Security is a critical aspect in many of the latest embedded and IoT systems. Malware is one of the severe threats of security for such devices. There have been enormous efforts in malware detection and analysis; however, occurrences of newer varieties of malicious codes prove that it is an extremely difficult problem given the nature of these surreptitious codes. In this article, instead of addressing a general solution, we aim at malware detection for platforms that have more than one core for performance enhancement. We investigate the utility of multiple cores from the point of view of security, where one of the cores operate as a watchdog. We define a notion of a new metric called LAMBDA (Lightweight Assessment of Malware for emBeddeD Architectures), denoted by λ, indicating a conceptual boundary between the programs which are allowed to run on a given platform, with the codes that are suspected as malwares. The metric λ is computed using carefully chosen monitors or features, which are tuples of high-level programs representing OS resources, along with low-level hardware performance counters. In comparison to heavy-weight machine learning techniques, we use an online hypothesis testing, in the form of t -test, to classify a given program-under-test. For applications where security is of prime concern, we propose an additional step based on multivariate analysis to classify the unknown programs that are closer to the threshold with a high degree of confidence. We present experimental results focusing on an ARM-based platform which validate that the proposed approach provides a lightweight, accurate assessment of malware codes for embedded platforms. In addition to it, we also present a security analysis to show the difficulty of a mimicry attack attempting to bypass LAMBDA.

Online data sharing for increased productivity and efficiency is one of the primary requirements today for any organization. The advent of cloud computing has pushed the limits of sharing across geographical boundaries, and has enabled a multitude of users to contribute and collaborate on shared data. However, protecting online data is critical to the success of the cloud, which leads to the requirement of efficient and secure cryptographic schemes for the same. Data owners would ideally want to store their data/files online in an encrypted manner, and delegate decryption rights for some of these to users, while retaining the power to revoke access at any point of time. An efficient solution in this regard would be one that allows users to decrypt multiple classes of data using a single key of constant size that can be efficiently broadcast to multiple users. Chu et al. proposed a key aggregate cryptosystem (KAC) in 2014 to address this problem, albeit without formal proofs of security. In this paper, we propose CPA and CCA secure KAC constructions that are efficiently implementable using elliptic curves and are suitable for implementation on cloud based data sharing environments. We lay special focus on how the standalone KAC scheme can be efficiently combined with broadcast encryption to cater to m data users and m' data owners while reducing the reducing the secure channel requirement from O(mm') in the standalone case to O(m + m').

A class of symmetric Hopfield networks with nonpositive synapses and zero threshold is analyzed in detail. It is shown that all stationary points have a one-to-one correspondence with the minimal vertex covers of certain undirected graphs, that the sequential Hopfield algorithm as applied to this class of networks converges in at most 2n steps (n being the number of neurons), and that the parallel Hopfield algorithm either converges in one step or enters a two-cycle in one step. The necessary and sufficient condition on the initial iterate for the parallel algorithm to converge in one step are given. A modified parallel algorithm which is guaranteed to converge in [3n/2] steps ([x] being the integer part of x) for an n-neuron network of this particular class is also given. By way of application, it is shown that this class naturally solves the vertex cover problem. Simulations confirm that the solution provided by this method is better than those provided by other known methods.