ScanSavant: Malware Detection for Android Applications with Explainable AI - S., Navaneethan; S., Udhaya Kumar (International Journal of Interactive Mobile Technologies (iJIM), 2024)
Mobile devices face SQL injection, malware, and web-based threats. Current solutions lack real-time detection. This paper introduces an Android app with advanced algorithms for real-time threat scanning. During testing, our application detected 94% of SQL injection attempts, outperforming the 86% average detection rate in similar studies. For malware analysis, it achieved a 97% detection accuracy on a dataset of infected files, higher than the industry standard of 93%. Additionally, our app can detect 85 malware variants and assign 15 attributes (Trojan.Gen.8, Worm.Autorun, Adware.Elex, Spyware.Zbot, Ransom.Cryptolocker, Rootkit.ZeroAccess, Exploit.CVE-2017-0143, Virus.MSIL.CoinMiner, Trojan.Emotet, Backdoor. DarkComet, PUP.Optional.Conduit, Adware.MyWebSearch, Virus.Win32.Sality, Trojan.Win32. Necurs, and Ransom.WannaCry) to some malwares, providing detailed analysis for better threat management. The application effectively scans both EXE and APK files, ensuring comprehensive protection. When assessing website links, the application identified security risks with 96% accuracy, demonstrating its capability in managing web-based threats. This app detects SQL injections, analyses malware, and assesses website security, bolstering cyber defence with user-friendly features and top-notch threat mitigation.