Intrusion Detection System System Research Articles

Detecting Distributed Denial of Service Attacks Using Data Mining Techniques

Users and organizations find it continuously challenging to deal with distributed denial of service (DDoS) attacks. . The security engineer works to keep a service available at all times by dealing with intruder attacks. The intrusion-detection system (IDS) is one of the solutions to detecting and classifying any anomalous behavior. The IDS system should always be updated with the latest intruder attack deterrents to preserve the confidentiality, integrity and availability of the service. In this paper, a new dataset is collected because there were no common data sets that contain modern DDoS attacks in different network layers, such as (SIDDoS, HTTP Flood). This work incorporates three well-known classification techniques: Multilayer Perceptron (MLP), Naive Bayes and Random Forest. The experimental results show that MLP achieved the highest accuracy rate (98.63%).

Evaluating Performance of Intrusion Detection System using Support Vector Machines: Review

The basic task in intrusion detection system is to classify network activities as normal or abnormal while minimizing misclassification. In literature, various machine learning and data mining techniques have been applied to Intrusion Detection Systems (IDSs) to protect the special computer systems, vulnerable traffics cyber-attacks for computer networks. In addition, Support Vector Machine (SVM) is applied as the classification techniques in literature. However, there is a lack of review for the IDS method using SVM as the classifier. The objective of this paper is to review the contemporary literature and to provide a critical evaluation of various techniques of intrusion detection using SVM as classifier. We analyze and identify the strengths and limitations of various SVM usages as classifier in IDS systems. This paper also highlights the usefulness of SVM in IDS system for network security environment with future direction.

Signature-Based Method and Stream Data Mining Technique Performance Evaluation for Security and Intrusion Detection in Advanced Metering Infrastructures (AMI)

Advanced Metering Infrastructure is a system for collecting, measuring and analyzing power energy use which beside the mentioned parameters is responsible to transmit this information between elements of this network. One of the problems of processing in these networks and the most main installation duct is security in the network. Beside the precautionary operations used for achieving security in AMI, today the usage of intrusion detection systems (IDS) is paid attention as a second blocker defense against network security attacks. According to variations of Intrusion Detection techniques in network, many different designs for applying IDS and AMI have been suggested so far. In this paper we aim to evaluate the newest techniques of operation in abnormality-based IDS in Intrusion Detection and security achievement in AMI against signature-based IDS operation with the use of standard criteria in equal situations experimentally. According to the results from this research we can see that the signature-based methods are not useful in IDS systems applied in AMI network. The measurement on anomaly-based IDS which is using data mining techniques showed very hopeful results. However these methods need some improvements in order to achieve their best place.

Network Based Distributed Intelligent Intrusion Detecting System

The breach of security barriers of the computer systems has always been a greater concern in the information technology era. The Intrusions and Attacks seem to come from different angles and in different type everyday. It has been a critical battle to keep phase with the ever increasingly mounting threats to the computer Systems. Intruders analyze the computer systems through networks for any of possible loop-holes and vulnerabilities. The intruders exploit these compromising vulnerabilities to mount the attacks. The existing Intrusion Detection Systems (IDS) are not efficient and intelligent enough to detect the new type of attacks. The signature based IDSs detect the known type where as the misuse detection based IDSs producing a huge amount of false alarm and still needs administrator assistance. Traditional intrusion detection systems are increasingly limited by their need of an up-to-date and comprehensive knowledge base. The need for an Intelligent IDS is very essential at this point and I would like to introduce a Network-Based Distributed IDS with the capability to learn and detect the new type of attacks since novel architecture of proposed Intelligent IDS. This IDS will focus on overcoming the most prominent drawback of the existing IDS systems and may be a new step forward in a new direction since the success architecture.

