TY - GEN
T1 - AI Techniques for Software Vulnerability Detection and Mitigation
AU - Khater, Heba M.
AU - Khayat, Mohamad
AU - Alrabaee, Saed
AU - Serhani, Mohamed
AU - Barka, Ezedin
AU - Sallabi, Farag
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The growth of the Internet of Things (IoT) is leading to some restructuring and transformation of everyday lives. The number and diversity of IoT devices have increased rapidly, enabling the vision of a smarter environment and opening the door to further automation, accompanied by the generation and collection of enormous amounts of data. The automation and ongoing proliferation of personal and professional data in the IoT have resulted in countless cyber-Attacks enabled by the growing security vulnerabilities of IoT devices. Therefore, it is crucial to detect and patch vulnerabilities before attacks happen in order to secure IoT environments. One of the most promising approaches for combating cybersecurity vulnerabilities and ensuring security is through the use of artificial intelligence (AI). In this paper, we provide a review in which we classify, map, and summarize the available literature on AI techniques used to recognize and reduce cybersecurity software vulnerabilities in the IoT. We present a thorough analysis of the majority of AI trends in cybersecurity, as well as cutting-edge solutions.
AB - The growth of the Internet of Things (IoT) is leading to some restructuring and transformation of everyday lives. The number and diversity of IoT devices have increased rapidly, enabling the vision of a smarter environment and opening the door to further automation, accompanied by the generation and collection of enormous amounts of data. The automation and ongoing proliferation of personal and professional data in the IoT have resulted in countless cyber-Attacks enabled by the growing security vulnerabilities of IoT devices. Therefore, it is crucial to detect and patch vulnerabilities before attacks happen in order to secure IoT environments. One of the most promising approaches for combating cybersecurity vulnerabilities and ensuring security is through the use of artificial intelligence (AI). In this paper, we provide a review in which we classify, map, and summarize the available literature on AI techniques used to recognize and reduce cybersecurity software vulnerabilities in the IoT. We present a thorough analysis of the majority of AI trends in cybersecurity, as well as cutting-edge solutions.
KW - Artificial Intelligence (AI)
KW - Internet of Things (IoT)
KW - Vulnerability Detection
UR - http://www.scopus.com/inward/record.url?scp=85182272405&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85182272405&partnerID=8YFLogxK
U2 - 10.1109/DSC61021.2023.10354233
DO - 10.1109/DSC61021.2023.10354233
M3 - Conference contribution
AN - SCOPUS:85182272405
T3 - Proceedings - 2023 IEEE Conference on Dependable and Secure Computing, DSC 2023
BT - Proceedings - 2023 IEEE Conference on Dependable and Secure Computing, DSC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th IEEE Conference on Dependable and Secure Computing, DSC 2023
Y2 - 7 November 2023 through 9 November 2023
ER -