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Today, malware is one of the primary cyberthreats to organizations. Malware has pervaded almost every type of computing device including the ones having limited memory, battery and computation power such as mobile phones, tablets and embedded devices like Internet-of-Things (IoT) devices. Consequently, the privacy and security of the malware infected systems and devices have been heavily jeopardized. In recent years, researchers have leveraged machine learning based strategies for malware detection and classification. Malware analysis approaches can only be employed in resource constrained environments if the methods are lightweight in nature. In this paper, we present MALITE, a lightweight malware analysis system, that can classify various malware families and distinguish between benign and malicious binaries. MALITE converts a binary into a gray scale or an RGB image and employs low memory and battery power consuming as well as computationally inexpensive malware analysis strategies. We have designed MALITE-MN, a lightweight neural network based architecture and MALITE-HRF, an ultra lightweight random forest based method that uses histogram features extracted by a sliding window. We evaluate the performance of both on six publicly available datasets (Malimg, Microsoft BIG, Dumpware10, MOTIF, Drebin and CICAndMal2017), and compare them to four state-of-the-art malware classification techniques. The results show that MALITE-MN and MALITE-HRF not only accurately identify and classify malware but also respectively consume several orders of magnitude lower resources (in terms of both memory as well as computation capabilities), making them much more suitable for resource constrained environments.

In recent years, Attribute-Based Access Control (ABAC) has become quite popular and effective for enforcing access control in dynamic and collaborative environments. Implementation of ABAC requires the creation of a set of attribute-based rules which cumulatively form a policy. Designing an ABAC policy ab initio demands a substantial amount of effort from the system administrator. Moreover, organizational changes may necessitate the inclusion of new rules in an already deployed policy. In such a case, re-mining the entire ABAC policy will require a considerable amount of time and administrative effort. Instead, it is better to incrementally augment the policy. Keeping these aspects of reducing administrative overhead in mind, in this paper, we propose PAMMELA, a Policy Administration Methodology using Machine Learning to help system administrators in creating new ABAC policies as well as augmenting existing ones. PAMMELA can generate a new policy for an organization by learning the rules of a policy currently enforced in a similar organization. For policy augmentation, PAMMELA can infer new rules based on the knowledge gathered from the existing rules. Experimental results show that our proposed approach provides a reasonably good performance in terms of the various machine learning evaluation metrics as well as execution time., Comment: This work is under progress

Quality of Service provisioning is one of the major design goals of IEEE 802.16 mesh networks. In order to provide quality delivery of delay sensitive services such as voice, video etc., it is required to route such traffic over a minimum delay path. In this paper we propose a routing metric for delay sensitive services in IEEE 802.16 mesh networks. We design a new cross layer routing metric, namely Expected Scheduler Delay (ESD), based on HoldOff exponent and the current load at each node of the network. This proposed metric takes into account the expected theoretical end-to-end delay of routing paths as well as network congestion to find the best suited path. We propose an efficient distributed scheme to calculate ESD and route the packets using source routing mechanism based on ESD. The simulation results demonstrate that our metric achieves reduced delay compared to a standard scheme used in IEEE 802.16 mesh, that takes hop count to find the path., Comment: This paper has been presented at the International Conference on Wireless and Optical Communications, May 2011, China