Survey of Intrusion Detection Research

The literature holds a great deal of research in the intrusion detection area. Much of this describes the design and implementation of specific intrusion detection systems. While the main focus has been the study of different detection algorithms and methods, there are a number of other issues that are of equal importance to make these systems function well in practice. I believe that the reason that the commercial market does not use many of the ideas described is that there are still too many unresolved issues. This survey focuses on presenting the different issues that must be addressed to build fully functional and practically usable intrusion detection systems (IDSs). It points out the state of the art in each area and suggests important open research issues

An Historical Analysis of Factors Contributing to the Emergence of the Intrusion Detection Discipline and its Role in Information Assurance

In 2003, Gartner, Inc., predicted the inevitable demise of the intrusion detection (ID) market, a major player in the computer security technology industry. In light of this prediction, IT executives need to know if intrusion detection technologies serve a strategic purpose within the framework of information assurance (IA). This research investigated the historical background and circumstances that led to the birth of the intrusion detection field and explored the evolution of the discipline through current research in order to identify appropriate roles for IDS technology within an information assurance framework. The research identified factors contributing to the birth of ID including increased procurement and employment of resource-sharing computer systems in the DoD, a growing need to operate in an open computing environment while maintaining security and the unmanageable volume of audit data produced as a result of security requirements. The research also uncovered six trends that could be used to describe the evolution of the ID discipline encompassing passive to active response mechanisms, centralized to distributed management platforms, centralized to distributed/agent-based detection, single to multiple detection approaches within a system, host-based to network to hybrid analysis and software-based to hardware-based/in-line devices. Finally, the research outlined three roles suitable for IDS to fulfill within the IA framework including employing IDS as a stimulus to incident response mechanisms, as a forensic tool for gathering evidence of computer misuse and as a vulnerability assessment or policy enforcement facility

A hybrid intrusion detection system

Anomaly intrusion detection normally has high false alarm rates, and a high volume of false alarms will prevent system administrators identifying the real attacks. Machine learning methods provide an effective way to decrease the false alarm rate and improve the detection rate of anomaly intrusion detection. In this research, we propose a novel approach using kernel methods and Support Vector Machine (SVM) for improving anomaly intrusion detectors\u27 accuracy. Two kernels, STIDE kernel and Markov Chain kernel, are developed specially for intrusion detection applications. The experiments show the STIDE and Markov Chain kernel based two class SVM anomaly detectors have better accuracy rate than the original STIDE and Markov Chain anomaly detectors.;Generally, anomaly intrusion detection approaches build normal profiles from labeled training data. However, labeled training data for intrusion detection is expensive and not easy to obtain. We propose an anomaly detection approach, using STIDE kernel and Markov Chain kernel based one class SVM, that does not need labeled training data. To further increase the detection rate and lower the false alarm rate, an approach of integrating specification based intrusion detection with anomaly intrusion detection is also proposed.;This research also establish a platform which generates automatically both misuse and anomaly intrusion detection software agents. In our method, a SIFT representing an intrusion is automatically converted to a Colored Petri Net (CPNs) representing an intrusion detection template, subsequently, the CPN is compiled into code for misuse intrusion detection software agents using a compiler and dynamically loaded and launched for misuse intrusion detection. On the other hand, a model representing a normal profile is automatically generated from training data, subsequently, an anomaly intrusion detection agent which carries this model is generated and launched for anomaly intrusion detection. By engaging both misuse and anomaly intrusion detection agents, our system can detect known attacks as well as novel unknown attacks

Feature selection and visualization techniques for network anomaly detector

Intrusion detection systems have been widely used as burglar alarms in the computer security field. There are two major types of detection techniques: misuse detection and anomaly detection. Although misuse detection can detect known attacks with lower false positive rate, anomaly detection is capable of detecting any new or varied attempted intrusion as long as the attempted intrusions disturb the normal states of the systems. The network anomaly detector is employed to monitor a segment of network for any suspicious activities based on the sniffered network traffic. The fast speed of network and wide use of encryption techniques make it almost unpractical to read payload information for the network anomaly detector. This work tries to answer the question: What are the best features for network anomaly detector? The main experiment data sets are from 1999 DARPA Lincoln Library off-line intrusion evaluation project since it is still the most comprehensive public benchmark data up to today. Firstly, 43 features of different levels and protocols are defined. Using the first three weeks as training data and last two weeks as testing data, the performance of the features are testified by using 5 different classifiers. Secondly, the feasibility of feature selection is investigated by employing some filter and wrapper techniques such as Correlation Feature Selection, etc. Thirdly, the effect of changing overlap and time window for the network anomaly detector is investigated. At last, GGobi and Mineset are utilized to visualize intrusion detections to save time and effort for system administrators. The results show the capability of our features is not limited to probing attacks and denial of service attacks. They can also detect remote to local attacks and backdoors. The feature selection techniques successfully reduce the dimensionality of the features from 43 to 10 without performance degrading. The three dimensional visualization pictures provide a straightforward view of normal network traffic and malicious attacks. The time plot of key features can be used to aid system administrators to quickly locate the possible intrusions

Exploring anti-corruption capabilities of e-procurement in construction project delivery in Nigeria

