DDoS attack resisting authentication protocol for mobile based online social network applications. (March 2022)

• Record Type: Journal Article
• Authors: Bhattacharya, Munmun; Roy, Sandip; Das, Ashok Kumar; Chattopadhyay, Samiran; Banerjee, Soumya; Mitra, Ankush
• Is Part Of: Journal of information security and applications. Volume 65(2022)
• Publication Date: 2022-03
• Subjects: Mobile online social networks -- DDoS attacks -- Authentication -- Key-refilling -- Machine learning -- NS3 simulation -- ProVerif simulation; Computer security -- Periodicals; Information technology -- Security measures -- Periodicals; 005.805
• DOI: 10.1016/j.jisa.2022.103115
• Languages: English
• ISSNs: 2214-2126

Abstract

Abstract: The rapid development of smartphone technology and the Internet services in mobile devices facilitates easy access to online social networking (OSN) sites anytime, anywhere. At the same time, this allures the adversaries to exploit the OSNs as a soft target for easy execution of various attacks that can quickly spread to a large number of users. In distributed denial-of-service (DDoS) attacks, an adversary aims to overwhelm the normal traffic of a targeted server with a flood of fake login messages so that the associated Internet service or website turns inoperable. In this paper, we propose a secure and lightweight authentication scheme ( P R D o S ) that resists DDoS and other security attacks in mobile OSN environments. We provide a multi-faceted solution towards the remedy of DDoS attacks in the OSN environment. After a certain threshold, the scheme discards further user login attempts and blocks an adversary who intends to overload the network server. We use the pre-loaded shadow identity and emergency key pairs, and a key-refilling strategy that rebuilds the essential synchronization between a blocked naive user and the OSN server. This technique restores the intended un-linkability property of the protocol. Using NS3 simulation, we study the impact of DDoS attackers on network throughput and network delay. Moreover, we validate and compare the proposed scheme against state-of-the-art solutions using the real attacks and benign datasets. We use the Canadian Institute for Cybersecurity (CIC) DoS dataset 2017, which is generated by capturing the normal and DoS attack packets separately with subsequent pre-processed for testing. We also use the machine learning (ML) algorithms, such as K-Nearest Neighbor (KNN), Gaussian Naive Bayes, and Multilayer Perceptron (MLP) to demonstrate the performance of the proposed solution in a practical attack detection scenario. We observe that these algorithms provide 97.05%, 95.48%, and 96.6% DDoS attack detection accuracy, respectively.