Conference Papers

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    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.
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    An effective analysis on intrusion detection systems in wireless mesh networks
    (Institute of Electrical and Electronics Engineers Inc., 2017) Karri, K.G.; Raju, V.P.; Santhi Thilagam, P.S.
    Intrusion Detection Systems(IDSs) are widely used to detect both known attacks and unknown attacks performed by internal and external attackers in wireless networks. However, challenging issues for developing IDSs inWireless Mesh Networks (WMNs) are 1) supporting interoperability and 2) handling volatile parameters. In addition, security standards for WMN are still in draft stage, and to protect the WMN, IDSs of similar wireless networks such as wireless sensor, Ad-Hoc, MANET can be adopted, but the best performance is not guaranteed. In this paper, we have classified the existing IDSs for wireless networks into four categories namely single layer IDS, cross-layer IDS, reputation-based IDS, reputation based cross-layer IDS, and analyzed the performance of these IDSs with core-layer attacks and detection methodology. Based on our analysis, we address the loopholes in existing IDSs and specify research directions for strengthening the existing IDSs and for developing new efficient IDSs with respect to backbone mesh and client mesh networks. © 2017 IEEE.
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    Efficient Traffic Signboard Recognition System Using Convolutional Networks
    (Springer, 2020) Mothukuri, S.K.P.; Tejas, R.; Patil, S.; Darshan, V.; Koolagudi, S.G.
    In this paper, a smart automatic traffic sign recognition system is proposed. This signboard recognition system plays a vital role in the automated driving system of transport vehicles. The model is built based on convolutional neural network. The German Traffic Sign Detection Benchmark (GTSDB), a standard open-source segmented image dataset with forty-three different signboard classes is considered for experimentation. Implementation of the system is highly focused on processing speed and classification accuracy. These aspects are concentrated, such that the built model is suitable for real-time automated driving systems. Similar experiments are carried in comparison with the pre-trained convolution models. The performance of the proposed model is better in the aspects of fast responsive time. © Springer Nature Singapore Pte Ltd. 2020.
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    Development of Fault Detection Method in Cable Using Arduino UNO
    (Institute of Electrical and Electronics Engineers Inc., 2022) Bairwa, B.; Rathod, S.; Yaragatti, U.R.; Manohar, K.A.
    This study provides the investigation of underground cable fault. Fault are classified into two type such as symmetrical and unsymmetrical fault. For this fault detection range of about 1m to 2.6 km of the underground cable have been investigated. In underground cable, fault is validating through live tests as per the research knowledge. The underground cable fault are largely caused due to improper insulation, interweave, mesh and other accessories. symmetrical and unsymmetrical fault are present to detect and classify incipient fault in underground cable at the distribution voltage level. The wavelet transformer approach has been used to detect the fault location of the underground fault. This project deal with number of high voltage cable fault location technique with modeling and simulation. © 2022 IEEE.