging technique to detect and prevent DDoS attacks in the IoT computing environme

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Nicholas, Oluwole Ogini, Adigwe Wilfred, and Oghenefego Ogwara Noah. "DISTRIBUTED DENIAL OF SERVICE ATTACK DETECTION AND PREVENTION MODEL FOR IOTBASED COMPUTING ENVIRONMENT USING ENSEMBLE MACHINE LEARNING APPROACH." International Journal of Network Security & Its Applications (IJNSA) 14, no. 4 (2022): 39–53. https://doi.org/10.5281/zenodo.7002129.

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Defending against Distributed Denial of Service (DDoS) in the Internet of Things (IoT) computing environment is a challenging task. DDoS attacks are type of collective attack in which attackers work together to compromise internet security and services. The resource-constrained devices used in IoT deployments have made it even easier for an attacker to break, because of the vast number of vulnerable IoT devices with significant compute power. This paper proposed an ensemble machine learning (ML) model using the bagging technique to detect and prevent DDoS attacks in the IoT computing environme

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Jabbar, Haidar, and Samir Al-Janabi. "AI-Driven Phishing Detection: Enhancing Cybersecurity with Reinforcement Learning." Journal of Cybersecurity and Privacy 5, no. 2 (2025): 26. https://doi.org/10.3390/jcp5020026.

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Phishing remains a persistent cybersecurity threat, often bypassing traditional detection methods due to evolving attack techniques. This study presents a Reinforcement Learning (RL)-based phishing detection framework, leveraging a Deep Q-Network (DQN) to enhance detection accuracy, reduce false positives, and improve classification performance. The model was trained and evaluated using a real-world dataset comprising 5000 emails (2500 phishing and 2500 benign) and externally validated against a synthetic phishing dataset of 1000 samples simulating unseen attacks. It achieved a 95% accuracy, 9

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Nan, Haymarn Oo, Cahyadi Risdianto Aris, Chaw Ling Teck, and Htein Maw Aung. "Flooding Attack Detection and Mitigation in SDN with Modified Adaptive Threshold Algorithm." International Journal of Computer Networks & Communications (IJCNC) 12, no. 3 (2020): 75–95. https://doi.org/10.5281/zenodo.3946219.

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Flooding attack is a network attack that sends a large amount of traffic to the victim networks or services to cause denial-of-service. In Software-Defined Networking (SDN) environment, this attack might not only breach the hosts and services but also the SDN controller. Besides, it will also cause a disconnection of links between the controller and the switches. Thus, an effective detection and mitigation technique of flooding attacks is required. Statistical analysis techniques are widely used for the detection and mitigation of flooding attacks. However, the effectiveness of these technique

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Radivilova, Tamara, Lyudmyla Kirichenko, Maksym Tawalbeh, and Andrii Ilkov. "DETECTION OF ANOMALIES IN THE TELECOMMUNICATIONS TRAFFIC BY STATISTICAL METHODS." Cybersecurity: Education, Science, Technique 11, no. 3 (2021): 183–94. http://dx.doi.org/10.28925/2663-4023.2021.11.183194.

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Anomaly detection is an important task in many areas of human life. Many statistical methods are used to detect anomalies. In this paper, statistical methods of data analysis, such as survival analysis, time series analysis (fractal), classification method (decision trees), cluster analysis, entropy method were chosen to detect anomalies. A description of the selected methods is given. To analyze anomalies, the traffic and attack implementations from an open dataset were taken. More than 3 million packets from the dataset were used to analyze the described methods. The dataset contained legiti

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Petkovic, Miodrag, Ilija Basicevic, Dragan Kukolj, and Miroslav Popovic. "Evaluation of Takagi-Sugeno-Kang fuzzy method in entropy-based detection of DDoS attacks." Computer Science and Information Systems 15, no. 1 (2018): 139–62. http://dx.doi.org/10.2298/csis160905039p.

