Abstract
The use of deep learning in various models is a powerful tool in detecting IoT attacks, identifying new types of intrusion to access a better secure network. Need to developing an intrusion detection system to detect and classify attacks in appropriate time and automated manner increases especially due to the use of IoT and the nature of its data that causes increasing in attacks. Malicious attacks are continuously changing, that cause new attacks. In this paper we present a survey about the detection of anomalies, thus intrusion detection by distinguishing between normal behavior and malicious behavior while analyzing network traffic to discover new attacks. This paper surveys previous researches by evaluating their performance through two categories of new datasets of real traffic are (CSE-CIC-IDS2018 dataset, Bot-IoT dataset). To evaluate the performance we show accuracy measurement for detect intrusion in different systems.
Highlights
Supervisory Control and Data Acquisitions and Industrial Control Systems are important, which the Critical National Infrastructures depend in managing its production using the Internet of Things (IoT)
This study focuses on anomaly-based NIDS, which is considered because it helps detect new threats in IoT
These techniques show general algorithms to design efficient NIDS based on artificial intelligence (AI), describing the most algorithms used in Machine Learning Techniques (ML) and Deep Learning Techniques (DL)
Summary
Supervisory Control and Data Acquisitions and Industrial Control Systems are important, which the Critical National Infrastructures depend in managing its production using the Internet of Things (IoT). 83-93 performance of deep learning using two new datasets of real traffic (i.e. CSE-CICIDS2018 and Bot-IoT datasets) These factors are as follows: ‘high accuracy rate’, ‘high detection rate’ (DR) and ‘low false alarm report’ (FAR); these factors influence NIDS performance. ‘Statistical-based anomaly IDS’ periodically captures from the traffic statistical features and matches them with the normal operation of a generated stochastic model of traffic [17]. ‘Machine learning-based anomaly IDS’ develops a model for the analysed patterns, which is explicit or implicit. These models should be regularly updated to support the efficiency of intrusion detection based on past results [19]. The feature set design is important to identify network traffic, and it is an ongoing research problem [22]
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