Operational Decision Making under Uncertainty: Inferential, Sequential, and Adversarial Approaches

Modern security threats are characterized by a stochastic, dynamic, partially observable, and ambiguous operational environment. This dissertation addresses such complex security threats using operations research techniques for decision making under uncertainty in operations planning, analysis, and assessment. First, this research develops a new method for robust queue inference with partially observable, stochastic arrival and departure times, motivated by cybersecurity and terrorism applications. In the dynamic setting, this work develops a new variant of Markov decision processes and an algorithm for robust information collection in dynamic, partially observable and ambiguous environments, with an application to a cybersecurity detection problem. In the adversarial setting, this work presents a new application of counterfactual regret minimization and robust optimization to a multi-domain cyber and air defense problem in a partially observable environment

End-to-end anomaly detection in stream data

Nowadays, huge volumes of data are generated with increasing velocity through various systems, applications, and activities. This increases the demand for stream and time series analysis to react to changing conditions in real-time for enhanced efficiency and quality of service delivery as well as upgraded safety and security in private and public sectors. Despite its very rich history, time series anomaly detection is still one of the vital topics in machine learning research and is receiving increasing attention. Identifying hidden patterns and selecting an appropriate model that fits the observed data well and also carries over to unobserved data is not a trivial task. Due to the increasing diversity of data sources and associated stochastic processes, this pivotal data analysis topic is loaded with various challenges like complex latent patterns, concept drift, and overfitting that may mislead the model and cause a high false alarm rate. Handling these challenges leads the advanced anomaly detection methods to develop sophisticated decision logic, which turns them into mysterious and inexplicable black-boxes. Contrary to this trend, end-users expect transparency and verifiability to trust a model and the outcomes it produces. Also, pointing the users to the most anomalous/malicious areas of time series and causal features could save them time, energy, and money. For the mentioned reasons, this thesis is addressing the crucial challenges in an end-to-end pipeline of stream-based anomaly detection through the three essential phases of behavior prediction, inference, and interpretation. The first step is focused on devising a time series model that leads to high average accuracy as well as small error deviation. On this basis, we propose higher-quality anomaly detection and scoring techniques that utilize the related contexts to reclassify the observations and post-pruning the unjustified events. Last but not least, we make the predictive process transparent and verifiable by providing meaningful reasoning behind its generated results based on the understandable concepts by a human. The provided insight can pinpoint the anomalous regions of time series and explain why the current status of a system has been flagged as anomalous. Stream-based anomaly detection research is a principal area of innovation to support our economy, security, and even the safety and health of societies worldwide. We believe our proposed analysis techniques can contribute to building a situational awareness platform and open new perspectives in a variety of domains like cybersecurity, and health

Formulating the Cyber Security Culture in Organizations: Proposing and Arguing Insights

Purpose: This research aims to enhance practical organizational practices and academic research literature by critically investigating the latest findings in cybersecurity culture research through a systematic review of relevant literature and research.   Theoretical Framework:This work seeks to summarize key research developments in a research area that remains challenging for companies as they seek to build strong security cultures to protect their information (Tripwire, 2020). And reviewing the legal regulations that must be trained to protect institutions from cyber threats in the Kingdom of Bahrain and Saudi Arabia.   Design/Methodology/Approach: The methodology of this study implements a systematic literature review to assess the main components of cybersecurity culture and what good practice can help to build it professionally.    Findings: The main results find that current literature must move from a technical approach to information security to a socio-cultural one. Also, this study predicts that cybercrime will increase dramatically and cost the world trillions annually.    Research Practical and Social Implications: this study attempts to define human resource management's role in cybersecurity awareness training and therfore the managers can deveplo the necessary rules to secure the organizational information.   Originality/Value: The study is within the first studies  to  be  conducted  in  GCC countries.  Moreover, the  to build a cyber security culture is unique topic add on to the academic knowledge. Also, can motivate the future studies to focus on efficiently organizing security procedures and enhancing security readiness appraisal consequences by providing more perceptions of imminent threats and security hazards.