A CONTROL NODE FOR INTRUSION DETECTION SYSTEMS MANAGEMENT

Currently, several systems for Intrusion Detection System (IDS) management exist, however they are suffering from numerous downfalls; the fact they mostly focus on the visualization of gathered data and not on the management itself (which is, in fact, the critical part) being the main one. The goal of this work is to develop a solution for IDS management that would simplify the usage and provide greater efficiency when detecting intrusions, thus providing the overall improvement of the system security. This article concerns about the analysis of current IDS solutions and their management tools, the architecture of our solution and the evaluation of the solution based on this architecture. The expected results improve the efficiency of an IDS system and also of the whole system security itself.

Methods of the Data Mining and Machine Learning in Computer Security

Due to the expansion of high-speed Internet access, the need for secure and reliable networks has become more critical. Since attacks are becoming more sophisticated and networks are becoming larger there is a need for an efficient intrusion detection systems (IDSs) that can distinguish between legitimate and illegitimate traffic and be able to signal attacks in real time, before serious damages are produced. Although there are some existing mechanisms for intrusion detection, there is need to improve the performance. Data mining techniques and machine learning are a new approach for intrusion detection. In this work naive Bayes classifier and decision trees with C4.5 and CART algorithms for detecting abnormal traffic patterns in the KDD Cup 1999 data are used. The IDS system is supposed to distinguish normal traffic from intrusions and to classify the intrusions into five classes: Normal, DoS, probe, R2L and U2R. Research shows that decision trees gives better overall performance than the naive Bayes classifier.

An Adaptive Hybrid Multi-level Intelligent Intrusion Detection System for Network Security

Intrusion Detection System (IDS) plays a vital factor in providing security to the networks through detecting malicious activities. Due to the extensive advancements in the computer networking, IDS has become an active area of research to determine various types of attacks in the networks. A large number of intrusion detection approaches are available in the literature using several traditional statistical and data mining approaches. Data mining techniques in IDS observed to provide significant results. Data mining approaches for misuse and anomaly-based intrusion detection generally include supervised, unsupervised and outlier approaches. It is important that the efficiency and potential of IDS be updated based on the criteria of new attacks. This study proposes a novel Adaptive Hybrid Multi-level Intelligent IDS (AHMIIDS) system which is the combined version of anomaly and misuse detection techniques. The anomaly detection is based on Bayesian Networks and then the misuse detection is performed using Adaptive Neuro Fuzzy Inference System (ANFIS). The outputs of both anomaly detection and misuse detection modules are applied to Decision Table Majority (DTM) to perform the final decision making. A rule-base approach is used in this system. It is observed from the results that the proposed AHMIIDS performs better than other conventional hybrid IDS.

Evaluation of Web Security Mechanisms Using Inline Scenario & Online Scenario

In today's world there is large amount use of computer especially for web application. Most of the people do their transaction through web application. So there are chances of personal data gets hacked then need to be provide more security for both web server and database server. For that double guard system is used. The double guard system is used to detect and prevent attacks using Intrusion detection system.This system prevents attacks and prevents user account from intruder from hacking his/her account. By using IDS, system can provide security for both web server and database server using mapping of request and query. An IDS system that models the network behavior of user sessions across both the front-end web server and the back-end database. This system able to search for in a place (container) attacks that previous Intrusion Detection System (IDS) would not be able to identify. System will try this by isolating the flow of information from each web server session. It quantify the detection accuracy when system attempt to model static and dynamic web requests with the back-end file system and database queries. For static websites, system built a well-correlated model, for effectively detecting different types of attacks. Moreover, system showed that this held true for dynamic requests where both retrieval of information and updates to the back-end database occur using the web- server front end.

Distributed node level security monitoring system for mobile ad hoc networks

In mobile ad hoc networks (MANET), the existing distributed intrusion detection systems (IDS) are not completely distributed in its design. Though the data is collected from the distributed nodes, IDS analyse them centrally. In this paper, we propose a distributed node level security monitoring system for detecting and isolating attackers in mobile ad hoc networks. In the traffic flow-monitoring phase, behaviour of neighbour node is monitored compared with the pattern table for abnormal behaviour. In trust estimation phase, global trust value is estimated for each node in the chosen path based on direct and indirect trust values. If the trust value of any node is below threshold value, the node will be authenticated using the secret sharing technique and data is rerouted towards alternate path for the attacked path. From the simulation results, we show that our proposed system performs better than the existing IDS systems for MANET.

