A Mean Convolutional Layer for Intrusion Detection System

Abstract

The significant development of Internet applications over the past 10 years has resulted in the rising necessity for the information network to be secured. An intrusion detection system is a fundamental network infrastructure defense that must be able to adapt to the ever-evolving threat landscape and identify new attacks that have low false alarm. Researchers have developed several supervised as well as unsupervised methods from the data mining and machine learning disciplines so that anomalies can be detected reliably. As an aspect of machine learning, deep learning uses a neuron-like structure to learn tasks. A successful deep learning technique method is convolution neural network (CNN); however, it is presently not suitable to detect anomalies. It is easier to identify expected contents within the input flow in CNNs, whereas there are minor differences in the abnormalities compared to the normal content. This suggests that a particular method is required for identifying such minor changes. It is expected that CNNs would learn the features that form the characteristic of the content of an image (flow) rather than variations that are unrelated to the content. Hence, this study recommends a new CNN architecture type known as mean convolution layer (CNN-MCL) that was developed for learning the anomalies’ content features and then identifying the particular abnormality. The recommended CNN-MCL helps in designing a strong network intrusion detection system that includes an innovative form of convolutional layer that can teach low-level abnormal characteristics. It was observed that assessing the proposed model on the CICIDS2017 dataset led to favorable results in terms of real-world application regarding detecting anomalies that are highly accurate and have low false-alarm rate as opposed to other best models.

A Double-Layered Hybrid Approach for Network Intrusion Detection System Using Combined Naive Bayes and SVM

“…. , F i } are [8,12,23,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41] (see Table 5). The correlated features between Probe and the rest {F 1 , F 2 , .…”

“…, F j } = [1,12,23,24,25,27,28,29,30,31,33,34,35,36,37,38,39,40,41]. Therefore, the intersect features of DOS/Probe are [12,23,25,27,28,29,30,31,33,34,35,36,37,38,39,40,41] In group 2, the correlated features between R2L and Normal {F 1 , F 2 , . .…”

“…. , F k } are [1,5,6,9,10,11,12,14,18,22,23,24,29,30,31,32,33,34,35,36,37,38,39]. The correlated features between U2R and Normal {F 1 , F 2 , .…”

Encryption Techniques for Intelligent Transportation Systems via Deep Learning for IOV in Smart Cities

“…A discussion on the existing literature related to the topic is presented below. Leila Mohammadpouret al have proposeda deep learning method to implement aneffective and flexible NIDS [5]. In this authors have used a convolutional neural network as an advanced deep learning technique.…”

Review of Anomaly Detection Systems in Industrial Control Systems Using Deep Feature Learning Approach

“…One of the most important features of deep learning is the use of unsupervised methods to autonomously learn hierarchical features in deep learning models [7] [11] [12] [13] [14].…”