An overview to Software Architecture in Intrusion Detection System

Today by growing network systems, security is a key feature of each network infrastructure. Network Intrusion Detection Systems (IDS) provide defense model for all security threats which are harmful to any network. The IDS could detect and block attack-related network traffic. The network control is a complex model. Implementation of an IDS could make delay in the network. Several software-based network intrusion detection systems are developed. However, the model has a problem with high speed traffic. This paper reviews of many type of software architecture in intrusion detection systems and describes the design and implementation of a high-performance network intrusion detection system that combines the use of software-based network intrusion detection sensors and a network processor board. The network processor which is a hardware-based model could acts as a customized load balancing splitter. This model cooperates with a set of modified content-based network intrusion detection sensors rather than IDS in processing network traffic and controls the high-speed.Comment: 8 Pages, International Journal of Soft Computing and Software Engineering [JSCSE]. arXiv admin note: text overlap with arXiv:1101.0241 by other author

SSHCure: a flow-based SSH intrusion detection system

SSH attacks are a main area of concern for network managers, due to the danger associated with a successful compromise. Detecting these attacks, and possibly compromised victims, is therefore a crucial activity. Most existing network intrusion detection systems designed for this purpose rely on the inspection of individual packets and, hence, do not scale to today's high-speed networks. To overcome this issue, this paper proposes SSHCure, a flow-based intrusion detection system for SSH attacks. It employs an efficient algorithm for the real-time detection of ongoing attacks and allows identification of compromised attack targets. A prototype implementation of the algorithm, including a graphical user interface, is implemented as a plugin for the popular NfSen monitoring tool. Finally, the detection performance of the system is validated with empirical traffic data

Reinforced Intrusion Detection Using Pursuit Reinforcement Competitive Learning

Today, information technology is growing rapidly,all information can be obtainedmuch easier. It raises some new problems; one of them is unauthorized access to the system. We need a reliable network security system that is resistant to a variety of attacks against the system. Therefore, Intrusion Detection System (IDS) required to overcome the problems of intrusions. Many researches have been done on intrusion detection using classification methods. Classification methodshave high precision, but it takes efforts to determine an appropriate classification model to the classification problem. In this paper, we propose a new reinforced approach to detect intrusion with On-line Clustering using Reinforcement Learning. Reinforcement Learning is a new paradigm in machine learning which involves interaction with the environment.It works with reward and punishment mechanism to achieve solution. We apply the Reinforcement Learning to the intrusion detection problem with considering competitive learning using Pursuit Reinforcement Competitive Learning (PRCL). Based on the experimental result, PRCL can detect intrusions in real time with high accuracy (99.816% for DoS, 95.015% for Probe, 94.731% for R2L and 99.373% for U2R) and high speed (44 ms).The proposed approach can help network administrators to detect intrusion, so the computer network security systembecome reliable.Keywords: Intrusion Detection System, On-Line Clustering, Reinforcement Learning, Unsupervised Learning

Soft fault detection using MIBs in computer networks

To improve network reliability and management in today\u27s high-speed communication system, a statistical anomaly network intrusion detection system (NIDS) has been investigated, for network soft faults using the Management Information Base (Mm) traffic parameters provided by Simple Network Management Protocol (SNMP), for both wired and wireless networks. The work done would be a contribution to a system to be designed MIB Anomaly Intrusion Detection, a hierarchical multi-tier and multiobservation-window Anomaly Intrusion Detection system. The data was derived from many experiments that had been carried out in the test bed that monitored 27 MIB traffic parameters simultaneously, focusing on the soft network faults. The work here has been focused on early detection, i.e., detection at low values of the ratio of fault to background traffic. The performance of this system would be measured using traffic intensity scenarios, as the fault traffic decreased from 10% to 0.5% of the background

Flexible and Robust Real-Time Intrusion Detection Systems to Network Dynamics

Deep learning-based intrusion detection systems have advanced due to their technological innovations such as high accuracy, automation, and scalability to develop an effective network intrusion detection system (NIDS). However, most of the previous research has focused on model generation through intensive analysis of feature engineering instead of considering real environments. They have limitations to applying the previous methods for a real network environment to detect real-time network attacks. In this paper, we propose a new flexible and robust NIDS based on Recurrent Neural Network (RNN) with a multi-classifier to generate a detection model in real time. The proposed system adaptively and intelligently adjusts the generated model with given system parameters that can be used as security parameters to defend against the attacker’s obfuscation techniques in real time. In the experimental results, the proposed system detects network attacks with a high accuracy and high-speed model upgrade in real-time while showing robustness under an attack

