AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments

This report considers the application of Articial Intelligence (AI) techniques to the problem of misuse detection and misuse localisation within telecommunications environments. A broad survey of techniques is provided, that covers inter alia rule based systems, model-based systems, case based reasoning, pattern matching, clustering and feature extraction, articial neural networks, genetic algorithms, arti cial immune systems, agent based systems, data mining and a variety of hybrid approaches. The report then considers the central issue of event correlation, that is at the heart of many misuse detection and localisation systems. The notion of being able to infer misuse by the correlation of individual temporally distributed events within a multiple data stream environment is explored, and a range of techniques, covering model based approaches, `programmed' AI and machine learning paradigms. It is found that, in general, correlation is best achieved via rule based approaches, but that these suffer from a number of drawbacks, such as the difculty of developing and maintaining an appropriate knowledge base, and the lack of ability to generalise from known misuses to new unseen misuses. Two distinct approaches are evident. One attempts to encode knowledge of known misuses, typically within rules, and use this to screen events. This approach cannot generally detect misuses for which it has not been programmed, i.e. it is prone to issuing false negatives. The other attempts to `learn' the features of event patterns that constitute normal behaviour, and, by observing patterns that do not match expected behaviour, detect when a misuse has occurred. This approach is prone to issuing false positives, i.e. inferring misuse from innocent patterns of behaviour that the system was not trained to recognise. Contemporary approaches are seen to favour hybridisation, often combining detection or localisation mechanisms for both abnormal and normal behaviour, the former to capture known cases of misuse, the latter to capture unknown cases. In some systems, these mechanisms even work together to update each other to increase detection rates and lower false positive rates. It is concluded that hybridisation offers the most promising future direction, but that a rule or state based component is likely to remain, being the most natural approach to the correlation of complex events. The challenge, then, is to mitigate the weaknesses of canonical programmed systems such that learning, generalisation and adaptation are more readily facilitated

Fuzzy intrusion detection

Visual data mining techniques are used to assess which metrics are most effective at detecting different types of attacks. The research confirms that data aggregation and data reduction play crucial roles in the formation of the metrics. Once the proper metrics are identified, fuzzy rules are constructed for detecting attacks in several categories. The attack categories are selected to match the different phases that intruders frequently use when attacking a system. A suite of attacks tools is assembled to test the fuzzy rules. The research shows that fuzzy rules applied to good metrics can provide an effective means of detecting a wide variety of network intrusion activity. This research is being used as a proof of concept for the development of system known as the Fuzzy Intrusion Recognition Engine (FIRE).This thesis examines the application of fuzzy systems to the problem of network intrusion detection. Historically, there have been two primary methods of performing intrusion detection: misuse detection and anomaly detection. In misuse detection, a database of attack signatures is maintained that match known intrusion activity. While misuse detection systems are very effective, they require constant updates to the signature database to remain effective or to detect distinctly new attacks. Anomaly detection systems attempt to discover suspicious behavior by comparing system activity against past usage profiles. In this research, network activity is collected and usage profiles established for a variety of metrics. A network data gathering and data analysis tool was developed to create the metrics from the network stream. Great care is given to identifying the metrics that are most suitable for detecting intrusion activity

Log Analysis Using Temporal Logic and Reconstruction Approach: Web Server Case

We present a post-mortem log analysis method based on Temporal Logic (TL), Event Processing Language (EPL), and reconstruction approach. After showing that the proposed method could be adapted to any misuse event or attack, we specifically investigate the case of web server misuses. To this end, we examine 5 different misuses on Wordpress web servers, and generate corresponding log files of these attacks for forensic analysis. Then we establish attack patterns and formalize them by means of a special case of temporal logic, i.e. many sorted first order metric temporal logic (MSFOMTL). Later on, we implement these attack patterns in the EPL, and performed experimental log analysis by using a time window mechanism sliding on sorted log records to evaluate effectiveness and efficacy of our proposed method. We found that our approach is potentially capable of providing a platform where investigators can define/store/share misuse patterns using a common language while providing fast and accurate forensic analysis on large log files

Online network intrusion detection system using temporal logic and stream data processing

These days, the world are becoming more interconnected, and the Internet has dominated the ways to communicate or to do business. Network security measures must be taken to protect the organization environment. Among these security measures are the intrusion detection systems. These systems aim to detect the actions that attempt to compromise the confidentiality, availability, and integrity of a resource by monitoring the events occurring in computer systems and/or networks. The increasing amounts of data that are transmitted at higher and higher speed networks created a challenging problem for the current intrusion detection systems. Once the traffic exceeds the operational boundaries of these systems, packets are dropped. This means that some attacks will not be detected. In this thesis, we propose developing an online network based intrusion detection system by the combined use of temporal logic and stream data processing. Temporal Logic formalisms allow us to represent attack patterns or normal behaviour. Stream data processing is a recent database technology applied to flows of data. It is designed with high performance features for data intensive applications processing. In this work we develop a system where temporal logic specifications are automatically translated into stream queries that run on the stream database server and are continuously evaluated against the traffic to detect intrusions. The experimental results show that this combination was efficient in using the resources of the running machines and was able to detect all the attacks in the test data. Additionally, the proposed solution provides a concise and unambiguous way to formally represent attack signatures and it is extensible allowing attacks to be added. Also, it is scalable as the system can benefit from using more CPUs and additional memory on the same machine, or using distributed servers

Network anomaly detection research: a survey

Data analysis to identifying attacks/anomalies is a crucial task in anomaly detection and network anomaly detection itself is an important issue in network security. Researchers have developed methods and algorithms for the improvement of the anomaly detection system. At the same time, survey papers on anomaly detection researches are available. Nevertheless, this paper attempts to analyze futher and to provide alternative taxonomy on anomaly detection researches focusing on methods, types of anomalies, data repositories, outlier identity and the most used data type. In addition, this paper summarizes information on application network categories of the existing studies

A Comprehensive Survey of Data Mining-based Fraud Detection Research

This survey paper categorises, compares, and summarises from almost all published technical and review articles in automated fraud detection within the last 10 years. It defines the professional fraudster, formalises the main types and subtypes of known fraud, and presents the nature of data evidence collected within affected industries. Within the business context of mining the data to achieve higher cost savings, this research presents methods and techniques together with their problems. Compared to all related reviews on fraud detection, this survey covers much more technical articles and is the only one, to the best of our knowledge, which proposes alternative data and solutions from related domains.Comment: 14 page