3,859 research outputs found
IDSGAN: Generative Adversarial Networks for Attack Generation against Intrusion Detection
As an important tool in security, the intrusion detection system bears the
responsibility of the defense to network attacks performed by malicious
traffic. Nowadays, with the help of machine learning algorithms, the intrusion
detection system develops rapidly. However, the robustness of this system is
questionable when it faces the adversarial attacks. To improve the detection
system, more potential attack approaches should be researched. In this paper, a
framework of the generative adversarial networks, IDSGAN, is proposed to
generate the adversarial attacks, which can deceive and evade the intrusion
detection system. Considering that the internal structure of the detection
system is unknown to attackers, adversarial attack examples perform the
black-box attacks against the detection system. IDSGAN leverages a generator to
transform original malicious traffic into adversarial malicious traffic. A
discriminator classifies traffic examples and simulates the black-box detection
system. More significantly, we only modify part of the attacks' nonfunctional
features to guarantee the validity of the intrusion. Based on the dataset
NSL-KDD, the feasibility of the model is demonstrated to attack many detection
systems with different attacks and the excellent results are achieved.
Moreover, the robustness of IDSGAN is verified by changing the amount of the
unmodified features.Comment: 8 pages, 5 figure
Artificial intelligence in the cyber domain: Offense and defense
Artificial intelligence techniques have grown rapidly in recent years, and their applications in practice can be seen in many fields, ranging from facial recognition to image analysis. In the cybersecurity domain, AI-based techniques can provide better cyber defense tools and help adversaries improve methods of attack. However, malicious actors are aware of the new prospects too and will probably attempt to use them for nefarious purposes. This survey paper aims at providing an overview of how artificial intelligence can be used in the context of cybersecurity in both offense and defense.Web of Science123art. no. 41
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