Cyberattacks (CAs) on modern interconnected power systems are currently a primary concern. The development of information and communication technology (ICT) has increased the possibility of unauthorized access to power system networks for data manipulation. Unauthorized data manipulation may lead to the partial or complete shutdown of a power network. In this paper, we propose a novel security unit that mitigates intrusion for an interconnected power system and compensates for data manipulation to augment cybersecurity. The studied two-area interconnected power system is first stabilized to alleviate frequency deviation and tie-line power between the areas by designing a fractional-order proportional integral derivative (FPID) controller. Since the parameters of the FPID controller can also be influenced by a CA, the proposed security unit, named the automatic intrusion mitigation unit (AIMU), guarantees control over such changes. The effectiveness of the AIMU is inspected against a CA, load variations, and unknown noises, and the results show that the proposed unit guarantees reliable performance in all circumstances.

Interconnected power system is a promising source of electric power that fulfils the excess demand of electricity throughout the world whose safe and reliable operation is necessary for decreasing loadshedding and increasing resiliency. The development of information and communication technology (ICT) not only blessing for us but also hampers our technology by promoting cyber-crime. Cyber-attack (CA) on power system is now becoming a common problem that produces unauthorized access to the control unit of power system and hampers the whole system partially or completely by changing the sensitive data of power system and control unit. The performance of the power system is regulated by employing a fractional-order-proportional-integral-derivative (FPID) controller and is compared with conventional PID controller in this paper. The reliable performance of the power system completely depends on the efficient design of controller, but the parameters of the controller are largely affected by the CA and damage the whole system. Any change of the control unit or the system parameters may decrease the resiliency and the stability of the power system. An automatic cyber-attack mitigation technique (ACAMU) has been proposed in this article to completely mitigate the CA and its impact on the system and controller to enhance the security and resiliency of power system by maintaining a fixed data for both system and controller.

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Community and portal websites like Twitter, Facebook, Tumbler, Instagram, and LinkedIn etc. have significant impact in our day-to-day life. One of the most popular micro-blogging platforms is twitter that can provide a huge amount of data which in future can be used for various applications of opinion mining like predictions, reviews, elections, marketing etc. The users use this platform to share their views, express sentiments on various events of their daily life. Previously, many researchers have worked with twitter sentiment analysis and compared various classifiers and got the accuracy below 82%. In this work for classifying tweets into sentiments, we have used various classifiers such as Naïve Bayes, Support Vector Machine and Maximum Entropy that segregate the positive and negative tweets. Using Bigram Collocation with classifiers, we’ve acquired 88.42% accuracy. KEYWORDS: Twitter; Sentiment Classification; Machine Learning; NLTK; Python; Naïve Bayes; Support Vector Machine (SVM); Maximum Entropy

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