Machine Learning for Improving the Security of Mobile Devices against Spyware: The Case of Pegasus

Patrick Dany Bavoua Kenfack, Alphonse Binele Abana, Emmanuel Tonye, Armel Brice Djousse Nguegang

doi:https://doi.org/10.59232/CYS-V1I1P101

Research Article | Open Access | Download Full Text

Volume 1 | Issue 1 | Year 2023 | Article Id: CYS-V1I1P101 DOI: https://doi.org/10.59232/CYS-V1I1P101

Machine Learning for Improving the Security of Mobile Devices against Spyware: The Case of Pegasus

Patrick Dany Bavoua Kenfack, Alphonse Binele Abana, Emmanuel Tonye, Armel Brice Djousse Nguegang

Received	Revised	Accepted	Published
15 May 2023	22 May 2023	04 Jun 2023	05 Jul 2023

Citation

Patrick Dany Bavoua Kenfack, Alphonse Binele Abana, Emmanuel Tonye, Armel Brice Djousse Nguegang. “Machine Learning for Improving the Security of Mobile Devices against Spyware: The Case of Pegasus.” DS Journal of Cyber Security, vol. 1, no. 1, pp. 1-18, 2023.

Abstract

Given the different approaches developed in the literature to guarantee the confidentiality of user data in smartphones, and the attack approach which is currently associated with the contemporary Spyware giant Pegasus, we are carrying out in this work a reflection on the development of a protection tool based on Machine learning. Focused on 0-day and even 0-click type approaches, the behaviour of Pegasus leads us to choose to carry out an IDS based on detection by behavioural analysis. With the challenges of considering the reduced performance of critical resources of Android phones, the proposed system uses the auto-encoder algorithm, applied to a network of multilayer and back-propagation perceptrons. For the Construction of the model, the search for a robust structure is done with the help of the KDD+aggregate dataset, and the Construction of the model with the IA-AE-IDS set that we have processed for the occasion. The model obtained with precision and a recall of at least 98% is then deployed on the open-source network analysis application PCAPdroid, mainly in the form of a service that the user can start and stop at will.

Keywords

Spyware, Machine learning, Pegasus, Auto encoder, Cyber security

References

[1] P. Agarwal et al., “Malware Analysis in Mobile Devices,” 7^th International Conference on Computing in Engineering & Technology (ICCET 2022), India, pp. 128 – 134, 2022.

[Cross Ref] [Google Scholar] [Publisher Link]

[2] Nataliya ZAGORODNA et al., "Network Attack Detection Using Machine Learning Methods," Challenges to National Defence in Contemporary Geopolitical Situation, vol. 2022, no. 1, pp. 55–61, 2022.

[CrossRef] [Google Scholar] [Publisher Link].

[3] D.P. Gaikwad, Vismita Nagrale, and M.P. Bauskar, “Ensemble of Learner for Network Intrusion Detection System,” Journal of Network Security Computer Networks, vol. 9, no. 1, 2023.

[CrossRef] [Publisher Link ]

[4] Emad Ul Haq Qazi, Muhammad Hamza Faheem, and Tanveer Zia, "HDLNIDS: Hybrid Deep-Learning-Based Network Intrusion Detection System," Applied Sciences, vol. 13, no. 8, pp. 1-16, 2023.

[CrossRef] [Google Scholar] [Publisher Link ]

[5] A Kalaivani, and R Pugazendi, "A Review on Intrusion Detection System and Its Techniques," Data Analytics and Artificial Intelligence, vol. 3, no. 2, pp. 132-137, 2023.

[CrossRef] [Publisher Link ]

[6] Maroua Ben Attia et al., "On-Device Anomaly Detection for Resource-Limited Systems," 30^th Annual ACM Symposium on Applied Computing, pp. 548-554, 2015.

[CrossRef] [Google Scholar] [Publisher Link ]

[7] Frédéric Majorczyk , "Détection D’intrusions Comportementales Par Diversification De COTS : Application Au Cas Des Serveurs Web Informatique," HAL Theses, Université Rennes, 2008.

[Google Scholar] [Publisher Link ]

[8] Maurras Ulbricht Togbe et al., "Etude Comparative Des Méthodes De Détection D’anomalies," Revue Des Nouvelles Technologies De l’Information, 2020.

