Neural Network Based Intrusion Detection Research Articles

Intelligent intrusion detection system using multilayer perceptron optimised by genetic algorithm

This paper presents a neural network-based intrusion detection method for the attacks on a computer network. Neural networks are used to predict unusual activities in the system. In particular, feed forward neural networks with the back propagation training algorithm were employed in this study. We propose a method of intrusion detection based on a combination of genetic algorithm (GA) and multilayer perceptron (MLP) neural network to develop a model for intrusion detection system. All tests were realised with the kdd99 data set. The performance of the proposed method of intrusion detection was evaluated on all KDD99 data set, 10% of the KDD99 data set were used for training the GA-MLP model. This system achieves a top accuracy of up to 93.05%.

Intelligent intrusion detection system using multilayer perceptron optimised by genetic algorithm

This paper presents a neural network-based intrusion detection method for the attacks on a computer network. Neural networks are used to predict unusual activities in the system. In particular, feed forward neural networks with the back propagation training algorithm were employed in this study. We propose a method of intrusion detection based on a combination of genetic algorithm (GA) and multilayer perceptron (MLP) neural network to develop a model for intrusion detection system. All tests were realised with the kdd99 data set. The performance of the proposed method of intrusion detection was evaluated on all KDD99 data set, 10% of the KDD99 data set were used for training the GA-MLP model. This system achieves a top accuracy of up to 93.05%.

A New Neural Network-Based Intrusion Detection System for Detecting Malicious Nodes in WSNs

The Wireless Sensor Networks (WSNs) are vulnerable to numerous security hazards that could affect the entire network performance, which could lead to catastrophic problems such as a denial of service attacks (DoS). The WSNs cannot protect these types of attacks by key management protocols, authentication protocols, and protected routing. A solution to this issue is the intrusion detection system (IDS). It evaluates the network with adequate data obtained and detects the sensor node(s) abnormal behavior. For this work, it is proposed to use the intrusion detection system (IDS), which recognizes automated attacks by WSNs. This IDS uses an improved LEACH protocol cluster-based architecture designed to reduce the energy consumption of the sensor nodes. In combination with the Multilayer Perceptron Neural Network, which includes the Feed Forward Neutral Network (FFNN) and the Backpropagation Neural Network (BPNN), IDS is based on fuzzy rule-set anomaly and abuse detection based learning methods based on the fugitive logic sensor to monitor hello, wormhole and SYBIL attacks.

ANNIDS: Artificial Neural Network based Intrusion Detection System for Internet of Things

Internet of Things (IoT) makes everything in the real world to get connected. The resource constrained characteristics and the different types of technology and protocols tend to the IoT be more vulnerable than the conventional networks. Intrusion Detection System (IDS) is a tool which monitors analyzes and detects the abnormalities in the network activities. Machine Learning techniques are implemented with the Intrusion detection systems to enhance the performance of IDS. Various studies on IoT reveals that Artificial Neural Network (ANN) provides better accuracy and detection rate than other approaches. In this paper, an Artificial Neural Network based IDS (ANNIDS) technique based on Multilayer Perceptron (MLP) is proposed to detect the attacks initiated by the Destination Oriented Direct Acyclic Graph Information Solicitation (DIS) attack and Version attack in IoT environment. Contiki O.S/Cooja Simulator 3.0 is used for the IoT simulation.

A new intrusion detection and alarm correlation technology based on neural network

With the continuous development of computer networks, the security of the network has become increasingly prominent. A major threat to network security is the intrusion of information systems through the network. Intrusion detection of the traditional intrusion detection and alarm technology is not sufficient. Based on neural network technology, this paper studies the intrusion detection and alarm correlation technology. Based on the research on the working principle and workflow of the existing intrusion detection system, a new neural network-based intrusion detection and alarm method is proposed. A neural network-based intrusion detection and alarm system is designed and implemented. Through the experiment of the system prototype, the results show that the intrusion detection and alarm system based on the neural network has a higher detection rate and a lower false alarm rate for intrusion behaviors such as denial of service attack and has higher detection ability for unknown attack behaviors.

Network data management model based on Naïve Bayes classifier and deep neural networks in heterogeneous wireless networks

A key purpose of a data management system is to monitor and control the large volume of data and other relevant information. Focusing on the data management issues and developing a detection system is necessary for resolving attacks or intrusion in a network. This network instruction detection system helps to identify unseen and unpredictable attacks in management station via loophole to break network security. Conventional instruction detection system has complexities in exploiting, enhancing the security features and this research work focuses on solving above issue. The proposed research work is designed for efficient and flexible network intrusion detection system using Naïve Bayes classifier and deep neural networks. The experimental results show that proposed Deep Neural Network-based Intrusion Detection System is suitable for classification with high accuracy and precision in both binary and multiclass, recall and f- measure values. Compared with other state-of-the-art approaches, the analytic accuracy has been improved.

