Journal Articles
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884
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Item Performance enhancement of electrical distribution systems with multiple distributed generation sources(2010) Krishnamurthy, M.; Vittal, K.P.The recent trends in electrical power distribution system operation and management are aimed at improving system conditions in order to render good service to the customer. Reforms in the distribution sector have given major scope for employment of distributed generation (DG) resources which will boost system performance. This article proposes a heuristic technique for allocation of multiple distribution generation sources in a distribution system. The allocation is determined based on overall improvement in network performance parameters like reduction in system losses, improvement in voltage stability, improvement in voltage profile. The hybrid of Genetic Algorithm with the proposed Network Performance Enhancement Index (NPEI) along with the heuristic rules facilitates determination of feasible location for insertion of DG sources. A priority list is prepared with decreasing values of NPEI so that the designer can select most feasible locations. The developed approach is tested with different test systems to ascertain its effectiveness.Item Detection of phishing websites using an efficient feature-based machine learning framework(Springer London, 2019) Rao, R.S.; Pais, A.R.Phishing is a cyber-attack which targets naive online users tricking into revealing sensitive information such as username, password, social security number or credit card number etc. Attackers fool the Internet users by masking webpage as a trustworthy or legitimate page to retrieve personal information. There are many anti-phishing solutions such as blacklist or whitelist, heuristic and visual similarity-based methods proposed to date, but online users are still getting trapped into revealing sensitive information in phishing websites. In this paper, we propose a novel classification model, based on heuristic features that are extracted from URL, source code, and third-party services to overcome the disadvantages of existing anti-phishing techniques. Our model has been evaluated using eight different machine learning algorithms and out of which, the Random Forest (RF) algorithm performed the best with an accuracy of 99.31%. The experiments were repeated with different (orthogonal and oblique) random forest classifiers to find the best classifier for the phishing website detection. Principal component analysis Random Forest (PCA-RF) performed the best out of all oblique Random Forests (oRFs) with an accuracy of 99.55%. We have also tested our model with the third-party-based features and without third-party-based features to determine the effectiveness of third-party services in the classification of suspicious websites. We also compared our results with the baseline models (CANTINA and CANTINA+). Our proposed technique outperformed these methods and also detected zero-day phishing attacks. © 2018, The Natural Computing Applications Forum.Item A heuristic technique to detect phishing websites using TWSVM classifier(Springer Science and Business Media Deutschland GmbH, 2021) Rao, R.S.; Pais, A.R.; Anand, P.Phishing websites are on the rise and are hosted on compromised domains such that legitimate behavior is embedded into the designed phishing site to overcome the detection. The traditional heuristic techniques using HTTPS, search engine, Page Ranking and WHOIS information may fail in detecting phishing sites hosted on the compromised domain. Moreover, list-based techniques fail to detect phishing sites when the target website is not in the whitelisted data. In this paper, we propose a novel heuristic technique using TWSVM to detect malicious registered phishing sites and also sites which are hosted on compromised servers, to overcome the aforementioned limitations. Our technique detects the phishing websites hosted on compromised domains by comparing the log-in page and home page of the visiting website. The hyperlink and URL-based features are used to detect phishing sites which are maliciously registered. We have used different versions of support vector machines (SVMs) for the classification of phishing websites. We found that twin support vector machine classifier (TWSVM) outperformed the other versions with a significant accuracy of 98.05% and recall of 98.33%. © 2020, Springer-Verlag London Ltd., part of Springer Nature.