Neural Network Intrusion Detection System Research Articles

Internet of Things Intrusion Detection System Based on Convolutional Neural Network

In recent years, the Internet of Things (IoT) technology has developed by leaps and bounds. However, the large and heterogeneous network structure of IoT brings high management costs. In particular, the low cost of IoT devices exposes them to more serious security concerns. First, a convolutional neural network intrusion detection system for IoT devices is proposed. After cleaning and preprocessing the NSL-KDD dataset, this paper uses feature engineering methods to select appropriate features. Then, based on the combination of DCNN and machine learning, this paper designs a cloud-based loss function, which adopts a regularization method to prevent overfitting. The model consists of one input layer, two convolutional layers, two pooling layers and three fully connected layers and one output layer. Finally, a framework that can fully consider the user’s privacy protection is proposed. The framework can only exchange model parameters or intermediate results without exchanging local individuals or sample data. This paper further builds a global model based on virtual fusion data, so as to achieve a balance between data privacy protection and data sharing computing. The performance indicators such as accuracy, precision, recall, F1 score, and AUC of the model are verified by simulation. The results show that the model is helpful in solving the problem that the IoT intrusion detection system cannot achieve high precision and low cost at the same time.

HCRNNIDS: Hybrid Convolutional Recurrent Neural Network-Based Network Intrusion Detection System

Nowadays, network attacks are the most crucial problem of modern society. All networks, from small to large, are vulnerable to network threats. An intrusion detection (ID) system is critical for mitigating and identifying malicious threats in networks. Currently, deep learning (DL) and machine learning (ML) are being applied in different domains, especially information security, for developing effective ID systems. These ID systems are capable of detecting malicious threats automatically and on time. However, malicious threats are occurring and changing continuously, so the network requires a very advanced security solution. Thus, creating an effective and smart ID system is a massive research problem. Various ID datasets are publicly available for ID research. Due to the complex nature of malicious attacks with a constantly changing attack detection mechanism, publicly existing ID datasets must be modified systematically on a regular basis. So, in this paper, a convolutional recurrent neural network (CRNN) is used to create a DL-based hybrid ID framework that predicts and classifies malicious cyberattacks in the network. In the HCRNNIDS, the convolutional neural network (CNN) performs convolution to capture local features, and the recurrent neural network (RNN) captures temporal features to improve the ID system’s performance and prediction. To assess the efficacy of the hybrid convolutional recurrent neural network intrusion detection system (HCRNNIDS), experiments were done on publicly available ID data, specifically the modern and realistic CSE-CIC-DS2018 data. The simulation outcomes prove that the proposed HCRNNIDS substantially outperforms current ID methodologies, attaining a high malicious attack detection rate accuracy of up to 97.75% for CSE-CIC-IDS2018 data with 10-fold cross-validation.

Analysis of possible adaptation of the general pattern of immune system within the systems for preventing intrusions

The subject of this research is the analysis of possible implementation of the mechanisms of functionality of human immune system applicable to information security systems in automated data processing systems. The objects of this research are the human immune system, information security systems, and automated data processing systems. The research is conducted on the basis of achievements of systemic-conceptual approach towards information protection in automated data processing systems, developed within the framework of the project sponsored by the Russian Foundation for Basic Research No. 19-01-00383 on creation of intelligent information protection systems based on the neural network intrusion detection systems and  the mechanisms of artificial immune systems. The article reviews similarity and difference between human immune system and information security systems. Special attention is given to identification of peculiarities of functionality of the mechanisms on detection of harmful intrusions into these systems respectively. Methodological research on the topic are carried out using the achievements in the area of creation of neural network intrusion detection system, built on the basis of artificial immune mechanisms that function similar to human immune system. The main result consists in the conclusion that adaptive information security systems containing the means and mechanisms of protection and built by analogy with the human immune system, may provide successful and effective protection of information in automated data processing systems. The specificity and importance of this conclusion is substantiated by the fact that it can be implemented despite the absence of full analogy between human immune system and information security system; moreover, multiple mechanism of protection implemented in human immune system are absent in the information security system, or the other way around.

Simulation analysis of intrusion detection system based on genetic attribute reduction algorithm and neural network based on rough set theory

With the network technology development and the current attention to network security, the intrusion detection technology of the network system research has being regarded more important. The previous system intrusion detection section has not been able to meet and adapt to the needs of the current rapid development of the network era. The construction of the framework of intrusion detection system is our primary job. This research adopts the genetic attribute reduction algorithm based on rough set and neural network intrusion detection system simulation analysis, through the simple computer algorithm and system simulation analysis of intrusion mode simulation model establishment. The results show that the study has made great success.

Flow Based Intrusion Detection System Using Multistage Neural Network

With the rapid expansion of computer networks during the past decade, security has become a crucial issue for computer systems. And to keep security at highest level, there is an increasing need for effective security monitors such as Network Intrusion Detection System to prevent such illicit. In the recent years many researchers focus their hard work on this field using different approaches to build dependable intrusion detection systems. One of these approaches is Flow-based intrusion detection systems that rely on aggregated network traffic flows. In this paper, Multistage Neural Network intrusion detection system based on aggregated flow data is proposed for detecting and classifying attacks in network traffic. The proposed system detects significant changes in the traffic that could be a possible attack in the first stage of neural network, while the second stage has the ability to recognize an attack, to differentiate one attack from another i.e. classifying attack, and the most important, to detect new attacks with high detection rate and low false negative. Two different neural network structures with the use of different training algorithms have been used in our proposed Intrusion Detection System. The experimental results show that the designed system is promising in terms of accuracy and low probability of false alarms, where the overall accuracy classification rate average is equal to 99.25%.

Intrusion Detection: Supervised Machine Learning

Due to the expansion of high-speed Internet access, the need for secure and reliable networks has become more critical. The sophistication of network attacks, as well as their severity, has also increased recently. As such, more and more organizations are becoming vulnerable to attack. The aim of this research is to classify network attacks using neural networks (NN), which leads to a higher detection rate and a lower false alarm rate in a shorter time. This paper focuses on two classification types: a single class (normal, or attack), and a multi class (normal, DoS, PRB, R2L, U2R), where the category of attack is also detected by the NN. Extensive analysis is conducted in order to assess the translation of symbolic data, partitioning of the training data and the complexity of the architecture. This paper investigates two engines; the first engine is the back-propagation neural network intrusion detection system (BPNNIDS) and the second engine is the radial basis function neural network intrusion detection system (BPNNIDS). The two engines proposed in this paper are tested against traditional and other machine learning algorithms using a common dataset: the DARPA 98 KDD99 benchmark dataset from International Knowledge Discovery and Data Mining Tools. BPNNIDS shows a superior response compared to the other techniques reported in literature especially in terms of response time, detection rate and false positive rate.