Yes / As one of the most serious types of cyber attack, Advanced Persistent Threats (APT) have caused major concerns on a global scale. APT refers to a persistent, multi-stage attack with the intention to compromise the system and gain information from the targeted system, which has the potential to cause significant damage and substantial financial loss. The accurate detection and prediction of APT is an ongoing challenge. This work proposes a novel machine learning-based system entitled MLAPT, which can accurately and rapidly detect and predict APT attacks in a systematic way. The MLAPT runs through three main phases: (1) Threat detection, in which eight methods have been developed to detect different techniques used during the various APT steps. The implementation and validation of these methods with real traffic is a significant contribution to the current body of research; (2) Alert correlation, in which a correlation framework is designed to link the outputs of the detection methods, aims to identify alerts that could be related and belong to a single APT scenario; and (3) Attack prediction, in which a machine learning-based prediction module is proposed based on the correlation framework output, to be used by the network security team to determine the probability of the early alerts to develop a complete APT attack. MLAPT is experimentally evaluated and the presented system is able to predict APT in its early steps with a prediction accuracy of 84.8%.

Yes / Over the past decade, the digitization of services transformed the healthcare sector leading to a sharp rise in cybersecurity threats. Poor cybersecurity in the healthcare sector, coupled with high value of patient records attracted the attention of hackers. Sophisticated advanced persistent threats and malware have significantly contributed to increasing risks to the health sector. Many recent attacks are attributed to the spread of malicious software, e.g., ransomware or bot malware. Machines infected with bot malware can be used as tools for remote attack or even cryptomining. This paper presents a novel approach, called BotDet, for botnet Command and Control (C&C) traffic detection to defend against malware attacks in critical ultrastructure systems. There are two stages in the development of the proposed system: 1) we have developed four detection modules to detect different possible techniques used in botnet C&C communications and 2) we have designed a correlation framework to reduce the rate of false alarms raised by individual detection modules. Evaluation results show that BotDet balances the true positive rate and the false positive rate with 82.3% and 13.6%, respectively. Furthermore, it proves BotDet capability of real time detection.

Vehicular safety and efficiency has been an ongoing research topic since the creation of the automobile. Despite this, deaths due to vehicular accidents are still extremely common, with driver issues and errors causing a vast majority of them. In order to combat the safety risks, Connected and Autonomous Vehicles (CAV) and other smart solutions have been heavily researched. CAVs provide the means to increase the safety of travel as well as its efficiency. However, before connected vehicles can be deployed and utilized, safe and secure communication and standards need to be created and evaluated to ensure that the introduction of a new safety threat does not overshadow the one that is already being faced. As such, it is integral for Intelligent Transportation Systems (ITS) to prevent, detect and respond to cyberattacks. This research focuses on the detection and response of ITS components to cyberattacks. An Intrusion Detection System (IDS) located on Roadside Units (RSU) was developed to detect misbehavior nodes. This model maintains a 98%-100% accuracy while reducing system overhead by removing the need for edge or cloud computing. A resilient Intrusion Response System (IRS) for a autonomous intersection was developed to protect again sybil attacks. The IRS utilizes adaptive switching between several intersection types to reduce delay by up to 78% compared to intersections without these defenses. / Master of Science / Vehicular safety and efficiency has been an ongoing research topic since the creation of the automobile. Despite this, deaths due to vehicular accidents are still extremely common, with driver issues and errors causing a vast majority of them. In order to combat the safety risks, Connected and Autonomous Vehicles (CAV) and other smart solutions have been heavily researched. CAVs provide the means to increase the safety of travel as well as its efficiency. However, before connected vehicles can be deployed and utilized, safe and secure communication and standards need to be created and evaluated to ensure that the introduction of a new safety threat does not overshadow the one that is already being faced. As such it is integral for Intelligent Transportation Systems (ITS) to prevent, detect and respond to cyberattacks. This research focuses on the detection and response of ITS components to cyberattacks. An Intrusion Detection System (IDS) was created to detect vehicles misbehaving or conducting cyberattacks. The IDS is installed on off-road computers, called Roadside Units (RSU) which prevents the need for a separate server to be created to hold the IDS. The IDS is able to identify misbehavior and attacks at a 98% to 100% accuracy. An autonomous intersection is an intersection where all directions for driving through the intersection are transmitted through wireless communication. A Intrusion Response System (IRS) was developed for an autonomous intersection, to defend against vehicles making multiple reservation requests to pass through the intersection. The IRS reduces vehicle delay through the intersection by 78% compared to an intersection without defenses.

