PERFORMANCE ANALYSIS OF MOBILE AD HOC NETWORK FOR DYNAMIC MALWARE DETECTION USING MACHINE LEARNING
DOI:
https://doi.org/10.29121/shodhkosh.v5.i6.2024.4606Keywords:
Malware Attack, Security, Machine Learning, AODV, Decision TreeAbstract [English]
Mobile Ad Hoc Networks (MANETs) are decentralized and self-configuring networks that facilitate communication without fixed infrastructure. However, their open and dynamic nature makes them highly vulnerable to various security threats, particularly malware attacks. Traditional signature-based malware detection systems are ineffective in such environments due to their inability to detect novel and evolving threats. This paper explores the advancements in machine learning (ML) techniques for dynamic malware detection in MANETs. We analyze various ML-based approaches, including supervised, unsupervised, and deep learning models, that enhance the accuracy and efficiency of threat detection. Furthermore, we discuss feature selection techniques, behavioral analysis, and anomaly detection methods that improve malware identification in real time. The integration of AI-driven malware detection solutions in MANETs significantly enhances their resilience against sophisticated attacks. The study concludes by highlighting the challenges and future research directions in developing adaptive and intelligent malware detection systems for secure mobile communication.
References
Gaurav Soni, Kamlesh Chandravanshi, ‘”A Nobel Defence Scheme Against Selfish Node Attack in MANET”, International Journal on Computational Science & Applications(IJCSA) 2013 ISSN: 2200-0011 is a AIRCC journal, DOI: https://doi.org/10.5121/ijcsa.2013.3305
SafaAltaha1, Khaled Riad2 (2024),"Machine Learning in Malware Analysis: Current Trends and Future Directions".".(IJACSA) Vol. 15, No. 1, 2024 DOI: https://doi.org/10.14569/IJACSA.2024.01501124
MohdAzahariMohd Yusof1 , Zubaile Abdullah2 , Firkhan Ali Hamid Ali3 , Khairul Amin Mohamad Sukri4 , Hanizan Shaker Hussain5(2023),"Detecting Malware with Classification Machine Learning Techniques".(IJACSA) Vol. 14, No. 6, 2023 DOI: https://doi.org/10.14569/IJACSA.2023.0140619
Hoda El Merabet1,AbderrahmaneHajraoui2 (2019), "A Survey of Malware Detection Techniques based on Machine Learning." (IJACSA) Vol. 10, No. 1, 2019 DOI: https://doi.org/10.14569/IJACSA.2019.0100148
Binayak Panda1, Dr. Satya Narayan Tripathy2. (2020),"Detection of Anomalous In-Memory Process based on DLL Sequence ".(IJACSA) Vol. 11, No. 10, 2020 DOI: https://doi.org/10.14569/IJACSA.2020.0111025
Soni, G., Chandravanshi, K., Jhariya, M.K., Rajput, A. (2022). An IPS Approach to Secure V-RSU Communication from Blackhole and Wormhole Attacks in VANET. In: Sarma, H.K.D., Balas, V.E., Bhuyan, B., Dutta, N. (eds) Contemporary Issues in Communication, Cloud and Big Data Analytics. Lecture Notes in Networks and Systems, vol 281. Springer DOI: https://doi.org/10.1007/978-981-16-4244-9_5
Lai Van Duong1, Cho Do Xuan2 (2021),"Detecting Malware based on Analyzing Abnormal behaviors of PE File ".(IJACSA) Vol. 12, No. 3, 2021 DOI: https://doi.org/10.14569/IJACSA.2021.0120355
Abhishek Kumar Pandey1,Fawaz Alsolami2 *(2020), "Malware Analysis in Web Application Security: An Investigation and Suggestion".(IJACSA) Vol. 11, No. 7, 2020 DOI: https://doi.org/10.14569/IJACSA.2020.0110725
G. Soni and K. Chandravanshi, "Security Scheme to Identify Malicious Maneuver of Flooding Attack for WSN in 6G," 2021 8th International Conference on Signal Processing and Integrated Networks (SPIN), Noida, India, 2021, pp. 124-129 DOI: https://doi.org/10.1109/SPIN52536.2021.9566066
TalalA.A Abdullah1, Waleed Ali2, Rawad Abdulghafor3 (2020)," Empirical Study on Intelligent Android Malware Detection based on Supervised Machine Learning". (IJACSA) Vol. 11, No. 4, 2020 DOI: https://doi.org/10.14569/IJACSA.2020.0110429
NurAdibahRosli1,WarusiaYassin2,FaizalM.A3,SitiRahayu Selamat4. (2019)," Clustering Analysis for Malware Behavior Detection using Registry Data".(IJACSA) Vol. 10, No. 12, 2019 DOI: https://doi.org/10.14569/IJACSA.2019.0101213
ABM.Adnan Azmee1, PrantoProtim Choudhury2, Md. Aosaful Alam3, Orko Dutta4, Muhammad Iqbal Hossain5. (2020)," Performance Analysis of Machine Learning Classifiers for Detecting PE Malware".(IJACSA) Vol. 11, No. 1, 2020 DOI: https://doi.org/10.14569/IJACSA.2020.0110163
Houria MADANI1, Noura OUERDI2, Abdelmalek Azizi3 (2023) "Ransomware: Analysis of Encrypted Files."(IJACSA) Vol. 14, No. 1, 2023 DOI: https://doi.org/10.14569/IJACSA.2023.0140124
Kebede, S.D., Tiwari, B., Tiwari, V. et al. Predictive machine learning-based integrated approach for DDoS detection and prevention. Multimed Tools Appl 81, 4185–4211 (2022). DOI: https://doi.org/10.1007/s11042-021-11740-z
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