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The detection of distributed denial of service (DDoS) attacks based on internet traffic anomalies is a method which is general in nature and can detect unknown or zero-day attacks. One of the statistical characteristics used for this purpose is network traffic entropy: a sudden change in entropy may indicate a DDoS attack. However, this approach often gives false positives, and this is the main obstacle to its wider deployment within network security equipment. In this paper, we propose a new, two-step method for detection of DDoS attacks. This method combines the approaches of network traffic

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Akram, Muhammad, Muhammad Ashraf, and Tae Ho Cho. "Enhancing the Statistical Filtering Scheme to Detect False Negative Attacks in Sensor Networks." Sukkur IBA Journal of Computing and Mathematical Sciences 1, no. 1 (2017): 52. http://dx.doi.org/10.30537/sjcms.v1i1.7.

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In this paper, we present a technique that detects both false positive and false negative attacks in statistical filtering-based wireless sensor networks. In statistical filtering scheme, legitimate reports are repeatedly verified en route before they reach the base station, which causes heavy energy consumption. While the original statistical filtering scheme detects only false reports, our proposed method promises to detect both attacks.

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Batool, Sehrish, Farrukh Zeeshan Khan, Syed Qaiser Ali Shah, et al. "Lightweight Statistical Approach towards TCP SYN Flood DDoS Attack Detection and Mitigation in SDN Environment." Security and Communication Networks 2022 (January 25, 2022): 1–14. http://dx.doi.org/10.1155/2022/2593672.

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Distributed Denial of Service (DDoS) attack is known to be one of the most lethal attacks in traditional network architecture. In this attack, the attacker uses botnets to overwhelm network resources. Botnets can be randomly compromised computers or IoT devices that are used to generate excessive traffic towards the victim, and as a result, legitimate users cannot access the services. In this research, software-defined networking (SDN) has been suggested as a solution to fight DDoS attacks. SDN uses the idea of centralized control and segregation of the data plane from the control plane. SDN i

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Babaey, Vahid, and Hamid Reza Faragardi. "Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders." Computers 14, no. 6 (2025): 205. https://doi.org/10.3390/computers14060205.

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The increasing sophistication of web-based services has intensified the risk of zero-day attacks, exposing critical vulnerabilities in user information security. Traditional detection systems often rely on labeled attack data and struggle to identify novel threats without prior knowledge. This paper introduces a novel one-class ensemble method for detecting zero-day web attacks, combining the strengths of Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and stacked autoencoders through latent representation concatenation and compression. Additionally, a structured tokenization strate

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Yu, Xiang, Wenchao Yu, Shudong Li, Xianfei Yang, Ying Chen, and Hui Lu. "WEB DDoS Attack Detection Method Based on Semisupervised Learning." Security and Communication Networks 2021 (November 29, 2021): 1–10. http://dx.doi.org/10.1155/2021/9534016.

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Since the services on the Internet are becoming increasingly abundant, all walks of life are inextricably linked with the Internet. Simultaneously, the Internet’s WEB attacks have never stopped. Relative to other common WEB attacks, WEB DDoS (distributed denial of service) will cause serious damage to the availability of the target network or system resources in a short period of time. At present, most researches are centered around machine learning-related DDoS attack detection algorithms. According to previous studies, unsupervised methods generally have a high false positive rate, while sup

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Khoeurt, Sopha, Chakchai So-In, Pakarat Musikawan, and Phet Aimtongkham. "Multidirectional Trust-Based Security Mechanisms for Sinkhole Attack Detection in the RPL Routing Protocol for Internet of Things." Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications 14, no. 3 (2023): 48–76. http://dx.doi.org/10.58346/jowua.2023.i3.005.

