Biblio

Conventional systems has to be updated as the technology advances at quick pace. A smart grid is a renovated and digitalized version of a standard electrical infrastructure that allows two-way communication between customers and the utility, which overcomes huge manual hustle. Advanced Metering Infrastructure plays a major role in a smart grid by automatically reporting the power consumption readings to the utility through communication networks. However, there is always a trade-off. Security of AMI communication is a major problem that must be constantly monitored if this technology is to be fully utilized. This paper mainly focuses on developing a virtual setup of fully functional smart meter and a web application for generating electricity bill which allows consumer to obtain demand response, where the data is managed at server side. It also focuses on analyzing the potential security concerns posed by MITM-Arp-spoofing attacks on AMI systems and session hijacking attacks on web interfaces. This work also focusses on mitigating the vulnerabilities of session hijacking on web interface by restricting the cookies so that the attacker is unable to acquire any confidential data.

Machine learning is a major area in artificial intelligence, which enables computer to learn itself explicitly without programming. As machine learning is widely used in making decision automatically, attackers have strong intention to manipulate the prediction generated my machine learning model. In this paper we study about the different types of attacks and its countermeasures on machine learning model. By research we found that there are many security threats in various algorithms such as K-nearest-neighbors (KNN) classifier, random forest, AdaBoost, support vector machine (SVM), decision tree, we revisit existing security threads and check what are the possible countermeasures during the training and prediction phase of machine learning model. In machine learning model there are 2 types of attacks that is causative attack which occurs during the training phase and exploratory attack which occurs during the prediction phase, we will also discuss about the countermeasures on machine learning model, the countermeasures are data sanitization, algorithm robustness enhancement, and privacy preserving techniques.