Cyber Threat Security System Using Artificial Intelligence for Android-Operated Mobile Devices
Main Article Content
Abstract
Malicious attacks on Android mobile devices have increased as smartphone usage has grown rapidly. The Android systems accommodate a variety of important approaches, like banking applications; hence, they become the target of malware that uses security system vulnerabilities. The cyber threat has grown exponentially over the past decade. Cybercriminals have become highly experienced. Current security regulators were insufficient to protect networks from an increasing number of highly skilled cybercriminals. The latest advances in Artificial Intelligence (AI) methods have led to a high level of innovation and automation. While the AI techniques provide important advantages, they could be utilized maliciously. The latest creation of cyber threats leverages modern AI (artificial intelligence)-aided techniques that are efficient for launching multi-level, potent, and potentially devastating attacks. Present cyber defence systems face different problems in protecting against recent and emerging risks. Hence, in this work, a cyber-threat security system using artificial intelligence for Android-operated mobile devices is presented. The machine learning (ML) and deep learning (DL) algorithms can conveniently identify threats on Android mobile devices
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
IJCERT Policy:
The published work presented in this paper is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. This means that the content of this paper can be shared, copied, and redistributed in any medium or format, as long as the original author is properly attributed. Additionally, any derivative works based on this paper must also be licensed under the same terms. This licensing agreement allows for broad dissemination and use of the work while maintaining the author's rights and recognition.
By submitting this paper to IJCERT, the author(s) agree to these licensing terms and confirm that the work is original and does not infringe on any third-party copyright or intellectual property rights.
References
Hasan Alkahtani and Theyazn H. H. Aldhyani, “Artificial Intelligence Algorithms for Malware Detection in Android-Operated Mobile Devices”, Sensors 2022, 22, 2268, doi.org/10.3390/s22062268
Daniel Kant, Andreas Johannsen, “Evaluation of AI-based use cases for enhancing the cyber security defense of small and medium-sized companies (SMEs)”, 2022, Society for Imaging Science and Technology, doi.org/10.2352/EI.2022.34.3.MOBMU-38
Zhimin Zhang, Huansheng Ning, Feifei Shi, Fadi Farha, Yang Xu, Jiabo Xu, Fan Zhang, Kim?Kwang Raymond Choo, “Artificial intelligence in cyber security: research advances, challenges, and opportunities”, 2021 Springer, doi.org/10.1007/s10462-021-09976-0
Muhammed AbuOdeh, Christian Adkins, Omid Setayeshfar, Prashant Doshi, Kyu H. Lee, “A Novel AI-based Methodology for Identifying Cyber Attacks in Honey Pots”, The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21), 2021, Association for the Advancement of Artificial Intelligence
Hooman Alavizadeh, Julian Jang-Jaccard, Tansu Alpcan and Seyit A. Camtepe, “A Markov Game Model for AI-based Cyber Security Attack Mitigation”, arXiv:2107.09258v1 [cs.GT] 20 Jul 2021
Thanh Cong Truong, Quoc Bao Diep and Ivan Zelinka, “Artificial Intelligence in the Cyber Domain: Offense and Defense”, Symmetry 2020, 12, 410; doi:10.3390/sym12030410
Jonghoon Lee, Jonghyun Kim, Ikkyun Kim, And Kijun Han, “Cyber Threat Detection based on Artificial Neural Networks using Event Profiles”, VOLUME 7, 2019, DOI 10.1109/ACCESS.2019.2953095, IEEE Access
A.M.S.N. Amarasinghe, W.A.C.H. Wijesinghe, D.L.A. Nirmana, Anuradha Jayakody, A.M.S. Priyankara, “AI Based Cyber Threats and Vulnerability Detection, Prevention and Prediction System”, 2019 International Conference on Advancements in Computing (ICAC), December 5-6, 2019. Malabe, Sri Lanka
Ozan Veranyurt, “Usage of Artificial Intelligence in DOS/DDOS Attack Detection”, International Journal of Basic and Clinical Studies (IJBCS) 2019; 8(1): 23-36, ISSN:2147-1428
Ricardo Calderon, “The Benefits of Artificial Intelligence in Cyber security”, Economic Crime Forensics Capstones, 2019, doi: digitalcommons.lasalle.edu/ecf_capstones
Vishal Dineshkumar Soni, “Role Of Artificial Intelligence in Combating Cyber Threats in Banking”, International Engineering Journal For Research & Development, 4(1), 7, 2019, doi.org/10.17605/OSF.IO/JYPGX
Nitika Khurana, Sudip Mittal, Aritran Piplai, Anupam Joshi, “Preventing Poisoning Attacks on AI Based Threat Intelligence Systems”, 2019 IEEE, 978-1-7281-0824-7/19
Gregory Falco, Arun Viswanathan, Carlos Caldera, And Howard Shrobe, “A Master Attack Methodology for an AI-Based Automated Attack Planner for Smart Cities”, 2018 IEEE ACCESS, doi:1 0.1109/ACCESS.2018.2867556
Amaan Anwar & Syed Imtiyaz Hassan, “Applying Artificial Intelligence Techniques to Prevent Cyber Assaults”, International Journal of Computational Intelligence Research ISSN 0973-1873 Volume 13, Number 5 (2017), pp. 883-889