Support Vector Machine and Random Forest Modeling for Intrusion Detection System (IDS)

The success of any Intrusion Detection System (IDS) is a complicated problem due to its nonlinearity and the quantitative or qualitative network traffic data stream with many features. To get rid of this problem, several types of intrusion detection methods have been proposed and shown different levels of accuracy. This is why the choice of the effective and robust method for IDS is very important topic in information security. In this work, we have built two models for the classification purpose. One is based on Support Vector Machines (SVM) and the other is Random Forests (RF). Experimental results show that either classifier is effective. SVM is slightly more accurate, but more expensive in terms of time. RF produces similar accuracy in a much faster manner if given modeling parameters. These classifiers can contribute to an IDS system as one source of analysis and increase its accuracy. In this paper, KDD’99 Dataset is used and find out which one is the best intrusion detector for this dataset. Statistical analysis on KDD’99 dataset found important issues which highly affect the performance of evaluated systems and results in a very poor evaluation of anomaly detection approaches. The most important deficiency in the KDD’99 dataset is the huge number of redundant records. To solve these issues, we have developed a new dataset, KDD99Train+ and KDD99Test+, which does not include any redundant records in the train set as well as in the test set, so the classifiers will not be biased towards more frequent records. The numbers of records in the train and test sets are now reasonable, which make it affordable to run the experiments on the complete set without the need to randomly select a small portion. The findings of this paper will be very useful to use SVM and RF in a more meaningful way in order to maximize the performance rate and minimize the false negative rate.

FPGA-Based Network Traffic Security: Design and Implementation Using C5.0 Decision Tree Classifier

In this work, a hardware intrusion detection system (IDS) model and its implementation are introduced to perform online real-time traffic monitoring and analysis. The introduced system gathers some advantages of many IDSs:hardware based from implementation point of view, network based from system type point of view, and anomaly detection from detection approach point of view. In addition, it can detect most of network attacks, such as denial of services (DoS), leakage, etc. from detection behavior point of view and can detect both internal and external intruders from intruder type point of view. Gathering these features in one IDS system gives lots of strengths and advantages of the work. The system is implemented by using field programmable gate array (FPGA), giving a more advantages to the system. A C5.0 decision tree classifier is used as inference engine to the system and gives a high detection ratio of 99.93%.

Comparative Analysis of Intrusion Detection System with Mining

Intrusion Detection System (IDS) is a system that monitors the network activities for a suspicious event. Suspiciousness in the event cannot be identified by a single activity. Set of activities that crosses the limit of normal behavior is considered as an intrusion. There are many methods that provide security like authorization, authentication, and encryption. But all these methods decide the security issue by a single activity so that the slow intrusion is not identified. In this paper, we review the existing real time IDS models that capture the slow poisoning of the network by using improved data mining algorithms. Normal IDS has the issues like low accuracy, high false negative rates. This system suggests the high accuracy IDS system with less false positives.

Increasing the Efficiency of IDS Systems by Hardware Implementation of Packet Capturing

Capturing is the first step in intrusion detection system (IDS). Having wire speed, omitting the OS fro m capturing process and no need for making a copy of packets from the system's environ ment to the user's environment are some o f the system characteristics. If these requirements are not met, packet capture system is considered as the main bottleneck of IDS and the overall efficiency of this system will be influenced. Presence of all these three characteristics calls for utilizat ion of hardware methods. In this paper, by using of FPGA, a line sniffing and load balancing system are designed in order to be applied in IDS systems. The main contribution of our work is the feasibility of attaching labels to the beginning part of each packet, aiming at quick easy access of other IDS modules to information of each packet and also reducing workload of these modules. Packet classification in the proposed system can be configured to 2, 3, and 5 tuple, wh ich can also be applied in IDS detection module in addition to load balancing part of this system. Load balancing module uses Hash table and its Hash function has the least flows collisions. This system is imp lemented on a set of virtex 6 and 7 families and is ab le to capture packets 100% and perform the above mentioned processes by speed of 12 Gbit/s.

