E-commerce security enhancement and anomaly intrusion detection using machine learning techniques

With the fast growth of the Internet and the World Wide Web, security has become a major concern of many organizations, enterprises and users. Criminal attacks and intrusions into computer and information systems are spreading quickly and they can come from anywhere on the globe. Intrusion prevention measures, such as user authentication, firewalls and cryptography have been used as the first line of defence to protect computer and information systems from intrusions. As intrusion prevention alone may not be sufficient in a highly dynamic environment, such as the Internet, intrusion detection has been used as the second line of defence against intrusions. However, existing cryptography-based intrusion prevention measures implemented in software, have problems with the protection of long-term private keys and the degradation of system performance. Moreover, the security of these software-based intrusion prevention measures depends on the security of the underlying operating system, and therefore they are vulnerable to threats caused by security flaws of the underlying operating system. On the other hand, existing anomaly intrusion detection approaches usually produce excessive false alarms. They also lack in efficiency due to high construction and maintenance costs. In our approach, we employ the "defence in depth" principle to develop a solution to solve these problems. Our solution consists of two lines of defence: preventing intrusions at the first line and detecting intrusions at the second line if the prevention measures of the first line have been penetrated. At the first line of defence, our goal is to develop an encryption model that enhances communication and end-system security, and improves the performance of web-based E-commerce systems. We have developed a hardware-based RSA encryption model to address the above mentioned problems of existing software-based intrusion prevention measures. The proposed hardware-based encryption model is based on the integration of an existing web-based client/server model and embedded hardware-based RSA encryption modules. DSP embedded hardware is selected to develop the proposed encryption model because of its advanced security features and high processing capability. The experimental results showed that the proposed DSP hardware-based RSA encryption model outperformed the software-based RSA implementation running on Pentium 4 machines that have almost double clock speed of the DSP's clock speed at large RSA encryption keys. At the second line of defence, our goal is to develop an anomaly intrusion detection model that improves the detection accuracy, efficiency and adaptability of existing anomaly detection approaches. Existing anomaly detection systems are not effective as they usually produce excessive false alarms. In addition, several anomaly detection approaches suffer a serious efficiency problem due to high construction costs of the detection profiles. High construction costs will eventually reduce the applicability of these approaches in practice. Furthermore, existing anomaly detection systems lack in adaptability because no mechanisms are provided to update their detection profiles dynamically, in order to adapt to the changes of the behaviour of monitored objects. We have developed a model for program anomaly intrusion detection to address these problems. The proposed detection model uses a hidden Markov model (HMM) to characterize normal program behaviour using system calls. In order to increase the detection rate and to reduce the false alarm rate, we propose two detection schemes: a two-layer detection scheme and a fuzzy-based detection scheme. The two-layer detection scheme aims at reducing false alarms by applying a double-layer test on each sequence of test traces of system calls. On the other hand, the fuzzy-based detection scheme focuses on further improving the detection rate, as well as reducing false alarms. It employs the fuzzy inference to combine multiple sequence information to correctly determine the sequence status. The experimental results showed that the proposed detection schemes reduced false alarms by approximately 48%, compared to the normal database scheme. In addition, our detection schemes generated strong anomaly signals for all tested traces, which in turn improve the detection rate. We propose an HMM incremental training scheme with optimal initialization to address the efficiency problem by reducing the construction costs, in terms of model training time and storage demand. Unlike the HMM batch training scheme, which updates the HMM model using the complete training set, our HMM incremental training scheme incrementally updates the HMM model using one training subset at a time, until convergence. The experimental results showed that the proposed HMM incremental training scheme reduced training time four-fold, compared to the HMM batch training, based on the well-known Baum-Welch algorithm. The proposed training scheme also reduced storage demand substantially, as the size of each training subset is significantly smaller than the size of the complete training set. We also describe our complete model for program anomaly detection using system calls in chapter 8. The complete model consists of two development stages: training stage and testing stage. In the training stage, an HMM model and a normal database are constructed to represent normal program behaviour. In addition, fuzzy sets and rules are defined to represent the space and combined conditions of the sequence parameters. In the testing stage, the HMM model and the normal database, are used to generate the sequence parameters which are used as the input for the fuzzy inference engine to evaluate each sequence of system calls for anomalies and possible intrusions. The proposed detection model also provides a mechanism to update its detection profile (the HMM model and the normal database) using online training data. This makes the proposed detection model up-to-date, and therefore, maintains the detection accuracy

Using the Pattern-of-Life in Networks to Improve the Effectiveness of Intrusion Detection Systems

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.As the complexity of cyber-attacks keeps increasing, new and more robust detection mechanisms need to be developed. The next generation of Intrusion Detection Systems (IDSs) should be able to adapt their detection characteristics based not only on the measureable network traffic, but also on the available high- level information related to the protected network to improve their detection results. We make use of the Pattern-of-Life (PoL) of a network as the main source of high-level information, which is correlated with the time of the day and the usage of the network resources. We propose the use of a Fuzzy Cognitive Map (FCM) to incorporate the PoL into the detection process. The main aim of this work is to evidence the improved the detection performance of an IDS using an FCM to leverage on network related contextual information. The results that we present verify that the proposed method improves the effectiveness of our IDS by reducing the total number of false alarms; providing an improvement of 9.68% when all the considered metrics are combined and a peak improvement of up to 35.64%, depending on particular metric combination

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