Dynamic analysis of malwarae intrusion in mobile devices using Adaboost Algorithm, KNN and SVM base classifiers

Abstract

Cyber security is becoming more worrisome; malware is spreading by the day through proliferation and distribution of variants of known family signatures using obfuscation techniques. Mobile devices components such as central processing unit, memory, battery life, executable files and operating systems are constantly being attacked and  rendered unusable. Attack agents are specifically evading detection, damaging mobile devices’ executive files, stealing information, surcharging users for SMS sent and received without their knowledge or permission, and freezing applications for a ransom among others. This research work is keying into the fight against malware intrusion by designing and developing an intrusion detection system (IDS) using ensemble learning, boosting. Adaboost algorithm trains base classifiers (KNN and SVM) using network security laboratory-knowledge discovery in databases (NSL-KDD) dataset to build a more formidable classifier that will detect malware intrusion in mobile devices using cloud technology. The result obtained in this combination technique is 91.4% accurate with a bias (standard deviation) as low as 2.7%.