Sanket Shukla

Sreenitha Kasarapu, Sanket Shukla, Rakibul Hassan et al.

One of the pivotal security threats for the embedded computing systems is malicious software a.k.a malware. With efficiency and efficacy, Machine Learning (ML) has been widely adopted for malware detection in recent times. Despite being efficient, the existing techniques require a tremendous number of benign and malware samples for training and modeling an efficient malware detector. Furthermore, such constraints limit the detection of emerging malware samples due to the lack of sufficient malware samples required for efficient training. To address such concerns, we introduce a code-aware data generation technique that generates multiple mutated samples of the limitedly seen malware by the devices. Loss minimization ensures that the generated samples closely mimic the limitedly seen malware and mitigate the impractical samples. Such developed malware is further incorporated into the training set to formulate the model that can efficiently detect the emerging malware despite having limited exposure. The experimental results demonstrates that the proposed technique achieves an accuracy of 90% in detecting limitedly seen malware, which is approximately 3x more than the accuracy attained by state-of-the-art techniques.

Abhijitt Dhavlle, Sanket Shukla

Our computer systems for decades have been threatened by various types of hardware and software attacks of which Malwares have been one of them. This malware has the ability to steal, destroy, contaminate, gain unintended access, or even disrupt the entire system. There have been techniques to detect malware by performing static and dynamic analysis of malware files, but, stealthy malware has circumvented the static analysis method and for dynamic analysis, there have been previous works that propose different methods to detect malware but, in this work we propose a novel technique to detect malware. We use malware binary images and then extract different features from the same and then employ different ML-classifiers on the dataset thus obtained. We show that this technique is successful in differentiating classes of malware based on the features extracted.