Search Results for network-security
Abstract
Managing network infrastructure resilience under hostile conditions is a fundamental priority for modern smart university ecosystems. This paper introduces an advanced analytical framework to model and quantify the resilience of hybrid networks protected by Shamir Secret Sharing and Diffie-Hellman cryptographic protocols under intelligent targeted cyber-attacks. The network architecture incorporates a primary hybrid path operating under a 4-out-of-5 threshold logic alongside an independent fallback path managed by a backup componentB_6. A rigorous mathematical derivation using probability theory establishes the joint availability equations for both normal operation and targeted strategic strikes, where an attacker specifically aims to compromise vital edge components B_1andB_5. To validate the closed-form reliability expressions, a robust Monte Carlo simulation with 10^5 iterations was executed in MATLAB. The numerical findings demonstrate that while targeted attacks reduce the primary path's capacity to zero, the independent backup mechanism successfully preserves data-flow continuity and encryption integrity, maintaining a steady system availability floor of R_(sys.)≥ 0.90. These insights provide concrete engineering design criteria for implementing fault-tolerant secure data pathways in digital academic institutions.
Abstract
Smart universities constitute a complex environment in which thousands of Internet of Things (IoT) devices are interconnected, rendering them an increasingly attractive target for sophisticated cyberattacks. This study aims to design an integrated framework combining Artificial Intelligence (AI) and IoT technologies for the early detection of cyberattacks within smart university environments, employing machine learning and deep learning algorithms — namely Random Forest and a hybrid CNN-LSTM model — to classify network traffic and detect anomalous patterns in real time. The study adopted a descriptive-analytical approach supported by a field questionnaire administered to a sample of 150 participants from IT and network security personnel across universities. Results revealed excellent questionnaire reliability (Cronbach's Alpha = 0.927) and a strong positive correlation (r = 0.674) between cybersecurity awareness and AI detection effectiveness, alongside the superiority of the proposed model over traditional systems by an accuracy margin of approximately 25 percentage points (96.3% vs. 71.2%). The study recommends adopting a five-layer architectural framework integrating IoT devices, data processing, AI inference, attack detection, and real-time response layers as a viable model for deployment in Arab smart universities.