Results 251 to 260 of about 1,173,800 (295)

Evasion Attacks On Svm Classifier

2019 9th International Conference on Advances in Computing and Communication (ICACC), 2019
Support Vector Machine (SVM) is one of the main classification techniques used in many security-related applications like malware detection, spam filtering, etc. To incorporate SVM into real-world security applications they must be able to cope up with the attack patterns that will lead to misclassifications. In this system, the vulnerability of SVM to
null Maria, Mikhiya James, M Mruthula, Vismaya Bhaskaran, S Asha   +4 more
openaire   +1 more source

Optimal Defense Strategy against Evasion Attacks

2020 16th International Conference on Mobility, Sensing and Networking (MSN), 2020
Recent detection method based on machine learning demonstrates significant advantages against varieties of network attacks, and has been widely deployed in cloud applications. However, novel attacks such as Advanced Persistent Threats (APTs) could evade detection of the intrusion detection system, which may lead to serious data leakage in cloud ...
Jiachen Wu, Jipeng Li, Yan Wang, Yanru Zhang, Yingjie Zhou   +4 more
openaire   +1 more source

Memory Corruption Attacks, Defenses, and Evasions

2009
The chapter introduces and describes representative defense mechanisms to protect from both basic and advanced exploitation of low-level coding vulnerabilities. Exploitation of low-level coding vulnerabilities has evolved from a basic stack-based buffer overflow with code injection to highly sophisticated attack techniques.
C. Bellettini, J.L. RRushi
openaire   +2 more sources

Evasion Attack and Defense on Machine Learning Models in Cyber-Physical Systems: A Survey

IEEE Communications Surveys and Tutorials
Cyber-physical systems (CPS) are increasingly relying on machine learning (ML) techniques to reduce labor costs and improve efficiency. However, the adoption of ML also exposes CPS to potential adversarial ML attacks witnessed in the literature ...
Shunyao Wang, Ryan K. L. Ko, Guangdong Bai, Naipeng Dong, Taejun Choi, Yanjun Zhang   +5 more
semanticscholar   +1 more source

Harbor attack: A pursuit-evasion game

2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2011
This paper studies a concrete pursuit-evasion game in which the evader, a.k.a. the intruder, tries to reach a harbor via a rectangular channel, while the pursuer, a.k.a. the defender, tries to stop him. Our goal is to develop insight into the nature of this game, the impact of the quality of information, and the potential evasion and defense numerical ...
Jean Walrand, Elijah Polak, Hoam Chung
openaire   +1 more source

EaTVul: ChatGPT-based Evasion Attack Against Software Vulnerability Detection

USENIX Security Symposium
Recently, deep learning has demonstrated promising results in enhancing the accuracy of vulnerability detection and identifying vulnerabilities in software. However, these techniques are still vulnerable to attacks.
Shigang Liu, Di Cao, Junae Kim, Tamas Abraham, Paul Montague, Seyit Ahmet Camtepe, Jun Zhang, Yang Xiang   +7 more
semanticscholar   +1 more source

A Novel Deep Reinforcement Learning (DRL) Method for Detecting Evasion Attack in Iot Environment

2024 International Conference on Integrated Circuits and Communication Systems (ICICACS)
The growing prevalence of Internet of Things (IoT) devices being connected to the internet has resulted in an increase in security concerns, particularly concerning evasion attacks.
Shyamala Ramachandran, P. D
semanticscholar   +1 more source

Secure Kernel Machines against Evasion Attacks

Proceedings of the 2016 ACM Workshop on Artificial Intelligence and Security, 2016
Machine learning is widely used in security-sensitive settings like spam and malware detection, although it has been shown that malicious data can be carefully modified at test time to evade detection. To overcome this limitation, adversary-aware learning algorithms have been developed, exploiting robust optimization and game-theoretical models to ...
RUSSU, PAOLO, DEMONTIS, AMBRA, BIGGIO, BATTISTA, FUMERA, GIORGIO, ROLI, FABIO   +4 more
openaire   +1 more source

An evasive attack on SNORT flowbits

2012 IEEE Network Operations and Management Symposium, 2012
The support of stateful signatures is an important feature of signature-based Network Intrusion Detection Systems (NIDSs) which permits the detection of multi-stage attacks. However, due to the difficulty to completely simulate every application protocol, several NIDS evasion techniques exploit this Achilles' heel, making the NIDS and its protected ...
null Tung Tran, I. Aib, E. Al-Shaer, R. Boutaba   +3 more
openaire   +1 more source

Guarding Deep Learning Systems With Boosted Evasion Attack Detection and Model Update

IEEE Internet of Things Journal
Deep learning systems are susceptible to evasion attacks, which represent a significant category of security vulnerabilities. These attacks entail the alteration of input data in such a way that the victim deep neural network (DNN) misclassifies it ...
Xiangru Chen, Dipal Halder, Kazi Mejbaul Islam, Sandip Ray   +3 more
semanticscholar   +1 more source