Purpose: Operational technology (OT) environments face significant risks and threats stemming from Industry 4.0. The security landscape for OT is confronted with unprecedented challenges due to the expanding attack surface resulting from factors like cloud adoption, Industrial Internet of Things, and increased mobility. Securing OT networks has become increasingly complex, and relying solely on perimeter firewalls or air gaps is a flawed approach. Malicious actors now target OT systems for high-stakes ransoms and lockouts, exploiting the manufacturing industry's reluctance to disrupt operations. Conventional security measures are insufficient against insider threats and agile hackers who can maneuver within the network. These adversaries display patience and persistence, often waiting for months to gain unauthorized access.  Acknowledging the complexity of OT within industrial organizations, the objective of this master's thesis is to offer a set of simplified principles and practices that can serve as valuable guidance for practitioners seeking to establish effective Attack Surface Management (ASM) strategies in OT environments. These OT security practices embody a comprehensive approach to cybersecurity, empowering OT security practitioners to adapt to ever-evolving industry dynamics and establish baseline protection against various threats and vulnerabilities. Design/Methodology/Approach: This thesis utilizes Action Design Research (ADR), which combines Action Research (AR) and Design Science (DS) approaches. ADR is applied to address a specific problem in an organizational context, involving intervention, evaluation, and the creation of new IT principles and practices. ADR is chosen as the appropriate methodology to guide the development and evaluation of a prototype OT Remote Connectivity and the secure integration of MES components into the organizational OT environment. Findings: This study made a valuable contribution to the field by introducing five innovative Design Principles (DPs) specifically designed to simplify ASM in OT environments. These newly proposed DPs complement the existing ones and address emerging challenges and considerations in the rapidly evolving landscape of OT security. They provide practitioners with fresh perspectives, guidelines, and approaches to enhance the effectiveness and efficiency of ASM strategies in OT. Practical Implications: The research project gives a comprehensive checklist of secure practices for OT, these were formulated and implemented, considering the entire lifecycle of OT devices. These practices encompassed various stages, from design, procurement to disposal, and aimed to enhance the security posture of OT systems. Building upon these secure practices, a functional prototype was developed to facilitate secure remote connectivity for suppliers/vendors and the seamless integration of Manufacturing Execution System (MES) components.

Substations are important components for transforming voltage and distributing power in electrical infrastructure. Modern substations are usually automated by substation automation systems, which offload the work of operators and reduce potential human error. The IEC 61850 standard was released in 2003 to address the compatibility of electronic devices with substation automation systems. However, it exposed more attack surfaces with the adoption of Ethernet, the wide use of digital devices, and the connection to the Internet. Therefore, it is necessary to analyze the security threats of modern substations. This master thesis investigated the common design options of IEC 61850 substations, then used the graph-based threat modeling method to explore the security weaknesses of those options and compare the effectiveness of security mechanisms. The construction of threat models is carried out with SecuriCAD and several domain-specific modeling languages that developed from the Meta Attack Language framework. Through the analysis of the results, we conclude that the evaluated security mechanisms can bring security benefits and mitigate security threats in the area of substation automation. / Transformatorstationer är viktiga komponenter för att transformera spänning och distribuera kraft i elektrisk infrastruktur. Moderna transformatorstationer automatiseras vanligtvis av transformatorstationsautomationssystem, som avlastar operatörernas arbete och minskar potentiella mänskliga fel. IEC 61850-standarden släpptes 2003 för att ta itu med kompatibiliteten hos elektroniska enheter med automationssystem för transformatorstationer. Men det exponerade fler attackytor med antagandet av Ethernet, den breda användningen av digitala enheter och anslutningen till Internet. Därför är det nödvändigt att analysera säkerhetshoten för moderna transformatorstationer. Denna masteruppsats undersökte de vanliga designalternativen för IEC 61850-transformatorstationer, och använde sedan den grafbaserade hotmodelleringsmetoden för att utforska säkerhetssvagheterna hos dessa alternativ och jämföra effektiviteten hos säkerhetsmekanismer. Konstruktionen av hotmodeller utförs med SecuriCAD och flera domänspecifika modelleringsspråk som utvecklats från ramverket Meta Attack Language. Genom analysen av resultaten drar vi slutsatsen att de utvärderade säkerhetsmekanismerna kan ge säkerhetsfördelar och mildra säkerhetshot inom området för automatisering av transformatorstationer.

