Sunday 21st of January 2018

SwarmDroid: Swarm Optimized Intrusion Detection System for the Android Mobile Enterprise

Abimbola Adebisi Adigun, Temitayo Matthew Fagbola and Adekanmi Adegun

The inadequacies inherent in the current defense mechanism of the mobile enterprise led to the development of new breed of security systems known as mobile intrusion detection system. The major worry of mobile / ubiquitous device users is the issue of data security since no mobile security application is 100% efficient. Existing studies conducted on android mobile security reveal that Android is the platform with the highest malware growth rate by the end of 2011 and that Global System for Mobile Communication -based Pivot Attacks, Mobile Botnets and Malicious Applications are the major security vulnerabilities compromising the confidentiality, integrity and availability of this mobile enterprise. In this paper, a SwarmDroid IDS is developed following a machine learning approach using Support Vector Machine. NSL-KDD dataset was used to test and evaluate the performance of the SwarmDroid IDS and compared with J48 and Random Forest which are state-of-the-art machine learning techniques for intrusion detection in mobiles. Particle Swarm Optimization was used for feature selection. The malware detection systems were simulated in a MATLAB environment. The SwarmDroid IDS was evaluated using detection time, true positive rate, false positive rate and detection accuracy as performance metrics. The result obtained from the evaluation revealed that SwarmDroid IDS outperforms J48 in terms of detection time and accuracy. Also, feature selection in Android application package files using particle swarm optimization technique plays a critical role in realizing high accuracy and low computational time complexity in SwarmDroid.

Keywords: SwarmDroid, Android, Random Forest, J48.

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Abimbola Adebisi Adigun
Department of Computer Science and Engineering, Ladoke Akintola University of Technology, Ogbomoso, Oyo State, Nigeria

Temitayo Matthew Fagbola
Fagbola Temitayo Matthew is a lecturer and researcher at the Department of Computer Science, Federal University, Oye-Ekiti, Ekiti State, Nigeria. He bagged B.Tech and M.Sc degrees in Computer Science from Ladoke Akintola University of Technology, Ogbomoso, Nigeria and University of Ibadan, Nigeria respectively and currently on his Ph.D programme in the Department of Computer Science & Engineering, Ladoke Akintola University of Technology, Ogbomoso, Nigeria. He has presented papers in conferences and published both locally and internationally in the areas of health informatics, data mining, cybersecurity, soft computing as well as on web and mobile apps design and development. In 2011, he published a book on the fundamentals of computing and information technology. His current research interests are in the area of multimedia cloud computing, social media computing, ICT in health and education, video-based face recognition and resolution reconstruction. He can be reached through The mobile is +234-703-0513-010

Adekanmi Adegun
Department of Computer Science, Landmark University, Omu-Aran, Kwara State, Nigeria

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