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A novel malware detection system based on machine learning and binary visualization

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Communications Workshops, ICC Workshops 2019 - Proceedings
PublisherIEEE
Number of pages6
ISBN (Electronic)978-1-7281-2373-8, 978-1-7281-2372-1
ISBN (Print)978-1-7281-2374-5
DOIs
Publication statusPublished - 11 Jul 2019
Event2019 IEEE International Conference on Communications Workshops - Shanghai, China
Duration: 20 May 201924 May 2019

Publication series

Name2019 IEEE International Conference on Communications Workshops, ICC Workshops 2019 - Proceedings
PublisherIEEE
ISSN (Print)2474-9133
ISSN (Electronic)2474-9133

Workshop

Workshop2019 IEEE International Conference on Communications Workshops
CountryChina
CityShanghai
Period20/05/1924/05/19
OtherICC Workshops 2019

Documents

  • 1904.00859

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    Accepted author manuscript (Post-print), 1.58 MB, PDF document

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