A2C: A Modular Multi-stage Collaborative Decision Framework for Human-AI Teams

This paper introduces A2C, a multi-stage collaborative decision framework designed to enable robust decision-making within human-AI teams. Drawing inspiration from concepts such as rejection learning and learning to defer, A2C incorporates AI systems trained to recognise uncertainty in their decisions and defer to human experts when needed. Moreover, A2C caters to scenarios where even human experts encounter limitations, such as in incident detection and response in cyber Security Operations Centres (SOC). In such scenarios, A2C facilitates collaborative explorations, enabling collective resolution of complex challenges. With support for three distinct decision-making modes in human-AI teams: Automated, Augmented, and Collaborative, A2C offers a flexible platform for developing effective strategies for human-AI collaboration. By harnessing the strengths of both humans and AI, it significantly improves the efficiency and effectiveness of complex decision-making in dynamic and evolving environments. To validate A2C's capabilities, we conducted extensive simulative experiments using benchmark datasets. The results clearly demonstrate that all three modes of decision-making can be effectively supported by A2C. Most notably, collaborative exploration by (simulated) human experts and AI achieves superior performance compared to AI in isolation, underscoring the framework's potential to enhance decision-making within human-AI teams

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

TOWARDS A HOLISTIC EFFICIENT STACKING ENSEMBLE INTRUSION DETECTION SYSTEM USING NEWLY GENERATED HETEROGENEOUS DATASETS

With the exponential growth of network-based applications globally, there has been a transformation in organizations\u27 business models. Furthermore, cost reduction of both computational devices and the internet have led people to become more technology dependent. Consequently, due to inordinate use of computer networks, new risks have emerged. Therefore, the process of improving the speed and accuracy of security mechanisms has become crucial.Although abundant new security tools have been developed, the rapid-growth of malicious activities continues to be a pressing issue, as their ever-evolving attacks continue to create severe threats to network security. Classical security techniquesfor instance, firewallsare used as a first line of defense against security problems but remain unable to detect internal intrusions or adequately provide security countermeasures. Thus, network administrators tend to rely predominantly on Intrusion Detection Systems to detect such network intrusive activities. Machine Learning is one of the practical approaches to intrusion detection that learns from data to differentiate between normal and malicious traffic. Although Machine Learning approaches are used frequently, an in-depth analysis of Machine Learning algorithms in the context of intrusion detection has received less attention in the literature.Moreover, adequate datasets are necessary to train and evaluate anomaly-based network intrusion detection systems. There exist a number of such datasetsas DARPA, KDDCUP, and NSL-KDDthat have been widely adopted by researchers to train and evaluate the performance of their proposed intrusion detection approaches. Based on several studies, many such datasets are outworn and unreliable to use. Furthermore, some of these datasets suffer from a lack of traffic diversity and volumes, do not cover the variety of attacks, have anonymized packet information and payload that cannot reflect the current trends, or lack feature set and metadata.This thesis provides a comprehensive analysis of some of the existing Machine Learning approaches for identifying network intrusions. Specifically, it analyzes the algorithms along various dimensionsnamely, feature selection, sensitivity to the hyper-parameter selection, and class imbalance problemsthat are inherent to intrusion detection. It also produces a new reliable dataset labeled Game Theory and Cyber Security (GTCS) that matches real-world criteria, contains normal and different classes of attacks, and reflects the current network traffic trends. The GTCS dataset is used to evaluate the performance of the different approaches, and a detailed experimental evaluation to summarize the effectiveness of each approach is presented. Finally, the thesis proposes an ensemble classifier model composed of multiple classifiers with different learning paradigms to address the issue of detection accuracy and false alarm rate in intrusion detection systems