Abstract
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.
Highlights
In the academic and industrial circles, Internet of Things (IoT) have became an active area.According to Cisco, 500 billion devices will be connected by the year 2030 [1]
True Negative (TN): is the number of actual legitimate records identified as normal, False Negative (FN): is the actual anomalous records categorized as normal
We evaluate the performance of Deep Learning and Dendritic Cell Algorithm (DeepDCA) model in terms of Accuracy, Precision, Recall, F-measure, and False alarm rate: Accuracy =
Summary
According to Cisco, 500 billion devices will be connected by the year 2030 [1]. This technology is promising in many sectors, such as uch as smart homes, health-care, intelligent transportation, power smart grid and numerous areas that not yet even conceived [2], it carries with it many security risks. Easy accessibility and tremendous propagation of IoT devices creates a fertile environment for cyber attacks. Most of these devices are small, inexpensive and have limited memory and computing capacity to run the current existing security software [3]. According to Hernández-Pereira, Elena, et al [33] Intrusion can be defined as “any set of actions that attempt to compromise the Confidentiality, Integrity, and Availability (CIA) of information resources.” Typical
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