No / Technological advancements have played a pivotal role in the rapid proliferation of the fourth industrial revolution (4IR) through the deployment of Internet of Things (IoT) devices in large numbers. COVID-19 caused serious disruptions across many industries with lockdowns and travel restrictions imposed across the globe. As a result, conducting business as usual became increasingly untenable, necessitating the adoption of new approaches in the workplace. For instance, virtual doctor consultations, remote learning, and virtual private network (VPN) connections for employees working from home became more prevalent. This paradigm shift has brought about positive benefits, however, it has also increased the attack vectors and surfaces, creating lucrative opportunities for cyberattacks. Consequently, more sophisticated attacks have emerged, including the Distributed Denial of Service (DDoS) and Ransomware attacks, which pose a serious threat to businesses and organisations worldwide. This paper proposes a system for detecting malicious activities in network traffic using sequential pattern mining (SPM) techniques. The proposed approach utilises SPM as an unsupervised learning technique to extract intrinsic communication patterns from network traffic, enabling the discovery of rules for detecting malicious activities and generating security alerts accordingly. By leveraging this approach, businesses and organisations can enhance the security of their networks, detect malicious activities including emerging ones, and thus respond proactively to potential threats.

Yes / The world has seen exponential growth in deploying Internet of Things (IoT) devices. In recent years, connected IoT devices have surpassed the number of connected non-IoT devices. The number of IoT devices continues to grow and they are becoming a critical component of the national infrastructure. IoT devices' characteristics and inherent limitations make them attractive targets for hackers and cyber criminals. Botnet attack is one of the serious threats on the Internet today. This article proposes pattern-based feature selection methods as part of a machine learning (ML) based botnet detection system. Specifically, two methods are proposed: the first is based on the most dominant pattern feature values and the second is based on Maximal Frequent Itemset (MFI) mining. The proposed feature selection method uses Gini Impurity (GI) and an unsupervised clustering method to select the most influential features automatically. The evaluation results show that the proposed methods have improved the performance of the detection system. The developed system has a True Positive Rate (TPR) of 100% and a False Positive Rate (FPR) of 0% for best performing models. In addition, the proposed methods reduce the computational cost of the system as evidenced by the detection speed of the system.

No / The rapid shift and increase in remote access to organisation resources have led to a significant increase in the number of attack vectors and attack surfaces, which in turn has motivated the development of newer and more sophisticated cyber-attacks. Such attacks include Multi-Stage Attacks (MSAs). In MSAs, the attack is executed through several stages. Classifying malicious traffic into stages to get more information about the attack life-cycle becomes a challenge. This paper proposes a malicious traffic clustering approach based on Latent Dirichlet Allocation (LDA). LDA is a topic modelling approach used in natural language processing to address similar problems. The proposed approach is unsupervised learning and therefore will be beneficial in scenarios where traffic data is not labeled and analysis needs to be performed. The proposed approach uncovers intrinsic contexts that relate to different categories of attack stages in MSAs. These are vital insights needed across different areas of cybersecurity teams like Incident Response (IR) within the Security Operations Center (SOC), the insights uncovered could have a positive impact in ensuring that attacks are detected at early stages in MSAs. Besides, for IR, these insights help to understand the attack behavioural patterns and lead to reduced time in recovery following an incident. The proposed approach is evaluated on a publicly available MSAs dataset. The performance results are promising as evidenced by over 99% accuracy in identified malicious traffic clusters.

Yes / In recent times, the proliferation of Internet of Things (IoT) technology has brought a significant shift in the digital transformation of various industries. The enabling technologies have accelerated this adoption. The possibilities unlocked by IoT have been unprecedented, leading to the emergence of smart applications that have been integrated into national infrastructure. However, the popularity of IoT technology has also attracted the attention of adversaries, who have leveraged the inherent limitations of IoT devices to launch sophisticated attacks, including Multi-Stage attacks (MSAs) such as IoT botnet attacks. These attacks have caused significant losses in revenue across industries, amounting to billions of dollars. To address this challenge, this paper proposes a system for IoT botnet detection that comprises two phases. The first phase aims to identify IoT botnet traffic, the input to this phase is the IoT traffic, which is subjected to feature selection and classification model training to distinguish malicious traffic from normal traffic. The second phase analyses the malicious traffic from stage one to identify different botnet attack campaigns. The second stage employs an alert correlation approach that combines the Latent Semantic Analysis (LSA) unsupervised learning and graph theory based techniques. The proposed system was evaluated using a publicly available real IoT traffic dataset and yielded promising results, with a True Positive Rate (TPR) of over 99% and a False Positive Rate (FPR) of 0%. / Researchers Supporting Project, King Saud University, Riyadh, Saudi Arabia, under Grant RSPD2024R953