Många utvecklare förlitar sig på moderna utvecklingsmiljöer med stöd för språkspecifika funktioner som underlättar utvecklingsprocessen. Nyutvecklade språk saknar ofta denna möjlighet, Meta Attack Language (MAL) är ett av dessa. Syftet med denna avhandling är att undersöka betydelsen av en integrerad utvecklingsmiljö i MAL med avseende på användarupplevelsen genom att utveckla en prototyp av en integrerad utvecklingsmiljö för MAL. Avhandlingen inleds med en litteraturstudie inom de relevanta områdena MAL, utvecklingsverktyg och kodanalys. Utifrån den insamlade informationen fastställs det att prototypen utvecklas som en Visual Studio Code-extension med en language server i enlighet med Language Server Protocol (LSP). För att mäta användarupplevelsen av en utvecklingsmiljö för MAL används User Experience Questionnaire (UEQ) som mäter användarupplevelsen utifrån sex perspektiv: attraktivitet, tydlighet, effektivitet, pålitlighet, stimulering och innovativitet. Mätningarna sker i forma av två likadana enkäter där MAL-utvecklare besvarar frågor angående användarupplevelsen av att utveckla i MAL. Den första enkäten undersöker användarupplevelsen utan prototypen och den andra undersöker användarupplevelsen med prototypen. För att undersöka om skillnaden med och utan prototypen är av statistisk signifikant utförs t-test för varje perspektiv. Avhandlingen har resultaterat i en Visual Studio Code-extension som tillhandahåller intelligent kodifyllning, diagnostics, go to definition och syntaxmarkering för MAL. Resultaten av enkäterna visar på en ökning på alla mätta perspektiv med en statistisk signifikant förbättring på attraktivitet, tydlighet och effektivitet. För att utveckla arbetet och dra större slutsatser skulle prototypen behöva utvecklas mer samt att svarsunderlaget till enkäterna vara större. / Many developers rely on modern development environments with support for language-specific features that facilitate the development process. Newly developed languages often lack this possibility, Meta Attack Language (MAL) is one of these. The purpose of this dissertation is to investigate the importance of an integrated development environment in MAL with regard to the user experience by developing a prototype of an integrated development environment for MAL. The dissertation begins with a literature study in the relevant areas consisting of MAL, development tools and code analysis. Based on the information gathered, it is determined that the prototype is developed as a Visual Studio Code-extension with a language server adhering to the Language Server Protocol (LSP). To measure the user experience of a development environment for MAL, the User Experience Questionnaire (UEQ) is used, which measures the user experience from six perspectives: attractiveness, perspicuity, efficiency, dependability, stimulation and novelty. The measurements take the form of two surveys of the same kind where MAL developers answer questions regarding the user experience of developing in MAL. The first survey examines the user experience without the prototype and the second examines the user experience with the prototype. To investigate whether the difference with and without the prototype is statistically significant, t-tests are performed for each perspective. The dissertation has resulted in a Visual Studio Code-extension that provides intelligent code completion, diagnostics, go to definition and syntax highlighting for MAL. The results of the surveys show an increase in all measured perspectives with a statistically significant improvement in attractiveness, perspicuity and efficiency. In order to develop the work and draw greater conclusions, the prototype would need to be developed more and the response basis to the questionnaires greater.

The tremendous increase in usage and complexity of modern communication and network systems connected to the Internet, places demands upon security management to protect organisations¿ sensitive data and resources from malicious intrusion. Malicious attacks by intruders and hackers exploit flaws and weakness points in deployed systems through several sophisticated techniques that cannot be prevented by traditional measures, such as user authentication, access controls and firewalls. Consequently, automated detection and timely response systems are urgently needed to detect abnormal activities by monitoring network traffic and system events. Network Intrusion Detection Systems (NIDS) and Network Intrusion Prevention Systems (NIPS) are technologies that inspect traffic and diagnose system behaviour to provide improved attack protection. The current implementation of intrusion detection systems (commercial and open-source) lacks the scalability to support the massive increase in network speed, the emergence of new protocols and services. Multi-giga networks have become a standard installation posing the NIDS to be susceptible to resource exhaustion attacks. The research focuses on two distinct problems for the NIDS: missing alerts due to packet loss as a result of NIDS performance limitations; and the huge volumes of generated alerts by the NIDS overwhelming the security analyst which makes event observation tedious. A methodology for analysing alerts using a proposed framework for alert correlation has been presented to provide the security operator with a global view of the security perspective. Missed alerts are recovered implicitly using a contextual technique to detect multi-stage attack scenarios. This is based on the assumption that the most serious intrusions consist of relevant steps that temporally ordered. The pre- and post- condition approach is used to identify the logical relations among low level alerts. The alerts are aggregated, verified using vulnerability modelling, and correlated to construct multi-stage attacks. A number of algorithms have been proposed in this research to support the functionality of our framework including: alert correlation, alert aggregation and graph reduction. These algorithms have been implemented in a tool called Multi-stage Attack Recognition System (MARS) consisting of a collection of integrated components. The system has been evaluated using a series of experiments and using different data sets i.e. publicly available datasets and data sets collected using real-life experiments. The results show that our approach can effectively detect multi-stage attacks. The false positive rates are reduced due to implementation of the vulnerability and target host information.

