Enhancing AI Malware Detection Using Neural Network with Binary Data Analysis

DOI

10.2991/978-94-6463-589-8_7How to use a DOI?

Keywords

Neural – Network; Anti Malware; Malware Detection; Threats

Abstract

Recent advancements in the realm of software have brought forth evolving risks that impact both individuals and organizations. The emergence of AI powered malware with its growing stealth, adaptability and harmful capabilities presents obstacles to cybersecurity defenses. This type of malware is adept at changing its behavior imitating actions and slipping past antivirus tools unnoticed. Ongoing efforts in malware research are centered on harnessing AI technologies to bolster the detection and categorization of malware especially when dealing with novel or unfamiliar threats. Alongside these strategies are being developed to fortify defenses against cyber-attacks driven by AI emphasizing the importance of enhancing the security and resilience of AI systems against threats. Tackling the challenges posed by AI driven malware necessitates exploration and innovative approaches to enhance the detection, prevention and overall security of AI systems. Our research introduces a technique for improving the detection of AI driven malware using networks coupled with binary data analysis. By utilizing AI technology, this technique aims to enhance detection accuracy and efficiency. Unlike dynamic analyses, binary analysis enables a comprehensive examination by leveraging all information from binary data while maintaining vital details through compression. The technique involves gathering data, developing models and conducting testing to assess the performance of the malware system. This technique tackles the drawbacks of antivirus programs by combining cutting edge networks with binary data examination. The results showcase the efficiency of this method in identifying and categorizing software, underscoring its ability to bolster cybersecurity measures, against cyber threats.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Muhammad Sufi Afifi Abdul Sakti
AU  - Mohsen Mohamad Hata
AU  - Zulaikha Hanan Bolhan
AU  - Mohamad Yusof Darus
PY  - 2024
DA  - 2024/12/01
TI  - Enhancing AI Malware Detection Using Neural Network with Binary Data Analysis
BT  - Proceedings of the International Conference on Innovation & Entrepreneurship in Computing, Engineering & Science Education (InvENT 2024)
PB  - Atlantis Press
SP  - 53
EP  - 63
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-589-8_7
DO  - 10.2991/978-94-6463-589-8_7
ID  - Sakti2024
ER  -