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Author: search.filters.author.Dua, AmitSubject: search.filters.subject.Data analytics

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    Security solutions against attacks in mobile ad hoc networks and their verification using BAN logic
    (IEEE, 2017) Dua, Amit
    In the last few years, there has been tremendous interest in ad hoc wireless networks as they have massive military and commercial potential. An ad hoc wireless network serves as an independent network that comprises of mobile devices that utilize wireless transmission for communication, having no fixed infrastructure. These networks eliminate the complexity of infrastructure setup and hence can be deployed in quick time. However, on the negative side, such networks are very vulnerable to attacks against availability, service integrity, security, privacy and several other possible threats. To overcome these attacks, several security mechanisms have been proposed to ensure the reliability of ad hoc networks.
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    Decision tree and SVM-based data analytics for theft detection in smart grid
    (IEEE, 2016-03) Dua, Amit
    Nontechnical losses, particularly due to electrical theft, have been a major concern in power system industries for a long time. Large-scale consumption of electricity in a fraudulent manner may imbalance the demand-supply gap to a great extent. Thus, there arises the need to develop a scheme that can detect these thefts precisely in the complex power networks. So, keeping focus on these points, this paper proposes a comprehensive top-down scheme based on decision tree (DT) and support vector machine (SVM). Unlike existing schemes, the proposed scheme is capable enough to precisely detect and locate real-time electricity theft at every level in power transmission and distribution (T&D). The proposed scheme is based on the combination of DT and SVM classifiers for rigorous analysis of gathered electricity consumption data. In other words, the proposed scheme can be viewed as a two-level data processing and analysis approach, since the data processed by DT are fed as an input to the SVM classifier. Furthermore, the obtained results indicate that the proposed scheme reduces false positives to a great extent and is practical enough to be implemented in real-time scenarios.