Intrusion Detection System with Multi Layer using Bayesian Networks

the era of network security, intrusion detection system plays a vital to detect real - time intrusions, and to execute work to stop the attack. Being everything shifting to internet, security became the foremost preference. In real world, the minority attacks R2L (Remote-To-User) and U2R (User-To- Root) are more hazardous than Probe and DoS (Denial-Of- Service) majority attacks. Present IDS are not much efficient to detect these low level attacks. Therefore, it is extremely important to improve the detection performance for the R2L and U2R attacks with the majority attacks. In this paper hierarchical layered approach for improving detection rate of minority attacks as well as majority attacks is propound. The propound model used Naive bayes classifier with K2 learning process on reduced NSL KDD dataset for each attack class. In this method every layer is individually trained to detect a single type of attack category and the outcome of one layer is passed into another layer to increase the detection rate and for better categorization of both the majority and minority attacks. Intrusion detection system (IDS), Network security, Feature selection, naive bayes classifier, R2U, U2R, DoS 1. INTRODUCTIONNetwork Security Can be defined as It is the set of guidelines adopted by a Network administrator to prevent the unauthorized access, modification and misuse of the network traffic by attackers.ID (Intrusion Detection) techniques are used to strength security and increase resistance to internal and external attacks. With the Increase in Technology the chance of malware and Intrusions get increase. The firewalls do not provide much security. In order to inspect network traffic many corporation are using proactive and reactive both types of IDS system. The goal of the intrusion detection system is to monitor and control the network traffic to prevent the numerous types of intrusion attempt. When an intrusion detection system is deployed, it provides warning to the users indicating that system is under attack. These warnings can help users to prevent the attack by increasing resistance to attack.

A comparative performance evaluation of intrusion detection techniques for hierarchical wireless sensor networks

An explosive growth in the field of wireless sensor networks (WSNs) has been achieved in the past few years. Due to its important wide range of applications especially military applications, environments monitoring, health care application, home automation, etc., they are exposed to security threats. Intrusion detection system (IDS) is one of the major and efficient defensive methods against attacks in WSN. Therefore, developing IDS for WSN have attracted much attention recently and thus, there are many publications proposing new IDS techniques or enhancement to the existing ones. This paper evaluates and compares the most prominent anomaly-based IDS systems for hierarchical WSNs and identifying their strengths and weaknesses. For each IDS, the architecture and the related functionality are briefly introduced, discussed, and compared, focusing on both the operational strengths and weakness. In addition, a comparison of the studied IDSs is carried out using a set of critical evaluation metrics that are divided into two groups; the first one related to performance and the second related to security. Finally based on the carried evaluation and comparison, a set of design principles are concluded, which have to be addressed and satisfied in future research of designing and implementing IDS for WSNs.

Intrusion Detection System using Bayesian Approach for Wireless Network

An Intrusion Detection System (IDS) is a software or hardware tool used to detect unauthorized access of a computer system or network. A wireless IDS performs this task exclusively for the wireless network. These systems monitor traffic on your network looking for and logging threats and alerting personnel to respond. An IDS usually performs this task in two ways, with either signature-based or anomaly based detection. Almost every IDS today is at least in part signature-based. This means that known attacks can be detected by looking for these signatures. The other approach is anomaly-based systems. These are not often implemented, mostly because of the high amount of false alarms. It detects traffic which deviates from what it considers normal an alert is generated. The traditional IDS system is not work well for wireless network, but the wireless network is more vulnerable than a wired network. A major problem with current IDS that employs Bayesian network is they give a series of false alarms in system environment modification. There are two types of false alarms in determining the any deviations from normal pattern: false positive and false negative. The main goal is to keep these alarms as low as possible. So a BN is used to build automatic intrusion detection system based on signature recognition. The goal is to recognize signatures of known attacks, match the observed behavior with those known signatures, and signal intrusion when there is a match. IDS must be able to adapt to these changes. The goal is to provide a framework for an adaptive intrusion detection system that uses Bayesian network