<p dir="ltr">In the contemporary digital era, Advanced Persistent Threat (APT) attacks are evolving, becoming increasingly sophisticated, and now perilously targeting critical cyber-physical systems, notably Industrial Control Systems (ICS). The intersection of digital and physical realms in these systems enables APT attacks on ICSs to potentially inflict physical damage, disrupt critical infrastructure, and jeopardize human safety, thereby posing severe consequences for our interconnected world. Provenance tracing techniques are essential for investigating these attacks, yet existing APT attack forensics approaches grapple with scalability and maintainability issues. These approaches often hinge on system- or application-level logging, incurring high space and run-time overheads and potentially encountering difficulties in accessing source code. Their dependency on heuristics and manual rules necessitates perpetual updates by domain-knowledge experts to counteract newly developed attacks. Additionally, while there have been efforts to verify the safety of Programming Logic Controller (PLC) code as adversaries increasingly target industrial environments, these works either exclusively consider PLC program code without connecting to the underlying physical process or only address time-related physical safety issues neglecting other vital physical features.</p><p dir="ltr">This dissertation introduces two novel frameworks, ATLAS and ARCHPLC, to address the aforementioned challenges, offering a synergistic approach to fortifying cybersecurity in the face of evolving APT and ICS threats. ATLAS, an effective and efficient multi-host attack investigation framework, constructs end-to-end APT attack stories from audit logs by combining causality analysis, Natural Language Processing (NLP), and machine learning. Identifying key attack patterns, ATLAS proficiently analyzes and pinpoints attack events, minimizing alert fatigue for cyber analysts. During evaluations involving ten real-world APT attacks executed in a realistic virtual environment, ATLAS demonstrated an ability to recover attack steps and construct attack stories with an average precision of 91.06%, a recall of 97.29%, and an F1-score of 93.76%, providing a robust framework for understanding and mitigating cyber threats.</p><p dir="ltr">Concurrently, ARCHPLC, an advanced approach for enhancing ICS security, combines static analysis of PLC code and data mining from ICS data traces to derive accurate invariants, providing a comprehensive understanding of ICS behavior. ARCHPLC employs physical causality graph analysis techniques to identify cause-effect relationships among plant components (e.g., sensors and actuators), enabling efficient and quantitative discovery of physical causality invariants. Supporting patching and run-time monitoring modes, ARCHPLC inserts derived invariants into PLC code using program synthesis in patching mode and inserts invariants into a dedicated monitoring program for continuous safety checks in run-time monitoring mode. ARCHPLC adeptly detects and mitigates run-time anomalies, providing exceptional protection against cyber-physical attacks with minimal overhead. In evaluations against 11 cyber-physical attacks on a Fischertechnik manufacturing plant and a chemical plant simulator, ARCHPLC protected the plants without any false positives or negatives, with an average run-time overhead of 14.31% in patching mode and 0.4% in run-time monitoring mode.</p><p dir="ltr">In summary, this dissertation provides invaluable solutions that equip cybersecurity professionals to enhance APT attack investigation, enabling them to identify and comprehend complex attacks with heightened accuracy. Moreover, these solutions significantly bolster the safety and security of ICS infrastructure, effectively protecting critical systems and strengthening defenses against cyber-physical attacks, thereby contributing substantially to the field of cybersecurity.</p>