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The Internet of Things (IoT) has gained popularity in recent years by connecting physical objects to the Internet, enabling innovative applications. To facilitate communication in low-power and lossy networks (LLNs), the IPv6-based routing protocol for LLNs (RPL) is widely used. However, RPL’s lack of specified security models makes it vulnerable to security threats, particularly sinkhole attacks. Existing sinkhole attack detection techniques suffer from high detection delays and false positives. To overcome these limitations, in our research we propose a multidirectional trust-based detection

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Dr, Ruksar Fatima* Mohd Shafiuddin. "DETERMINING FALSE ATTACKS USING DDSGA TECHNIQUE." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 6 (2017): 103–6. https://doi.org/10.5281/zenodo.805359.

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For the sake of identifying masquerade attackers in a computer, various alignment algorithms has been proposed. The semi-global alignment algorithm (SGA) is the most effective and also efficient technique to detect these type of attacks till now, but it has not reach the level of accuracy and effectiveness required by large scale and multiuser systems. To support these shortcomings of SGA and to increase both the effectiveness and the performances of this algorithm, the Data-Driven Semi-Global Alignment, DDSGA approach has been proposed. DDSGA has much more improvements and increases the scori

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Prihantono, Yuri, and Kalamullah Ramli. "Model-Based Feature Selection for Developing Network Attack Detection and Alerting System." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 6, no. 2 (2022): 322–29. http://dx.doi.org/10.29207/resti.v6i2.3989.

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The use of the Intrusion Detection Systems (IDS) still has unresolved problems, namely the lack of accuracy in attack detection, resulting in false-positive problems and many false alarms. Machine learning is one way that is often utilized to overcome challenges that arise during the implementation of IDS.. We present a system that uses a machine learning approach to detect network attacks and send attack alerts in this study. The CSE-CICIDS2018 Dataset and Model-Based Feature Selection technique are used to assess the performance of eight classifier algorithms in identifying network attacks i

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Rao, Kareti Madhava, and S. Ramakrishna. "A Node Confident based IDS to Avoid Packet Drop Attacks for Wireless Sensor Network." International Journal of Computer Network and Information Security 13, no. 6 (2021): 41–56. http://dx.doi.org/10.5815/ijcnis.2021.06.04.

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Because of the great characteristics of Wireless Sensor Networks like easier to use and less cost of deployment, they have attracted the researchers to conduct the investigations and received the importance in various civilian and military applications. A number of security attacks have been involved due to the lack of centralized management in these networks. The packet drop attack is one of the attacks and it has a compromised node which drops the malicious packets. In WSNs, different techniques have been implemented to identify the packet drop attack but none of them provides the feasibilit

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Akanji, Oluwatobi Shadrach, Opeyemi Aderiike Abisoye, and Mohammed Awwal Iliyasu. "MITIGATING SLOW HYPERTEXT TRANSFER PROTOCOL DISTRIBUTED DENIAL OF SERVICE ATTACKS IN SOFTWARE DEFINED NETWORKS." Journal of Information and Communication Technology 20, Number 3 (2021): 277–304. http://dx.doi.org/10.32890/jict2021.20.3.1.

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Distributed Denial of Service (DDoS) attacks has been one of the persistent forms of attacks on information technology infrastructure connected to public networks due to the ease of access to DDoS attack tools. Researchers have been able to develop several techniques to curb volumetric DDoS which overwhelms the target with a large number of request packets. However, compared to slow DDoS, limited number of research has been executed on mitigating slow DDoS. Attackers have resorted to slow DDoS because it mimics the behaviour of a slow legitimate client thereby causing service unavailability. T

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Radhakrishnan, Palamalai, Senthil Kumar Seeni, Dhamotharan Rukmani Devi, et al. "DoS attack detection and hill climbing based optimal forwarder selection." Indonesian Journal of Electrical Engineering and Computer Science 36, no. 2 (2024): 882. http://dx.doi.org/10.11591/ijeecs.v36.i2.pp882-891.

