DeepDCA: Novel Network-Based Detection of IoT Attacks Using Artificial Immune System

Applied Sciences

Bander Alzahrani¹  Abdullah Al-Barakati²  Daniyal Alghazzawi²  Sahar Aldhaheri²  Li Cheng³ 

[1] Chemistry, Chinese Academy of Sciences, Urumqi 830011, China;Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia;;Xinjiang Technical Institute of Physics &

关键词: artificial intelligence;    artificial immune system;    cyber security;    danger theory;    deep learning;    dendritic cell;    internet of things;    iot;    network security;   

DOI  :  10.3390/app10061909

来源: DOAJ

【 摘 要 】

Recently Internet of Things (IoT) attains tremendous popularity, although this promising technology leads to a variety of security obstacles. The conventional solutions do not suit the new dilemmas brought by the IoT ecosystem. Conversely, Artificial Immune Systems (AIS) is intelligent and adaptive systems mimic the human immune system which holds desirable properties for such a dynamic environment and provides an opportunity to improve IoT security. In this work, we develop a novel hybrid Deep Learning and Dendritic Cell Algorithm (DeepDCA) in the context of an Intrusion Detection System (IDS). The framework adopts Dendritic Cell Algorithm (DCA) and Self Normalizing Neural Network (SNN). The aim of this research is to classify IoT intrusion and minimize the false alarm generation. Also, automate and smooth the signal extraction phase which improves the classification performance. The proposed IDS selects the convenient set of features from the IoT-Bot dataset, performs signal categorization using the SNN then use the DCA for classification. The experimentation results show that DeepDCA performed well in detecting the IoT attacks with a high detection rate demonstrating over 98.73% accuracy and low false-positive rate. Also, we compared these results with State-of-the-art techniques, which showed that our model is capable of performing better classification tasks than SVM, NB, KNN, and MLP. We plan to carry out further experiments to verify the framework using a more challenging dataset and make further comparisons with other signal extraction approaches. Also, involve in real-time (online) attack detection.

【 授权许可】

Unknown