The use of electronic (e-­) procurement to support the execution of supply chain management activities in the different industrial sectors is permeating all regions of the world. However, in countries in sub-Saharan Africa where there is a significant level of corruption and unethical practices in the procurement process, there is a need for a better understanding of how e-Procurement can help to check the incidence of corrupt and unethical practices in construction project delivery. This study relied on a cross-sectional survey of 759 respondents, including architects, builders, engineers, estate/facilities managers, contractors, construction/project managers, quantity surveyors, supply chain managers and others to identify and analyse the anti-corruption capabilities of e-Procurement in construction project delivery in Nigeria. The results of the descriptive statistics, relative importance index and principal components analysis identified 18 anti-corruption capabilities in e-Procurement in construction project delivery with the three most important ones being the capability of e-Procurement to ensure good inventory management/record keeping; accountability by providing audit services trail and minimise direct human contacts during bidding. The key underlying dimensions of these capabilities include the advantage of e-Procurement over the traditional paper-based method; transparent bidding process and increase in competition in construction project delivery process. The findings of this study have implications, especially, on the use of e-Procurement to curb corruption in construction procurement activities

Attack visualization for intrusion detection system

Attacks detection and visualization is the process of attempting to identify instances of network misuse by comparing current activity against the expected actions of an intruder. Most current approaches to attack detection involve the use of rule-based expert systems to identify indications of known attacks. However, these techniques are less successful in identifying attacks, which vary from expected patterns. Artificial neural networks provide the potential to identify and classify network activity based on limited, incomplete, and nonlinear data sources. Presenting an approach to the process of Attack visualization that utilizes the analytical strengths of neural networks, and providing the results from a preliminary analysis of the network parameters being watched like Internet Protocol (IP) packet length, packet traffic, IP byte traffic, IP packet rate, IP byte rate, User Datagram Protocol (UDP) packet length, UDP packet traffic, UDP byte traffic, UDP packet rate, UDP byte rate, Heart Beat (HB) End-to-end delay, and HB Packet loss rate. Beside collected attack data, numerical simulated data was generated using the neural network sigmoids with Matlab. The characteristics of the obtained data showed lots of similarities with the actual collected network data. Further work is continuing to obtain different attack data using the Opnet simulating program

Application of a Layered Hidden Markov Model in the Detection of Network Attacks

Network-based attacks against computer systems are a common and increasing problem. Attackers continue to increase the sophistication and complexity of their attacks with the goal of removing sensitive data or disrupting operations. Attack detection technology works very well for the detection of known attacks using a signature-based intrusion detection system. However, attackers can utilize attacks that are undetectable to those signature-based systems whether they are truly new attacks or modified versions of known attacks. Anomaly-based intrusion detection systems approach the problem of attack detection by detecting when traffic differs from a learned baseline. In the case of this research, the focus was on a relatively new area known as payload anomaly detection. In payload anomaly detection, the system focuses exclusively on the payload of packets and learns the normal contents of those payloads. When a payload\u27s contents differ from the norm, an anomaly is detected and may be a potential attack. A risk with anomaly-based detection mechanisms is they suffer from high false positive rates which reduce their effectiveness. This research built upon previous research in payload anomaly detection by combining multiple techniques of detection in a layered approach. The layers of the system included a high-level navigation layer, a request payload analysis layer, and a request-response analysis layer. The system was tested using the test data provided by some earlier payload anomaly detection systems as well as new data sets. The results of the experiments showed that by combining these layers of detection into a single system, there were higher detection rates and lower false positive rates

Feature Subset Selection in Intrusion Detection Using Soft Computing Techniques

Intrusions on computer network systems are major security issues these days. Therefore, it is of utmost importance to prevent such intrusions. The prevention of such intrusions is entirely dependent on their detection that is a main part of any security tool such as Intrusion Detection System (IDS), Intrusion Prevention System (IPS), Adaptive Security Alliance (ASA), checkpoints and firewalls. Therefore, accurate detection of network attack is imperative. A variety of intrusion detection approaches are available but the main problem is their performance, which can be enhanced by increasing the detection rates and reducing false positives. Such weaknesses of the existing techniques have motivated the research presented in this thesis. One of the weaknesses of the existing intrusion detection approaches is the usage of a raw dataset for classification but the classifier may get confused due to redundancy and hence may not classify correctly. To overcome this issue, Principal Component Analysis (PCA) has been employed to transform raw features into principal features space and select the features based on their sensitivity. The sensitivity is determined by the values of eigenvalues. The recent approaches use PCA to project features space to principal feature space and select features corresponding to the highest eigenvalues, but the features corresponding to the highest eigenvalues may not have the optimal sensitivity for the classifier due to ignoring many sensitive features. Instead of using traditional approach of selecting features with the highest eigenvalues such as PCA, this research applied a Genetic Algorithm (GA) to search the principal feature space that offers a subset of features with optimal sensitivity and the highest discriminatory power. Based on the selected features, the classification is performed. The Support Vector Machine (SVM) and Multilayer Perceptron (MLP) are used for classification purpose due to their proven ability in classification. This research work uses the Knowledge Discovery and Data mining (KDD) cup dataset, which is considered benchmark for evaluating security detection mechanisms. The performance of this approach was analyzed and compared with existing approaches. The results show that proposed method provides an optimal intrusion detection mechanism that outperforms the existing approaches and has the capability to minimize the number of features and maximize the detection rates