Home Adaptation Security

As several home appliances, such as air conditioners, heaters, and refrigerators, were connecting to the Internet, they became targets of cyber-attacks, which cause serious problems such as compromising safety and even harming users. We have proposed a method to detect such attacks based on user behaviour based on adaptive learning capabilities of system. This method models user behaviour as sequences of user events including operation of home devices and other monitored activities. Considering users behave depending on the condition of the home such as climatic temperature, our method learns event sequences for each condition. To mitigate the impact of events of other users in the home included in the monitored sequence, our method generates multiple event sequences by removing some events and learning the frequently observed sequences. In this project, we also discuss the effectiveness of our method by comparing with a method learning users’ behaviour by Hidden Markov Models. We have utilized tools such as Arduino IDE and Pycharm those which provided software support for the project and helps us to process real time data with more resolution and accuracy incorporated convincingly.