Publicação

Attack and Intrusion Detection on the IoT

Ver documento

Detalhes bibliográficos
Resumo:Nowadays, the Internet of Things (IoT) has a big impact on our way of living, making security a huge concern. In many cases, standard techniques like firewalls are unable to prevent intrusions, and thus, Intrusion Detection Systems (Intrusion Detection System (IDS)s) are required, either for detecting external or internal breaches of security. Currently, for the IoT, there are no ready-to-use IDS solutions based on pattern recognition. The main goal of this work is to develop an Anomaly-based IDS for the IoT, operating at the Constrained Application Protocol (CoAP). The new Anomaly-based IDS, AnIDS, is presented along with the architecture, software libraries, platforms, scenario and performance evaluation. Client CoAP level misbehaviors are programmed in Contiki, and features are calculated with Wireshark, a packet dissector. These features are pre-processed by standardization and the feature extraction algorithms Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). The resulting data is processed using Support Vector Machine (SVM), a machine learning algorithm based on the Scikit-Learn package, in order to train a classifier. This classifier is evaluated with the plotting of Confusion Matrices and Receiving Operator Characteristic (ROC) Curves. AnIDS allows to detect the signs of an intrusion, by cross checking the normal behavior, known a priori of the nodes, with a known data pattern of intrusion. AnIDS is also able to identify the type of misbehavior present on a node based on a priori data patterns of the specific intrusion. The Client CoAP misbehavior implemented are: implicit Denial of Service (DoS), repeatedly Acknowledgement (ACK) sending, wrong URI requesting, and invalid CoAP content accept option requesting. Results demonstrate that AnIDS can obtain an F_Measure and a Recall scores of ∼ 98%, demonstrating that the system is capable to distinguish between the normal and the intruder behavior. As CoAP is the most disseminated open source application protocol on the IoT, AnIDS can have a significant impact on IoT security.
Autores principais:Silva, João Miguel Coimbra Barros da
Assunto:IoT 6LoWPAN CoAP Machine Learning IDS IoT 6LoWPAN CoAP Machine Learning IDS
Ano:2018
País:Portugal
Tipo de documento:dissertação de mestrado
Tipo de acesso:acesso aberto
Instituição associada:Universidade de Coimbra
Idioma:inglês
Origem:Estudo Geral - Universidade de Coimbra

Registos relacionados

Atividades financiadas

Não foram encontrados projetos financiados.

Registos relacionados