Neural Network Based Intrusion Detection: State of the Art

With the advancement in technology and over internet, the number of attacks through unauthorized access has also increased. For fighting these attacks and to ensure the safety of the system, powerful Intrusion Detection System (IDS) is required. IDS’s are used for sensing the attack persisting inside the network system. This paper reviews different existing Intrusion Detection Systems using Artificial Neural Networks for detecting malicious network activity as the success of neural networks comes from the fact that they are able to learn a number of behaviours depending on network input. They are very effective and fast in classification and can easily identify new threats. One of the features that uphold neural networks in the field of intrusion detection is its flexibility to adapt to any environment. This work evaluates the parameters that play the major role in enhancing the efficiency and accuracy of IDS.

A combined PFCM and recurrent neural network-based intrusion detection system for cloud environment

A combined PFCM and recurrent neural network-based intrusion detection system for cloud environment

Application of Temperature Prediction Based on Neural Network in Intrusion Detection of IoT

The security of network information in the Internet of Things faces enormous challenges. The traditional security defense mechanism is passive and certain loopholes. Intrusion detection can carry out network security monitoring and take corresponding measures actively. The neural network-based intrusion detection technology has specific adaptive capabilities, which can adapt to complex network environments and provide high intrusion detection rate. For the sake of solving the problem that the farmland Internet of Things is very vulnerable to invasion, we use a neural network to construct the farmland Internet of Things intrusion detection system to detect anomalous intrusion. In this study, the temperature of the IoT acquisition system is taken as the research object. It has divided which into different time granularities for feature analysis. We provide the detection standard for the data training detection module by comparing the traditional ARIMA and neural network methods. Its results show that the information on the temperature series is abundant. In addition, the neural network can predict the temperature sequence of varying time granularities better and ensure a small prediction error. It provides the testing standard for the construction of an intrusion detection system of the Internet of Things.

A Combined PFCM and Recurrent Neural Network based Intrusion Detection System for Cloud Environment

The main objective of this paper is intrusion detection system for a cloud environment using combined PFCM-RNN. Traditional IDSs are not suitable for cloud environment as network-based IDSs (NIDS) cannot detect encrypted node communication, also host-based IDSs (HIDS) are not able to find the hidden attack trail. The traditional intrusion detection is largely inefficient to be deployed in cloud computing environments due to their openness and specific essence. Accordingly, this proposed work consists of two modules namely clustering module and classification module. In clustering module, the input dataset is grouped into clusters with the use of possibilistic fuzzy C-means clustering (PFCM). In classification module, the centroid from the clusters is given to the recurrent neural network which is used to classify whether the data is intruded or not. For experimental evaluation, we use the benchmark database and the results clearly demonstrate the proposed technique outperformed conventional methods.

Neural Network based Intrusion Detection Systems

Recent Intrusion Detection Systems (IDSs) which are used to monitor real-time attacks on computer and network systems are still faced with problems of low detection rate, high false positive, high false negative and alert flooding. This paper present a Neural Network-based approach that combined supervised and unsupervised learning techniques designed to correct some of these problems. The design is divided into two phases namely: Training and Detection. In the training phase, Multiple Self–Organizing Map algorithm (SOM) was constructed to capture a number of different input patterns, discover significant features in these patterns and learn how to classify input. Sigmoid Activation Function (SAF) was used to transform the input into a reasonable value (0, 1). The learning weights were randomly assigned in the range (-1, +1) to obtain the output consistent with the training. SAF was represented using a hyperbolic tangent in order to increase the learning speed and make learning efficient. Momentum and adaptive learning rates were introduced to significantly improve the performance of the back-propagation neural network. The trained lattice of neuron was used as input in the back propagation for the real-time monitoring and detection of intrusive activities. The design was implemented in Visual Basic.Net. An evaluation was carried out using Network Traffic data collected from Defence Advanced Research Projects Agency dataset consisting of normal and intrusive traffic. The training model was performed by means of Root Mean Square (RMS) error analysis using learning rate of 0.70, 4 input layers, 8 hidden layers and 2 output layers. The evaluation result of the new design showed a promising and improved technique when compared with the recent and best known related work.

