Conference Papers
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Item Split personality malware detection and defeating in popular virtual machines(2012) Kumar, A.V.; Vishnani, K.; Kumar, K.V.Virtual Machines have gained immense popularity amongst the Security Researchers and Malware Analysts due to their pertinent design to analyze malware without risking permanent infection to the actual system carrying out the tests. This is because during analysis, even if a malware infects and destabilizes the guest OS, the analyst can simply load in a fresh image thus avoiding any damage to the actual machine. However, the cat and mouse game between the Black Hat and the White Hat Hackers is a well established fact. Hence, the malware writers have once again raised their stakes by creating a new kind of malware which can detect the presence of virtual machines. Once it detects that it is running on a virtual machine, it either terminates execution immediately or simply hides its malicious intent and continues to execute in a benign manner thus evading its own detection. This category of malware has been termed as Split Personality malware or Analysis Aware malware in the Information Security jargon. This paper aims at defeating the split personality malware in popular virtual machine environment. This work includes first the study of various virtual machine detection techniques and then development of a method to thwart these techniques from successfully detecting the virtual machines-VirtualBox, VirtualPC and VMware. Copyright © 2012 ACM.Item EMD based Detrending of Non-linear and Non-stationary Power System Signals(Institute of Electrical and Electronics Engineers Inc., 2021) Aalam, M.K.; Shubhanga, K.N.In electromechanical modal analysis of power systems using Wide Area Measurement System (WAMS) based setup, signal processing is complex as the signals are non-stationary and non-linear in nature. In order to get accurate modal parameters, as a first step, it is required to remove the non-linear trend of the signal. In the literature, many conventional methods such as filtering, averaging and peak detection techniques are employed for removing trend. In this paper, Empirical Mode Decomposition (EMD) method, an iterative algorithm is presented to detrend a signal. The EMD method and its variant are compared with another popularly used peak detection method referred to as the Zhou's detrending algorithm to find the efficacy of the EMD methods. To test the algorithms, a four machine, two-area power system with three-wind farms is modeled and simulated to generate the power system signals which bring out non-linear and non-stationary nature. Further, the modal characterization is carried out employing Prony analysis. © 2021 IEEE.