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<span>Wireless networks are becoming a more and more common form of networking and communication, with several uses in many industries. However, the rising popularity has also increased security risks, such as Denial of Service (DoS) attacks. To solve these issues, Denial of Service Attack Detection and Hill Climbing (DDHC) based optimal forwarder selection in Wireless Network. The suggested method seeks to efficiently identify DoS attacks and enhance network performance by preventing the communication hiccups brought on by such attacks. Fuzzy learning method is suggested to analyze tren

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Palamalai, Radhakrishnan Senthil Kumar Seeni Dhamotharan Rukmani Devi Tumuluri Kanthimathi Devadhas David Neels Ponkumar Vikram Nattamai Sankaran Subbiah Murugan. "DoS attack detection and hill climbing based optimal forwarder selection." Indonesian Journal of Electrical Engineering and Computer Science 36, no. 2 (2024): 882–91. https://doi.org/10.11591/ijeecs.v36.i2.pp882-891.

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Wireless networks are becoming a more and more common form of networking and communication, with several uses in many industries. However, the rising popularity has also increased security risks, such as Denial of Service (DoS) attacks. To solve these issues, Denial of Service Attack Detection and Hill Climbing (DDHC) based optimal forwarder selection in Wireless Network. The suggested method seeks to efficiently identify DoS attacks and enhance network performance by preventing the communication hiccups brought on by such attacks. Fuzzy learning method is suggested to analyze trends and find

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Kulbir, Kaur Sandhu. "A NOVEL APPROACH FOR IMAGE IMPAINTING USING ADAPTIVE SVM." GLOBAL JOURNAL OF ADVANCED ENGINEERING TECHNOLOGIES AND SCIENCES 8, no. 2 (2021): 17–25. https://doi.org/10.5281/zenodo.4568089.

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Data security is critical for most businesses and even home computer users. As, all the sensitive and important information is manipulated online. Therefore, it is essential to protect the data from intruders. We need to find the best ways to protect our system from different types of attacks.Intrusions refer to the network attacks against vulnerable services, data-driven attacks on applications, host-based attacks like privilege escalation, unauthorized logins and access to sensitive files, or malware like viruses, worms and Trojan horses. The problem in current IDS is the high false positive

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Simone, Antonino, and Boris Škorić. "False positive probabilities in q-ary Tardos codes: comparison of attacks." Designs, Codes and Cryptography 75, no. 3 (2014): 519–42. http://dx.doi.org/10.1007/s10623-014-9937-5.

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BHUYAN, MD KHOKAN, Md Kamruzzaman, Sadia Islam Nilima, RABEYA KHATOON, and Nur Mohammad. "Convolutional Neural Networks Based Detection System for Cyber-attacks in Industrial Control Systems." Journal of Computer Science and Technology Studies 6, no. 3 (2024): 86–96. http://dx.doi.org/10.32996/jcsts.2024.6.3.9.

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Convolutional Neural Networks (CNNs) have emerged as a powerful tool for various pattern recognition tasks, including the detection of cyber-attacks in Industrial Control Systems (ICS). This paper presents a CNN-based detection system specifically designed to safeguard ICS from sophisticated cyber threats. The proposed system leverages the capability of CNNs to automatically learn and extract high-level features from raw data, enabling it to identify anomalies indicative of cyber-attacks with high accuracy. Utilizing a comprehensive cyber-attack dataset containing 59 different types of attacks

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Gunupusala Satyanarayana, Et al. "Efficient Inductive Transfer Learning based Framework for Zero-Day Attack Detection." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 552–59. http://dx.doi.org/10.17762/ijritcc.v11i9.8843.

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An Intrusion Detection System (IDS) is a type of security domain that tracks and evaluates network connections or system operations to detect potential security breaches, unauthorized usage, and malicious activity within computer networks. Machine learning (ML) and deep learning (DL) algorithms provide better IDS based on the labelled dataset. However, due to a lack of labelled data, its effectiveness in detecting zero-day attacks is limited. Anomaly detection methods frequently produce high False Positive Rates (FPR). Transfer learning (TL) is a powerful technique in various domains, includin

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Liu, I.-Hsien, Jung-Shian Li, Yen-Chu Peng, and Chuan-Gang Liu. "A Robust Countermeasures for Poisoning Attacks on Deep Neural Networks of Computer Interaction Systems." Applied Sciences 12, no. 15 (2022): 7753. http://dx.doi.org/10.3390/app12157753.

