2023/03/13 by Islam Debicha, Debicha, Islam, Benjamin Cochez +9 · 4 citations
Computer Science · #Advanced Malware Detection Techniques #Adversarial Robustness in Machine Learning #Adversarial system #Artificial Intelligence (cs.AI) #Artificial intelligence #Computer science #Computer security #Cryptography and Security (cs.CR) #Data science #Domain (mathematical analysis) #Evasion (ethics) #FOS: Computer and information sciences #Field (mathematics) #Intrusion detection system #Machine learning #Network Security and Intrusion Detection #Risk analysis (engineering) #Vulnerability (computing)
paper · pdf · doi:10.48550/arxiv.2303.07003
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2023/03/13 · openalex created_date 2023/03/16 · openalex updated_date 2026/07/28
Nowadays, numerous applications incorporate machine learning (ML) algorithms due to their prominent achievements. However, many studies in the field of computer vision have shown that ML can be fooled by intentionally crafted instances, called adversarial examples. These adversarial examples take advantage of the intrinsic vulnerability of ML models. Recent research raises many concerns in the cybersecurity field. An increasing number of researchers are studying the feasibility of such attacks on security systems based on ML algorithms, such as Intrusion Detection Systems (IDS). The feasibility of such adversarial attacks would be influenced by various domain-specific constraints. This can potentially increase the difficulty of crafting adversarial examples. Despite the considerable amount of research that has been done in this area, much of it focuses on showing that it is possible to fool a model using features extracted from the raw data but does not address the practical side, i.e., the reverse transformation from theory to practice. For this reason, we propose a review browsing through various important papers to provide a comprehensive analysis. Our analysis highlights some challenges that have not been addressed in the reviewed papers.