Malware have been used as a means for conducting cyber attacks for decades. Wide adoption of smartphones, which store lots of private and confidential information, made them an important target for malware developers. Android as the dominant mobile operating system has always been an interesting platform for malware developers and lots of Android malware species are infecting vulnerable users every day which make manual malware investigation an impossible mission. Leveraging machine learning techniques for malware forensics would assist cyber forensic investigators in their fight against malicious programs. In this paper, we present two machine learning aided approaches for static analysis of the mobile applications: one based on permissions , while the other based on source code analysis that utilizes a bag of words representation model. Our source code based classification achieved F-score of 95.1%, while the approach that used permission names only performed with F-measure of 89%. Our approach provides a method for automated static code analysis and malware detection with high accuracy and reduces smartphone malware analysis time.
Widespread adoption of cloud computing has increased the attractiveness of such services to cybercriminals. Distributed denial of service (DDoS) attacks targeting the cloud's bandwidth, services and resources to render the cloud unavailable to both cloud providers, and users are a common form of attacks. In recent times, feature selection has been identified as a pre-processing phase in cloud DDoS attack defence which can potentially increase classification accuracy and reduce computational complexity by identifying important features from the original dataset during supervised learning. In this work, we propose an ensemble-based multi-filter feature selection method that combines the output of four filter methods to achieve an optimum selection. We then perform an extensive experimental evaluation of our proposed method using intrusion detection benchmark dataset, NSL-KDD and decision tree classifier. The findings show that our proposed method can effectively reduce the number of features from 41 to 13 and has a high detection rate and classification accuracy when compared to other classification techniques.
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