MOBILE-BASED SOCIAL NETWORKS ENDPOINT SECURITY ENHANCING MODEL

Abstract

The pervasive integration of mobile devices and social networking has created a critical security paradigm where the endpoint device has become the primary target for cyber threats. This study addresses the fundamental disconnect between the value of data on mobile devices and the inadequacy of security models focused predominantly on server and network protection. The research was guided by the objective to establish emerging security trends and develop an Endpoint Security Enhancing Model for Mobile-based Social Networks (MbSNs). Employing a pragmatic, mixed-methods approach, the study integrated a quantitative survey of 257 users with qualitative vulnerability simulations of major platforms—WhatsApp, Facebook, and X (formerly Twitter)—using the Mobile Security Framework (MobSF) and GitHub Suite for penetration testing. The investigation yielded several critical findings. The survey revealed a significant "awareness-action gap" among users, who demonstrated knowledge of threats like phishing but exhibited poor security hygiene, with 19.1% using no device password and 84.8% not using a VPN. Crucially, statistical analysis (correlation and linear regression) showed no significant relationship between specific mobile threats and the choice of social networking application (R² as low as 1.4%), indicating that vulnerability is universal across platforms. The simulation results provided empirical validation, uncovering high-severity vulnerabilities (CVSS scores 7.0 8.1) rooted not in broken encryption, but in systemic design flaws, including dangerous permission misuse (such as RECORD_AUDIO, CAMERA), buffer overflows, and insecure software components like exported broadcast receivers, which create direct data leakage pathways. In response to these findings, the study designed and implemented the Mobile based Authentication Technique (MbAT), a novel, layered security model built on a Defense-in-Depth principle. The model anchors its security to a hardware root of trust—the SIM card—and employs a dual-layer encryption strategy. It leverages the robust Signal Protocol for end-to-end encrypted data-in-transit, ensuring forward secrecy and post compromise security, while utilizing the lightweight Blowfish algorithm for efficient encryption of data-at-rest on the endpoint device. A critical innovation of MbAT is its secure handling of the XML-JSON transformation layer, a necessary interoperability feature, which is compartmentalized and "sandwiched" between robust SIM-based authentication and cryptographic operations to mitigate associated injection and parsing threats. In conclusion, this research successfully re-frames mobile social network security as a socio-technical challenge, demonstrating that the threat landscape is uniform across applications and rooted in the interplay of user behavior, permission models, and architectural flaws. The proposed MbAT model offers a holistic, proof-of-concept solution that transforms the endpoint from the weakest link into a verifiable component of the security architecture. By providing a scalable, user-aware framework that integrates a hardware-anchored root of trust with state of-the-art cryptographic protocols, this study lays a foundational blueprint for achieving a more secure and privacy-respecting future for mobile social networking.