SMS-Based Mobile Botnet Detection Framework Using Intelligent Agents

Abstract

Along with increasing security measures in Android platforms, the amount of Android malware that use remote exploits has grown significantly. Using mobile botnets, attackers concentrate on reliable attack vectors such as SMS messages. Short Message Service (SMS) has been increasingly targeted by a number of malicious applications (“apps”) that have the ability to abuse SMS features in order to send spam, to transfer command and control (C&C) instructions, to distribute malicious applications via URLs embedded in text messages, to send text messages to premium-rate numbers, and to exploit smartphones. In this paper, we propose an SMS-based botnet detection formwork that uses multi-agent technology based on observations of SMS and Android smartphone features. This formwork has the ability to detect SMS botnets and identify ways to block the attacks in order to prevent damage caused by botnet attacks. We developed an adaptive hybrid model of SMS botnet detectors by using a combination of signature-based and anomaly-based algorithms. These components utilize multi-agent technology to recognize malicious SMS and prevent users from opening these messages that infecting smartphones. This framework includes defence module that employed a more proactive approach that allows us to directly generate signatures and rules that can be used to protect Android smartphones from abuse by SMS botnets. The framework creates a user profile that is used to perform behavioural profiling analysis in order to identity malicious SMS and cut the C&C Channel.