Cyber-attacks are an increasing problem for organizations across the world. The attacks on systems are getting more and more sophisticated and thereby more and more difficult to protect against. The security of systems is crucial to protect your data from unauthorized access. One approach for testing the resilience of these systems is the use of threat modelling and attack simulations. The use of threat models also enables you to identify vulnerabilities in your infrastructure. The overall resilience of the system can then be increased by implementing protection against these vulnerabilities which can take many forms. There can be security issues regarding a single component in the infrastructure and more structural issues concerning more than one component in the system. Meta Attack Language (MAL) is a meta language to write threat languages of different systems. In MAL there exits different components called assets, these assets can have defenses. The problem is that structural weaknesses cannot be identified in the current state of the language. This thesis work will provide a solution to identify vulnerable patterns in a threat model and translate these pattern to secure patterns. A prototype has been created that take a threat model as input and outputs a new updated threat model. The prototype will translate the input to a graph database and run a series of predefined queries on the database that will identify and replace vulnerable patterns. A formal logic for finding vulnerable patterns is suggested and an API to change these patterns is implemented. The result shows that by running a model through the prototype, structural vulnerabilities can be identified and mitigated. This could potentially increase the overall resilience of the system. / Attacker på IT system är ett ökande problem för organisationer runt om i världen. Attackerna blir mer och mer sofistikerade och därmed svårare att skydda sig emot. Säkerheten av systemen är väldigt viktigt för att skydda data från obehörig åtkomst. Ett tillvägagångssätt för att testa säkerheten mot attacker är att använda hotmodeller och attack-simuleringar. Resultatet av en sådan simulering kan sedan användas för att göra systemet säkrare genom att implementera skydd mot kända sårbarheter. Dessa sårbarheter kan komma i många olika former. De kan finnas på en enskild komponent i arkitekturen eller så kan de bero på mer strukturella svagheter där flera komponenter berörs. Meta Attack Language (MAL) är ett meta-språk som kan användas för skapa hot-språk. I MAL så finns det olika komponenter som kan ha försvar associerat till sig. Problemet är att i det nuvarande stadiet av MAL så går det inte att identifiera strukturella svagheter där fler än en komponent är en del av problemet. Det här arbetet föreslår en lösning till att identifiera skadliga mönster och översätta dessa mönster till säkra mönster. En prototyp har tagits fram som tar en hotmodell som in-data och returnerar en uppdaterad hotmodell. Prototypen översätter hotmodellen till en grafdatabas och exekverar en serie av sökningar som identifierar och uppdaterar skadliga mönster. En logik för att hitta mönster föreslås och ett API av funktioner för att ändra mönster har utvecklats. Resultaten visar att genom körning av en modell genom prototypen så skulle detta potentiellt kunna öka systemens säkerhet.

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Air traffic communication is one of the most vital systems for air traffic management controllers. It is used every day to allow millions of people to travel safely and efficiently across the globe. But many of the systems considered industry-standard are used without any sort of encryption and authentication meaning that they are vulnerable to different wireless attacks. In this thesis vulnerabilities within an air traffic management system called ADS-B will be investigated. The structure and theory behind this system will be described as well as the reasons why ADS-B is unencrypted. Two attacks will then be implemented and performed in an open-source air traffic management simulator called openScope. ADS-B data from these attacks will be gathered and combined with actual ADS-B data from genuine aircrafts. The collected data will be cleaned and used for machine learning purposes where three different algorithms will be applied to detect attacks. Based on our findings, where two out of the three machine learning algorithms used were able to detect 99.99% of the attacks, we propose that machine learning algorithms should be used to improve ADS-B security. We also think that educating air traffic controllers on how to detect and handle attacks is an important part of the future of air traffic management.

