TY - GEN
T1 - A novel malware detection system based on machine learning and binary visualization
AU - Baptista, Irina
AU - Shiaeles, Stavros
AU - Kolokotronis, Nicholas
PY - 2019/7/11
Y1 - 2019/7/11
N2 - The continued evolution and diversity of malware constitutes a major threat in modern systems. It is well proven that security defenses currently available are ineffective to mitigate the skills and imagination of cyber-criminals necessitating the development of novel solutions. Deep learning algorithms and artificial intelligence (AI) are rapidly evolving with remarkable results in many application areas. Following the advances of AI and recognizing the need for efficient malware detection methods, this paper presents a new approach for malware detection based on binary visualization and self-organizing incremental neural networks. The proposed method's performance in detecting malicious payloads in various file types was investigated and the experimental results showed that a detection accuracy of 91.7% and 94.1% was achieved for ransomware in.pdf and.doc files respectively. With respect to other formats of malicious code and other file types, including binaries, the proposed method behaved well with an incremental detection rate that allows efficiently detecting unknown malware at real-time.
AB - The continued evolution and diversity of malware constitutes a major threat in modern systems. It is well proven that security defenses currently available are ineffective to mitigate the skills and imagination of cyber-criminals necessitating the development of novel solutions. Deep learning algorithms and artificial intelligence (AI) are rapidly evolving with remarkable results in many application areas. Following the advances of AI and recognizing the need for efficient malware detection methods, this paper presents a new approach for malware detection based on binary visualization and self-organizing incremental neural networks. The proposed method's performance in detecting malicious payloads in various file types was investigated and the experimental results showed that a detection accuracy of 91.7% and 94.1% was achieved for ransomware in.pdf and.doc files respectively. With respect to other formats of malicious code and other file types, including binaries, the proposed method behaved well with an incremental detection rate that allows efficiently detecting unknown malware at real-time.
KW - Binary visualisation
KW - Machine learning
KW - Malicious software
KW - Security
KW - Self-organizing neural networks
UR - http://www.scopus.com/inward/record.url?scp=85070336785&partnerID=8YFLogxK
UR - https://icc2019.ieee-icc.org/authors/call-submissions
UR - https://pearl.plymouth.ac.uk/
U2 - 10.1109/ICCW.2019.8757060
DO - 10.1109/ICCW.2019.8757060
M3 - Conference contribution
SN - 978-1-7281-2374-5
T3 - 2019 IEEE International Conference on Communications Workshops, ICC Workshops 2019 - Proceedings
BT - 2019 IEEE International Conference on Communications Workshops, ICC Workshops 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Communications Workshops
Y2 - 20 May 2019 through 24 May 2019
ER -