Establishing cyber situational awareness in industrial control systems

The cyber threat to industrial control systems is an acknowledged security issue, but a qualified dataset to quantify the risk remains largely unavailable. Senior executives of facilities that operate these systems face competing requirements for investment budgets, but without an understanding of the nature of the threat cyber security may not be a high priority. Operational managers and cyber incident responders at these facilities face a similarly complex situation. They must plan for the defence of critical systems, often unfamiliar to IT security professionals, from potentially capable, adaptable and covert antagonists who will actively attempt to evade detection. The scope of the challenge requires a coherent, enterprise-level awareness of the threat, such that organisations can assess their operational priorities, plan their defensive posture, and rehearse their responses prior to such an attack. This thesis proposes a novel combination of concepts found in risk assessment, intrusion detection, education, exercising, safety and process models, fused with experiential learning through serious games. It progressively builds a common set of shared mental models across an ICS operation to frame the nature of the adversary and establish enterprise situational awareness that permeates through all levels of teams involved in addressing the threat. This is underpinned by a set of coping strategies that identifies probable targets for advanced threat actors, proactively determining antagonistic courses of actions to derive an appropriate response strategy

Scalable Automation of Online Network Attack Characterization

Cyber attacks to enterprise networks and critical infrastructures are becoming more prevalent and diverse. Timely recognition of attack strategies and behaviors will assist analysts or resilient network defense systems in deploying effective means in anticipation of future threats. An attack can be characterized by the sequences of observed events that are relevant to critical assets. Earlier work has developed a semi-supervised learning framework to process large-scale events and extract attack behaviors. While the framework is designed to support online processing, the implementation requires extension and restructuring to support scalable automation of sustainable online network attack characterization. This work builds upon the semi-supervised Bayesian classification framework, and aims at providing a modular and scalable system that supports a variety of features to describe attacks, ranging from packet level information to metadata produced by sensors, such as Snort and Bro. The system will continuously process data streams, generating newly learned models, as well as record critical information of aged behavior models. These behavior models will reflect the attack strategies that are relevant to the critical assets, enhancing the situational awareness and enabling predictive and resilient network defense. The accuracy of the models is demonstrated through comparisons to network topologies and scenarios provided from the source of the dataset utilized. These scenarios often encapsulate multiple complex network attack behaviors allowing for more realistic representations of network traffic over time and better test cases for experimentation

Advances and Applications of Dezert-Smarandache Theory (DSmT) for Information Fusion (Collected Works), Vol. 4

The fourth volume on Advances and Applications of Dezert-Smarandache Theory (DSmT) for information fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics. The contributions (see List of Articles published in this book, at the end of the volume) have been published or presented after disseminating the third volume (2009, http://fs.unm.edu/DSmT-book3.pdf) in international conferences, seminars, workshops and journals. First Part of this book presents the theoretical advancement of DSmT, dealing with Belief functions, conditioning and deconditioning, Analytic Hierarchy Process, Decision Making, Multi-Criteria, evidence theory, combination rule, evidence distance, conflicting belief, sources of evidences with different importance and reliabilities, importance of sources, pignistic probability transformation, Qualitative reasoning under uncertainty, Imprecise belief structures, 2-Tuple linguistic label, Electre Tri Method, hierarchical proportional redistribution, basic belief assignment, subjective probability measure, Smarandache codification, neutrosophic logic, Evidence theory, outranking methods, Dempster-Shafer Theory, Bayes fusion rule, frequentist probability, mean square error, controlling factor, optimal assignment solution, data association, Transferable Belief Model, and others. More applications of DSmT have emerged in the past years since the apparition of the third book of DSmT 2009. Subsequently, the second part of this volume is about applications of DSmT in correlation with Electronic Support Measures, belief function, sensor networks, Ground Moving Target and Multiple target tracking, Vehicle-Born Improvised Explosive Device, Belief Interacting Multiple Model filter, seismic and acoustic sensor, Support Vector Machines, Alarm classification, ability of human visual system, Uncertainty Representation and Reasoning Evaluation Framework, Threat Assessment, Handwritten Signature Verification, Automatic Aircraft Recognition, Dynamic Data-Driven Application System, adjustment of secure communication trust analysis, and so on. Finally, the third part presents a List of References related with DSmT published or presented along the years since its inception in 2004, chronologically ordered