A novel intrusion detection system by using intelligent data mining in weka environment

Nowadays, the using of intelligent data mining approaches to predict intrusion in local area networks has been increasing rapidly. In this paper, an improved approach for Intrusion Detection System (IDS) based on combining data mining and expert system is presented and implemented in WEKA. The taxonomy consists of a classification of the detection principle as well as certain WEKA aspects of the intrusion detection system such as open-source data mining. The combining methods may give better performance of IDS systems, and make the detection more effective. The result of the evaluation of the new design produced a better result in terms of detection efficiency and false alarm rate from the existing problems. This presents useful information in intrusion detection.

Dynamic Multi-Layer Signature Based Intrusion Detection System Using Mobile Agents

Intrusion detection systems have become a key component in ensuring the safety of systems and networks. As networks grow in size and speed continues to increase, it is crucial that efficient scalable techniques should be developed for IDS systems. Signature based detection is the most extensively used threat detection technique for Intrusion Detection Systems (IDS). One of the foremost challenges for signaturebased IDS systems is how to keep up with large volume of incoming traffic when each packet needs to be compared with every signature in the database. When an IDS cannot keep up with the traffic flood, all it can do is to drop packets, therefore, may miss potential attacks. This paper proposes a new model called Dynamic Multi-Layer Signature based IDS using Mobile Agents, which can detect imminent threats with very high success rate by dynamically and automatically creating and using small and efficient multiple databases, and at the same time, provide mechanism to update these small signature databases at regular intervals using Mobile Agents.

Intrusion detections systems – an outmoded network protection model

Intrusion detection systems (IDS) are a common component of computer network defences, but their cost usually outweighs their effectiveness and they give a false sense of security says this article. They attempt to detect malicious activity, but were designed when firewalls were at an earlier stage in their development and use techniques - signatures and noise - which generate resource-consuming false positives. IDS systems are cumbersome, merely reactive, and entail high, often hidden and unnecessary costs for industry, but are often retained because bizarrely they meet auditing requirements. With current technology, the way forward is with intrusion protection pystems (IPS) which are less expensive, preventative and much more sensitive to context and traffic patterns. Intrusion detection systems have become almost a standard element of most modern computer network defence in-depth solutions, but are they really worth their weight in bureaucracy?

Intrusion detection using a linguistic hedged fuzzy-XCS classifier system

Intrusion detection systems (IDS) are a fundamental defence component in the architecture of the current telecommunication systems. Misuse detection is one of the different approaches to create IDS. It is based on the automatic generation of detection rules from labelled examples. Such examples are either attacks or normal situations. From this perspective the problem can be viewed as a supervised classification one. In this sense, this paper proposes the use of XCS as a classification technique to aid in the tasks of misuse detection in IDS systems. The final proposed XCS variant includes the use of hedged linguistic fuzzy classifiers to allow for interpretability. The use of this linguistic fuzzy approach provides with both the possibility of testing human designed detectors and a posteriori human fine tuning of the models obtained. To evaluate the performance not only several classic classification problems as Wine or Breast Cancer datasets are considered, but also a problem based on real data, the KDD-99. This latter problem, the KDD-99, is a classic in the literature of intrusion systems. It shows that with simple configurations the proposed variant obtains competitive results compared with other techniques shown in the recent literature. It also generates human interpretable knowledge, something very appreciated by security experts. In fact, this effort is integrated into a global detection architecture, where the security administrator is guiding part of the intrusion detection (and prevention) process.