A Comparative Study on Performance Evaluation of Intrusion Detection System through Feature Reduction for High Speed Networks

Abstract- The rapid growth in the usage of the internet had led to many serious security issues in the network The intrusion detection system IDS is one of the sophisticated defensive systems used to detect the malicious activities happening in the network services across the world Hence more advanced IDS are been developed in past few years To improve the performance of the IDS the system has to be trained effectively to increase the efficiency and decrease the false alarm rate To train the system the attributes selection plays the major role This paper evaluates and compares the performance of the intrusion detection systems for different feature reduction techniques in high speed network

Payload-based anomaly detection in HTTP traffic

University of Technology, Sydney. Faculty of Engineering and Information Technology.Internet provides quality and convenience to human life but at the same time it provides a platform for network hackers and criminals. Intrusion Detection Systems (IDSs) have been proven to be powerful methods for detecting anomalies in the network. Traditional IDSs based on signatures are unable to detect new (zero days) attacks. Anomaly-based systems are alternative to signature based systems. However, present anomaly detection systems suffer from three major setbacks: (a) Large number of false alarms, (b) Very high volume of network traffic due to high data rates (Gbps), and (c) Inefficiency in operation. In this thesis, we address above issues and develop efficient intrusion detection frameworks and models which can be used in detecting a wide variety of attacks including web-based attacks. Our proposed methods are designed to have very few false alarms. We also address Intrusion Detection as a Pattern Recognition problem and discuss all aspects that are important in realizing an anomaly-based IDS. We present three payload-based anomaly detectors, including Geometrical Structure Anomaly Detection (GSAD), Two-Tier Intrusion Detection system using Linear Discriminant Analysis (LDA), and Real-time Payload-based Intrusion Detection System (RePIDS), for intrusion detection. These detectors perform deep-packet analysis and examine payload content using n-gram text categorization and Mahalanobis Distance Map (MDM) techniques. An MDM extracts hidden correlations between the features within each payload and among packet payloads. GSAD generates model of normal network payload as geometrical structure using MDMs in a fully automatic and unsupervised manner. We have implemented the GSAD model in HTTP environment for web-based applications. For efficient operation of IDSs, the detection speed is a key point. Current IDSs examine a large number of data features to detect intrusions and misuse patterns. Hence, for quickly and accurately identifying anomalies of Internet traffic, feature reduction becomes mandatory. We have proposed two models to address this issue, namely two-tier intrusion detection model and RePIDS. Two-tier intrusion detection model uses Linear Discriminant Analysis approach for feature reduction and optimal feature selection. It uses MDM technique to create a model of normal network payload using an extracted feature set. RePIDS uses a 3-tier Iterative Feature Selection Engine (IFSEng) to reduce dimensionality of the raw dataset using Principal Component Analysis (PCA) technique. IFSEng extracts the most significant features from the original feature set and uses mathematical and graphical methods for optimal feature subset selection. Like two-tier intrusion detection model, RePIDS then uses MDM technique to generate a model of normal network payload using extracted features. We test the proposed IDSs on two publicly available datasets of attacks and normal traffic. Experimental results confirm the effectiveness and validation of our proposed solutions in terms of detection rate, false alarm rate and computational complexity

MULTI-GIGABIT PATTERN FOR DATA IN NETWORK SECURITY

In the current scenario network security is emerging the world. Matching large sets of patterns against an incoming stream of data is a fundamental task in several fields such as network security or computational biology. High-speed network intrusion detection systems (IDS) rely on efficient pattern matching techniques to analyze the packet payload and make decisions on the significance of the packet body. However, matching the streaming payload bytes against thousands of patterns at multi-gigabit rates is computationally intensive. Various techniques have been proposed in past but the performance of the system is reducing because of multi-gigabit rates.Pattern matching is a significant issue in intrusion detection systems, but by no means the only one. Handling multi-content rules, reordering, and reassembling incoming packets are also significant for system performance. We present two pattern matching techniques to compare incoming packets against intrusion detection search patterns. The first approach, decoded partial CAM (DpCAM), pre-decodes incoming characters, aligns the decoded data, and performs logical AND on them to produce the match signal for each pattern. The second approach, perfect hashing memory (PHmem), uses perfect hashing to determine a unique memory location that contains the search pattern and a comparison between incoming data and memory output to determine the match. The suggested methods have implemented in vhdl coding and we use Xilinx for synthesis