[Google Scholar] [Publisher Link ]

[9] Safana Abbas, Wedad Abdul Khuder Naser, and Amal Abbas Kadhim, "Subject Review: Intrusion Detection System (IDS) and Intrusion Prevention System (IPS)," Global Journal of Engineering and Technology Advances, vol. 14, no. 2, pp. 155-158, 2023.

[CrossRef] [Publisher Link ]

[10] Hongmin Wang, Qiang Wei, and Yaobin Xie, "A Novel Method for Network Intrusion Detection," Scientific Programming, vol. 2022, pp.1-13, 2022.

[CrossRef] [Google Scholar] [Publisher Link ]

[11] Kunpeng Wang, Jingmei Li, and Weifei Wu, "Intrusion Detection Algorithm Based on Transfer Extreme Learning Machine," Intelligent Data Analysis, vol. 27, no. 2, pp. 463-482, 2023.

[CrossRef] [Google Scholar] [Publisher Link ]

[12] Sihong Lin et al., "An Intrusion Detection Method Based on Granular Autoencoders," Journal of Intelligent and Fuzzy Systems, 2023.

[Google Scholar] [Publisher Link ]

[13] Anita Shiravani et al., "Network Intrusion Detection Using Data Dimensions Reduction Techniques,", Journal of Big Data, vol. 10, no. 27, 2023.

[CrossRef] [Google Scholar] [Publisher Link ]

[14] Hong Qian et al., "A Novel Cyber Intrusion Detection Model Based on Improved Hybrid Sampling," Transactions of the Institute of Measurement and Control, 2023.

[CrossRef] [Google Scholar] [Publisher Link ]

[15] T.P Deshmukh et al., "Smart Intrusion Detection in Industrial Devices Using Deep Belief Network," International Journal of Scientific Research in Engineering and Management, vol. 5, no. 4, pp. 2318-2323, 2023.

[CrossRef] [Publisher Link ]

[16] Nihar Mudigonda, "A Method for Network Intrusion Detection Using Deep Learning," Journal of Student Research, vol. 11, no. 3, 2022.

[CrossRef] [Google Scholar] [Publisher Link ]

[17] Reza Ghanbarzadeh, Ali Hosseinalipour, and Ali Ghaffaria, "Novel Network Intrusion Detection Method Based on Metaheuristic Optimization Algorithms," Journal of Ambient Intelligence and Humanized Computing, 2023.

[CrossRef] [Google Scholar] [Publisher Link ]

[18] Ali Muhammad, Iqbal Murtza, Ayesha Saadia, and Kashif Kifayat, "Cortex-Inspired Ensemble Based Network Intrusion Detection System," Neural Computing and Applications, 2023.

[CrossRef] [Google Scholar] [Publisher Link ]

[19] Smrutirekha Panda, "Cyber Intrusion Detection, Prevention, and Future IT Strategy," International Research Journal of Engineering and Technology, vol. 9, 2022.

[Google Scholar] [Publisher Link ]

[20] Ngamba Thockchom, Moirangthem Marjit Singh, and Utpal Nandi, "A Novel Ensemble Learning-Based Model for Network Intrusion Detection," Complex & Intelligent Systems, 2023.

[CrossRef] [Google Scholar] [Publisher Link ]

[21] Hyojoon Han, Hyukho Kim, and Yangwoo Kim, "Correlation Between Deep Neural Network Hidden Layer and Intrusion Detection Performance in IoT Intrusion Detection System," Symmetry, vol. 14, no. 10, pp. 1-19, 2022.

[CrossRef] [Google Scholar] [Publisher Link ]

[22] Taehoon Kim, and Wooguil Pak, "Deep Learning-Based Network Intrusion Detection Using Multiple Image Transformers," Applied Sciences, vol. 13, no. 5, pp. 1-15, 2023.

[CrossRef] [Google Scholar] [Publisher Link ]

[23] Alaa Mohammed Banaamah, Iftikhar Ahmad, "Intrusion Detection in Iot Using Deep Learning," Sensors, vol. 22, no. 21, pp. 1-12, 2022.

[CrossRef] [Google Scholar] [Publisher Link ]

[24] D. Kamalakkannan et al., "A Detection of Intrusions Based on Deep Learning," Cybernetics and Systems, 2023.

[CrossRef] [Google Scholar] [Publisher Link ]

[25] Nisha T.N. Dhanya Pramod, "Insider Intrusion Detection Techniques: A State-of-the-Art Review," Journal of Computer Information Systems, 2023.

[CrossRef] [Google Scholar] [Publisher Link ]