A survey on deep learning for cybersecurity: Progress, challenges, and opportunities

• Published in: Computer networks (Amsterdam, Netherlands : 1999) Vol. 212; p. 109032
• Main Authors: Macas, Mayra, Wu, Chunming, Fuertes, Walter
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 20-07-2022; Elsevier Sequoia S.A
• Subjects: Artificial intelligence; Artificial neural networks; Botnets; Cyber-threat; Cybersecurity; Deep learning; Encrypted traffic analysis; Internet; Intrusion detection; Machine learning; Security systems; Spam filtering; System effectiveness; Deep learning; Spam filtering; Botnets; Intrusion detection; Machine learning; Cybersecurity; Encrypted traffic analysis; Artificial intelligence; Cyber-threat
• ISSN: 1389-1286; 1872-7069
• DOI: 10.1016/j.comnet.2022.109032

As the number of Internet-connected systems rises, cyber analysts find it increasingly difficult to effectively monitor the produced volume of data, its velocity and diversity. Signature-based cybersecurity strategies are unlikely to achieve the required performance for detecting new attack vectors. Moreover, technological advances enable attackers to develop sophisticated attack strategies that can avoid detection by current security systems. As the cyber-threat landscape worsens, we need advanced tools and technologies to detect, investigate, and make quick decisions regarding emerging attacks and threats. Applications of artificial intelligence (AI) have the potential to analyze and automatically classify vast amounts of Internet traffic. AI-based solutions that automate the detection of attacks and tackle complex cybersecurity problems are gaining increasing attention. This paper comprehensively presents the promising applications of deep learning, a subfield of AI based on multiple layers of artificial neural networks, in a wide variety of security tasks. Before critically and comparatively surveying state-of-the-art solutions from the literature, we discuss the key characteristics of representative deep learning architectures employed in cybersecurity applications, we introduce the emerging trends in deep learning, and we provide an overview of necessary resources like a generic framework and suitable datasets. We identify the limitations of the reviewed works, and we bring forth a vision of the current challenges of the area, providing valuable insights and good practices for researchers and developers working on related problems. Finally, we uncover current pain points and outline directions for future research to address them.