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In recent years, human–computer interactions have begun to apply deep neural networks (DNNs), known as deep learning, to make them work more friendly. Nowadays, adversarial example attacks, poisoning attacks, and backdoor attacks are the typical attack examples for DNNs. In this paper, we focus on poisoning attacks and analyze three poisoning attacks on DNNs. We develop a countermeasure for poisoning attacks, which is Data Washing, an algorithm based on a denoising autoencoder. It can effectively alleviate the damages inflicted upon datasets caused by poisoning attacks. Furthermore, we also pr

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Akinyelu, Andronicus A., and Aderemi O. Adewumi. "Classification of Phishing Email Using Random Forest Machine Learning Technique." Journal of Applied Mathematics 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/425731.

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Phishing is one of the major challenges faced by the world of e-commerce today. Thanks to phishing attacks, billions of dollars have been lost by many companies and individuals. In 2012, an online report put the loss due to phishing attack at about $1.5 billion. This global impact of phishing attacks will continue to be on the increase and thus requires more efficient phishing detection techniques to curb the menace. This paper investigates and reports the use of random forest machine learning algorithm in classification of phishing attacks, with the major objective of developing an improved p

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Alani, Mohammed M., and Ernesto Damiani. "XRecon: An Explainbale IoT Reconnaissance Attack Detection System Based on Ensemble Learning." Sensors 23, no. 11 (2023): 5298. http://dx.doi.org/10.3390/s23115298.

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IoT devices have grown in popularity in recent years. Statistics show that the number of online IoT devices exceeded 35 billion in 2022. This rapid growth in adoption made these devices an obvious target for malicious actors. Attacks such as botnets and malware injection usually start with a phase of reconnaissance to gather information about the target IoT device before exploitation. In this paper, we introduce a machine-learning-based detection system for reconnaissance attacks based on an explainable ensemble model. Our proposed system aims to detect scanning and reconnaissance activity of

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Priambodo, Dimas Febriyan, Achmad Husein Noor Faizi, Fika Dwi Rahmawati, Septia Ulfa Sunaringtyas, Jeckson Sidabutar, and Tiyas Yulita. "Collaborative Intrusion Detection System with Snort Machine Learning Plugin." JOIV : International Journal on Informatics Visualization 8, no. 3 (2024): 1230. http://dx.doi.org/10.62527/joiv.8.3.2018.

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The increasing prevalence of cybercrime and cyber-attacks underscores the imperative need for organizations to implement robust network security measures. Nevertheless, current Intrusion Detection Systems (IDS) often rely on single-sensor or multi-sensor in the same type of IDS, including Host-Based IDS (HIDS) or Network-Based IDS (NIDS), which inherently possess limited detection capabilities. To address this limitation, this research combines NIDS and HIDS components into a collaborative-IDS system, thus expanding the scope of intrusion detection and enhancing the efficacy of the established

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Sun, San-Tsai, and Konstantin Beznosov. "Retrofitting Existing Web Applications with Effective Dynamic Protection Against SQL Injection Attacks." International Journal of Secure Software Engineering 1, no. 1 (2010): 20–40. http://dx.doi.org/10.4018/jsse.2010102002.

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This article presents an approach for retrofitting existing Web applications with run-time protection against known, as well as unseen, SQL injection attacks (SQLIAs) without the involvement of application developers. The precision of the approach is also enhanced with a method for reducing the rate of false positives in the SQLIA detection logic, via runtime discovery of the developers’ intention for individual SQL statements made by Web applications. The proposed approach is implemented in the form of protection mechanisms for J2EE, ASP.NET, and ASP applications. Named SQLPrevent, these mech

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Cho, Tae Ho, and Su Man Nam. "MODELING AND SIMULATION OF THRESHOLD ANALYSIS FOR PVFS IN WIRELESS SENSOR NETWORKS." International Journal of Research -GRANTHAALAYAH 4, no. 8 (2016): 1–10. http://dx.doi.org/10.29121/granthaalayah.v4.i8.2016.2555.

