Real-Time Network Intrusion Prevention System Based on Machine Learning)
Computer Science and Information Technology · 2025/2026
IntelliGuard, an Android application powered by a custom AI model designed for high-precision detection. The team initially trained a sophisticated model on over 11,000 real-world apps—both malicious and benign—achieving a 94% accuracy rate in distinguishing safe software from suspicious threats. The true technical achievement, however, lies in compressing this massive intelligence into a microscopic model of less than 400 KB that runs entirely on the device without internet access or data transmission to external servers, completing scans in under a tenth of a second. Unlike traditional antivirus tools that rely on static blacklists of known signatures, IntelliGuard identifies “Zero-Day” attacks by focusing on behavior rather than identity; it analyzes 229 different indicators to determine how an app acts, allowing it to catch brand-new threats unrecognized by any global database. Furthermore, the project overcomes a major academic hurdle by functioning on standard Android devices without requiring “Rooting,” meaning it provides seamless, background protection for the average user without compromising privacy or requiring dangerous system modifications.