The entangled cyberspace: an integrated approach for predicting cyber-attacks

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonSignificant studies in cyber defence analysis have predominantly revolved around a single linear analysis of information from a single source of evidence (The Network). These studies were limited in their ability to understand the dynamics of entanglements related to cyber-incidents. This research integrates evidence beyond the network in an attempt to understand and predict phases of the kill-chain across the information space. This research provides a multi-dimensional phased analysis of the traditional kill-chain model using structural vector autoregressive models. In the ‘Entangled Cyberspace Framework’, each phase of the kill-chain corresponds to a single dimension of the information space based on time observations of certain events. Events are represented as time signals, where each phase is characterised by multiple time signals representing multiple events on that phase. Multiple time signals are analysed using structural models for multiple time series analysis (Vector Auto-Regressive models). At each phase of the kill-chain, we perform a lagged co-integration analysis of events across the information space. This nature of analysis detects hidden entanglements that characterise events in the kill-chain beyond the network. The measured prediction accuracy and error measured at each stage of the experiment represents the usefulness of selected events in characterising the defined stage of the kill-chain. The entangled cyberspace, in theory, is the fusion of three conceptual foundations: a) A multi-dimensional characterisation of cyberspace, b) A sequential phased model for perpetrating cyber-attacks and c) A structural model for integrating and simultaneously analysing multiple sources of evidence. It starts with the characterisation of the information space into different dimensions of interest. The framework goes further to identify evidence sources across these characterised dimensions and integrates them in the analytical context under consideration (e.g. Malware Injection). The concrete findings show that our approach and analytical methodology are capable of detecting entanglements when applied to a set of entangled activities across the information space. The findings also prove that activities beyond the network have significant effects on the nature of the unfolding cyber-attack vector. The predictive features of events across the kill-chain were also presented in this research as opinion and emotion drivers on the social dimension, packet data details and social and cultural events on the economic layer. Finally, co-integration detected between events across and within dimensions of the information space proves the existence of both inter-dimensional and intra-dimensional entanglements that affect the nature of events unfolding during the kill-chain (from the adversary’s point of view). The novelty of this research rests in the ability to hop across the information space for detecting evidential clues of activities that are related-to cyber-incidents. This research also expands the standard multi-dimensional information space to include SPEC factors as indicators of cyber-incidents. This research improves the current information security management model, specifically in the monitoring, analysis and detection phases. This research provides a methodology that accommodates a robust evidence base for understanding the attack surface. Practically, this research provides a basis for creating applications and tools for protecting critical national infrastructure by integrating data from social platforms, real-world political, cultural and economic events and the cyber-physical

An artificial intelligence-based collaboration approach in industrial IoT manufacturing : key concepts, architectural extensions and potential applications

The digitization of manufacturing industry has led to leaner and more efficient production, under the Industry 4.0 concept. Nowadays, datasets collected from shop floor assets and information technology (IT) systems are used in data-driven analytics efforts to support more informed business intelligence decisions. However, these results are currently only used in isolated and dispersed parts of the production process. At the same time, full integration of artificial intelligence (AI) in all parts of manufacturing systems is currently lacking. In this context, the goal of this manuscript is to present a more holistic integration of AI by promoting collaboration. To this end, collaboration is understood as a multi-dimensional conceptual term that covers all important enablers for AI adoption in manufacturing contexts and is promoted in terms of business intelligence optimization, human-in-the-loop and secure federation across manufacturing sites. To address these challenges, the proposed architectural approach builds on three technical pillars: (1) components that extend the functionality of the existing layers in the Reference Architectural Model for Industry 4.0; (2) definition of new layers for collaboration by means of human-in-the-loop and federation; (3) security concerns with AI-powered mechanisms. In addition, system implementation aspects are discussed and potential applications in industrial environments, as well as business impacts, are presented