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Wireless sensor networks (WSNs) suffer serious damage from false positive and negative attacks due to their hardware restrictions. The sensor network causes both unnecessary energy consumption and information loss through false reports and normal reports, which include false message authentication codes (MACs). A probabilistic voting-based filtering scheme (PVFS) effectively detects the two types of attacks through a pre-defined threshold, which is the number of detected false MACs in a report. Since the threshold significantly influences the ability to detect attacks, the sensor network shoul

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Man Nam, Su, and Youn Kyoung Seo. "A Method of Improving Energy Efficiency Through Geofencing and False Data Blocking In Context-Aware Architecture for Probabilistic Voting-Based Filtering Scheme of WSNs." International journal of Computer Networks & Communications 13, no. 3 (2021): 97–107. http://dx.doi.org/10.5121/ijcnc.2021.13306.

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In wireless sensor networks, sensor nodes have the disadvantage of being vulnerable to several attacks due to the use of wireless communication and constrained energy. Adversaries exploit vulnerable characteristics of these nodes to capture them and generate false positive and false negative attacks. These attacks result in false alarms in a base station and information loss in intermediate nodes. A context-aware architecture for a probabilistic voting-based filtering scheme (CAA-PVFS) identifies compromised nodes that cause the damage. Although this method immediately detects the compromised

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Gu, Yong Hao, and Wei Ming Wu. "DDoS Detection and Prevention Based on Joint Entropy and Conditional Entropy." Key Engineering Materials 474-476 (April 2011): 2129–33. http://dx.doi.org/10.4028/www.scientific.net/kem.474-476.2129.

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Distributed Denial of Service (DDoS) imposes a very serious threat to the stability of the Internet. Compared with many detection approaches, detecting DDoS attacks based on entropy has advantages such as simplicity, high sensitivity and low false positive rate. But the method with single attribute entropy has high false positive rate when detecting attribute forged attacks. This paper presents a detecting method based on joint entropy and a filtering way based on conditional entropy. The efficiency of this scheme is validated with simulation on the research lab network.

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Nam, Su Man, and Tae Ho Cho. "Discrete event simulation–based energy efficient path determination scheme for probabilistic voting–based filtering scheme in sensor networks." International Journal of Distributed Sensor Networks 16, no. 8 (2020): 155014772094913. http://dx.doi.org/10.1177/1550147720949134.

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In wireless sensor networks, sensors are extremely vulnerable to false positive and false negative attacks due to their stringent energy and computational constraints. Several en-route filtering schemes mainly focus on saving energy through early detection of false data within a short distance against these attacks; however, they cannot immediately block the false data injected by compromised nodes. A security scheme uses context-aware architecture for a probabilistic voting–based filtering scheme to detect the compromised nodes and block the injection of false data, unlike security protocols.

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Karthik, M., and M. Krishnan. "Securing an Internet of Things from Distributed Denial of Service and Mirai Botnet Attacks Using a Novel Hybrid Detection and Mitigation Mechanism." International Journal of Intelligent Engineering and Systems 14, no. 1 (2021): 113–23. http://dx.doi.org/10.22266/ijies2021.0228.12.

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Internet of Things (IoT) has become more familiar in all applications and industrial fields such as medical, military, transportation, etc. It has some limitations because of the attack model in the transmission or communication channel. Moreover, one of the deadliest attacks is known as a Distributed Denial of Service Attack (DDoS). The Presence of DDoS in network layer cause huge damage in data transmission channel that ends in data loss or collapse. To address this issue the current research focused on an innovative detection and mitigation of Mirai and DDoS attack in IoT environment. Initi

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