As a result of the rapid advancement of mobile and internet technology, a plethora of new mobile security risks has recently emerged. Many techniques have been developed to address the risks associated with Android malware. The most extensively used method for identifying Android malware is signature-based detection. The drawback of this method, however, is that it is unable to detect unknown malware. As a consequence of this problem,...

Due to the exponential rise of mobile technology, a slew of new mobile security concerns has surfaced recently. To address the hazards connected with malware, many approaches have been developed. Signature-based detection is the most widely used approach for detecting Android malware. This approach has the disadvantage of being unable to identify unknown malware. As a result of this issue, machine learning (ML) for detecting malware...

This paper describes MAlSim - Mobile Agent Malware Simulator - a mobile agent framework developed to address one of the most important problems related to the simulation of attacks against information systems i.e. the lack of adequate tools for reproducing behaviour of malicious software (malware). The framework can be deployed over the network of an arbitrary information system and it aims at simulating behaviour of each instance...

This paper describes MAlSim - Mobile Agent Malware Simulator - a mobile agent framework developed to address one of the most important problems related to the simulation of attacks against information systems, i.e. the lack of adequate tools for reproducing behaviour of malicious software (malware). The framework can be deployed over the network of an arbitrary information system and it aims at simulating behaviour of each instance...