Hardware Realization of Artificial Neural Network Based Intrusion Detection & Prevention System

In the 21st century with the exponential growth of the Internet, the vulnerability of the network which connects us is on the rise at a very fast pace. Today organizations are spending millions of dollars to protect their sensitive data from different vulnerabilities that they face every day. In this paper, a new methodology towards implementing an Intrusion Detection & Prevention System (IDPS) based on Artificial Neural Network (ANN) onto Field Programmable Gate Array (FPGA) is proposed. This system not only detects different network attacks but also prevents them from being propagated. The parallel structure of an ANN makes it potentially fast for the computation of certain tasks. FPGA platforms are the optimum and best choice for the modern digital systems nowadays. The same feature makes ANN well suited for implementation in FPGA technology. Hardware realization of ANN to a large extent depends on the efficient implementation of a single neuron. However FPGA realization of ANNs with a large number of neurons is still a challenging task. The proposed multilayer ANN based IDPS uses multiple neurons for higher performance and greater accuracy. Simulation of the design in MATLAB SIMULINK 2010b by using Knowledge Discovery and Data Mining (KDD) CUP dataset shows a very good performance. Subsequently MATLAB HDL coder was used to generate VHDL code for the proposed design that produced Intellectual Property (IP) cores for Xilinx Targeted Design Platforms. For evaluation purposes the proposed design was synthesized, implemented and tested onto Xilinx Virtex-7 2000T FPGA device.

Artificial Neural Network based Intrusion Detection System: A Survey

Detecting unknown or modified attacks is one of the recent challenges in the field of IDS. Anomaly based IDS can play a very important role in this case. In the first part of this paper, we will focus on how ANN is recently used to address these issues. Number of the researchers has already shown the importance of the various Artificial Neural Network (ANN) based techniques for anomaly detection. In this paper, we will focus on Simple and Hybrid ANN based approach for anomaly detection. In simple approach we will discuss on how Back Propagation Neural Network (BPNN), Self Organizing Maps (SOM), Support Vector Machine (SVM), and Simulated Annealing Neural Network (SA) are used for anomaly detection? While in hybrid approach, we will focus on how more than one above technique are used? In the second part of the paper, we will try to compare the different ANN based techniques in terms of training time, number of the epochs required, converge rate, detection rate, learning approach, etc. Finally we will provide guidelines for the future work.

Artificial Neural Network Based Intrusion Detection Method Combined with Manifold Learning Algorithm

Computer and network security is one of the most emergency issues for a large scale of applications. The unexpected intrusion may make terrible disaster to the network users. It is therefore imperative to detect the network attacks to prevent this kind of violations. The intrusion patter recognition is now a hot topic in this research area. The use of the artificial neural networks (ANN) can provide intelligent intrusion detection. However, the intrusion detection rate is often affected by the input feature vector of the ANN. This is because the original feature space always contains a certain number of useless features. To overcome this problem, a new network intrusion detection approach based on manifold learning nonlinear feature dimension descending and ANN classifier is presented in this paper. The locally linear embedding (LLE) algorithm was used to reduce the original intrusion feature space. Then the satisfactory ANN model with proper input features was obtained. The efficiency of the proposed method was evaluated with the real intrusion data. The analysis results show that the proposed approach has good intrusion detection rate, and performs better than the standard GA-ANN method.

Neural network based intrusion detection system for detecting changes in hardware profile

To protect the information and/or the system maintaining the information is a challenge in fast growing electronic information age. Some of the technological components available in a multilayered IT security policy are Authorization, access controls, and confidentiality. The intrusion detection systems are one of the most widely accepted solutions as cryptographic protocols rely on prevention as the sole means of defense. This paper discusses neural network based intrusion detection system for detecting changes in the hardware profile. For carrying out this work, back propagation network (BPN) architecture is used. Analysis is carried out on performance log for finding out if an unauthorized access to the system has been made to change its hardware profile. Analysis of these logs shows positive results of implementing Neural Network approach for different kind of intrusions.

A novel intrusion detection mode based on understandable neural network trees

Several data mining techniques such as Hidden Markov Model (HMM), artificial neural network, statistical techniques and expert systems are used to model network packets in the field of intrusion detection. In this paper a novel intrusion detection mode based on understandable Neural Network Tree (NNTree) is presented. NNTree is a modular neural network with the overall structure being a Decision Tree (DT), and each non-terminal node being an Expert Neural Network (ENN). One crucial advantage of using NNTrees is that they keep the non-symbolic model ENN’s capability of learning in changing environments. Another potential advantage of using NNTrees is that they are actually “gray boxes” as they can be interpreted easily if the number of inputs for each ENN is limited. We showed through experiments that the trained NNTree achieved a simple ENN at each non-terminal node as well as a satisfying recognition rate of the network packets dataset. We also compared the performance with that of a three-layer backpropagation neural network. Experimental results indicated that the NNTree based intrusion detection model achieved better performance than the neural network based intrusion detection model.