Suchergebnisse

The Internet of Things (IoT) provides the ability to extend the Internet into devices and everyday objects, in a way that they are uniquely addressable. Sensors, actuators, as well as everyday devices and objects, such as cellphones, cars, and homes, are interconnected and form a network that can be accessed, monitored, and controlled remotely. Security is an important subject in the IoT networks since the devices and the networks can be used as a means of invading the users' privacy. The current work examines the issue of security agent location using indicative intrusion detection techniques for network layer attacks. We analyze the methodology, operation, as well as the complexity of each technique. Through the extensive implementation and experimentation, we are able to conclude that the local security agents have the same performance results with centralized and decentralized approaches, but with negligible overhead. As such, they are useful when internal network communication, or network augmentation with monitoring nodes, is not feasible. ; This work has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No 739578 and the Government of the Republic of Cyprus through the Directorate General for European Programmes, Coordination and Development.

Machine learning models have long be proposed to detect the presence of unauthorized activity within computer networks. They are used as anomaly detection techniques to detect abnormal behaviors within the network. We propose to use Support Vector Machine (SVM) learning anomaly detection model to detect abnormalities within the Internet of Things. SVM creates its normal profile hyperplane based on both benign and malicious local sensor activity. An important aspect of our work is the use of actual IoT network traffic with specific network layer attacks implemented by us. This is in contrast to other works creating supervised learning models, with generic datasets. The proposed detection model achieves up to 100% accuracy when evaluated with unknown data taken from the same network topology as it was trained and 81% accuracy when operating in an unknown topology. ; This work has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No 739578 and the Government of the Republic of Cyprus through the Directorate General for European Programmes, Coordination and Development.