An Adversarial Examples against Deep Learning-Based Network Intrusion Detection System: A Review

Ahmad Abubakar Yunusa, Fatima Umar Zambuk, Badamasi Imam Ya'u, Hamza Hussaini, Ismail Haruna

Abstract


Security holds significant importance in our daily lives, as any compromise in confidentiality can pose a serious threat from criminals. Cybercriminals constantly seek ways to exploit vulnerabilities and gather data for their own gain, particularly exploiting the known vulnerability of deep learning algorithms to adversarial examples. To protect computers and networks from potential attacks by hackers, who may attempt to steal or manipulate sensitive information stored in databases, cyber security specialists and designers are required to develop various intrusion detection systems. In this research paper, we conduct a survey on existing studies that explore the efficacy of adversarial examples in the domain of network intrusion detection systems, with the goal of devising defensive measures. Our review reveals that multiple attack methods have been investigated, demonstrating their effectiveness in detecting intrusions, and highlighting the vulnerability of deep neural networks to such attack strategies.



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