Recent research has indicated that although security systems are developing, illegal intrusion to computers is on the rise. The research conducted here illustrates that improving intrusion detection and prevention methods is fundamental for improving the overall security of systems. This research includes the design of a novel Intrusion Detection System (IDS) which identifies four levels of visibility of attacks. Two major areas of security concern were identified: speed and volume of attacks; and complexity of multistage attacks. Hence, the Multistage Intrusion Detection and Prevention System (MIDaPS) that is designed here is made of two fundamental elements: a multistage attack engine that heavily depends on attack trees and a Denial of Service Engine. MIDaPS were tested and found to improve current intrusion detection and processing performances. After an intensive literature review, over 25 GB of data was collected on honeynets. This was then used to analyse the complexity of attacks in a series of experiments. Statistical and analytic methods were used to design the novel MIDaPS. Key findings indicate that an attack needs to be protected at 4 different levels. Hence, MIDaPS is built with 4 levels of protection. As, recent attack vectors use legitimate actions, MIDaPS uses a novel approach of attack trees to trace the attacker's actions. MIDaPS was tested and results suggest an improvement to current system performance by 84% whilst detecting DDOS attacks within 10 minutes.

Voice Over IP (VoIP) telephony is becoming widespread, and is often integrated into computer networks. Because of his, it is likely that malicious software will threaten VoIP systems the same way traditional computer systems have been attacked by viruses, worms, and other automated agents. While most users have become familiar with email spam and viruses in email attachments, spam and malicious traffic over telephony currently is a relatively unknown threat. VoIP networks are a challenge to secure against such malware as much of the network intelligence is focused on the edge devices and access environment. A novel security architecture is being developed which improves the security of a large VoIP network with many inexperienced users, such as non-IT office workers or telecommunication service customers. The new architecture establishes interaction between the VoIP backend and the end users, thus providing information about ongoing and unknown attacks to all users. An evaluation of the effectiveness and performance of different implementations of this architecture is done using virtual machines and network simulation software to emulate vulnerable clients and servers through providing apparent attack vectors.

De nos jours, les systèmes de production sont confrontés à leur 4e révolution. Celle-ci est numérique avec des réseaux toujours plus denses et complexes s’ouvrant sur l’extérieur. Cette ouverture rend ces systèmes plus vulnérables. Les menaces sur ces Systèmes Cyber-Physiques de Production (SCPP) ne sont plus seulement théoriques. L’attaque sur l’aciérie allemande ou le cryptovirus Wannacry en sont de parfaits exemples. Ce travail propose un outil contribuant à la sécurité des SCPP. Nos contributions sont triples : La conception d'un Système de Détection et Réaction aux Anomalies (SDRA) placé sur le réseau de terrain. Celui-ci intègre des méthodes de détection comportementales et informationnelles. Il comprend également des capacités de réaction à la fois passives, mettant en œuvre de la remontée d'information vers l'humain ou vers des systèmes de niveaux supérieurs, et actives intégrant du filtrage d'ordre ou de la mise en repli. L'application des méthodes proposées entraîne naturellement un effort de conception supplémentaire qui doit être réduit. Nous avons donc mis au point une démarche permettant d’assister les concepteurs pour la configuration de notre SDRA. Cette dernière se base sur une approche hybride (composant/opération) et étend un flot de conception existant. Plusieurs transformations raffinent des vues surveillance/supervision des composants alors que d’autres génèrent la configuration du SDRA. Une troisième contribution propose un démonstrateur réaliste basé sur un environnement virtuel de test. Ce dernier intègre la simulation conjointe de la partie opérative et de la partie commande et permet de montrer les qualités fonctionnelles des solutions face à des scénarios d’attaque ou de défaillance. / Today, production systems are facing their 4th revolution. This revolution is digital with increasingly dense and complex networks opening on the outside. This openness makes these systems more vulnerable. The threats on these Cyber-Physical Production Systems (CPPS) are no longer just theoretical. The attacks on the German steel mill or the Wannacry crypto virus are perfect examples. This work proposes a tool contributing to the security of the SCPP. Our contributions are threefold: The design of an Anomaly Detection and Response System (ADRS) placed on the field network. It integrates behavioral and informational detection methods. It also includes passive response capabilities, implementing feedback to the human or to higher level systems, and active integrating order filtering or fallback. The application of the proposed methods naturally entails an additional design effort which must be reduced. We have therefore developed an approach to assist designers in the configuration of our ADRS. It is based on a hybrid approach (component / operation) and extends an existing design flow. Several transformations refine monitoring / supervision views of the components while others generate the configuration of the ADRS. A third contribution proposes a realistic demonstrator based on a virtual test environment. It integrates the joint simulation of the operative part and the control part and makes it possible to show the functional qualities of the solutions in the face of attack or failure scenarios.