<p>With the fast spread of machine learning techniques, sharing and adopting public deep neural networks become very popular. As deep neural networks are not intuitive for human to understand, malicious behaviors can be injected into deep neural networks undetected. We call it trojan attack or backdoor attack on neural networks. Trojaned models operate normally when regular inputs are provided, and misclassify to a specific output label when the input is stamped with some special pattern called trojan trigger. Deploying trojaned models can cause various severe consequences including endangering human lives (in applications like autonomous driving). Trojan attacks on deep neural networks introduce two challenges. From the attacker's perspective, since the training data or training process is usually not accessible to the attacker, the attacker needs to find a way to carry out the trojan attack without access to training data. From the user's perspective, the user needs to quickly scan the online public deep neural networks and detect trojaned models.</p> <p>We try to address these challenges in this dissertation. For trojan attack without access to training data, We propose to invert the neural network to generate a general trojan trigger, and then retrain the model with reverse-engineered training data to inject malicious behaviors to the model. The malicious behaviors are only activated by inputs stamped with the trojan trigger. To scan and detect trojaned models, we develop a novel technique that analyzes inner neuron behaviors by determining how output activation change when we introduce different levels of stimulation to a neuron. A trojan trigger is then reverse-engineered through an optimization procedure using the stimulation analysis results, to confirm that a neuron is truly compromised. Furthermore, for complex trojan attacks, we propose a novel complex trigger detection method. It leverages a novel symmetric feature differencing method to distinguish features of injected complex triggers from natural features. For trojan attacks on NLP models, we propose a novel backdoor scanning technique. It transforms a subject model to an equivalent but differentiable form. It then inverts a distribution of words denoting their likelihood in the trigger and applies a novel word discriminativity analysis to determine if the subject model is particularly discriminative for the presence of likely trigger words.</p>

A significant security risk of modern vehicles is their vulnerability to relay attacks, due to challenge-response methods, such as those employed in Passive Keyless Entry (PKE) used by most commercial cars, being inherently exposed. This class of attacks are where communication between a vehicle and its key is relayed by an attacker over long range - thereby bypassing any encryption and unlocking the vehicle without requiring direct access to the key. While a multitude of defenses have been proposed in recent years, many lack either robustness or practicality. Any viable system will likely have to rely on an environmental parameter which is not easily manipulated. Moreover, the system has to be: cost effective; easily implementable; and take user comfort, such as the key’s battery life, into account. This thesis implements and evaluates a PKE system resistant to relay attacks, analyses a multitude of proposed strategies in literature for feasibility, as well as suggests a novel method: Approach Curve Matching. It is concluded that the most promising strategies are: Immobility Detection, Distance Bounding Protocols, and Approach Curve Matching - the first of which is chosen to be implemented in the prototype PKE system. The project develops a PKE system and implements the communication protocol using Bluetooth, as opposed to the conventional RFID. Immobility Detection, using an accelerometer, is then implemented. The final system is then tested and evaluated. It is concluded that while Immobility Detection is not comprehensively effective, it is easily implementable, cost-effective, and can greatly increase the security of PKE systems. Finally, it is proposed that Immobility Detection should be employed promptly by manufacturers while investigating potentially more effective, albeit uncertain, strategies. / En betydande säkerhetsbrist av moderna fordon är deras sårbarhet mot så kallade ’relay attacker’. Dessa typer av attacker, där signaler mellan bilen och nyckeln vidarebefordras, kringgår all kryptering och låser upp bilen utan att ha direkt tillgång till nyckeln. En stor del av kommersiella fordon tillämpar ’Passive Keyless Entry’ (PKE) som bygger på ’challenge-response’ metoder som visats vara särskilt utsatta för dessa attacker. En mängd olika skyddssystem har föreslagits på senare år, men många saknar erforderlig robusthet eller genomförbarhet. Ett lämpligt system bör uppfylla en rad olika kriterier. Strategin måste grundas på en omgivningsparameter som är både oföränderlig och tillräckligt rymdberoende att två nära positoner kan skiljas åt. Dessutom ska systemet vara kostnadseffektivt, implementerbart, och ta hänsyn till användarkomfort såsom batteritid. Projektets huvudsyfte är konstruktionen och analysen av ett ’relay attack’ resistent PKE system. I detta projekt ingår också en analys av ett antal föreslagna försvar och ett förslag om en ny metod: ’Approach Curve Matching’. Slutsatsen dras att de mest lovande taktikerna är: analys av Jaccard indexet av Wi-Fi hotspots, ’Distance Bounding Protocols’, ’Approach Curve Matching’ och ’Immobility Detection’ som också implementeras i prototyp PKE systemet. Projektet utvecklar först ett PKE system med två Raspberry Pis som agerar som bilens och nyckelns mikrodatorer och implementerar kommunikationsprotokollet med hjälp av Bluetooth. ’Immobility Detection’ är sedan implementerad genom en inbyggd accelerometer i nyckeln. Slutligen testas och utvärderas systemet. Det konkluderas att trots att ’Immobility Detection’s effektivitet inte är helt omfattande är den lätt att implementera, kostnadseffektiv, och kan bidra till en betydlig ökning av säkerheten hos PKE system. Vidare observerade projektet att Bluetooths ’Received Signal Strength Indicator’ (RSSI) mätningar är utsatta för avsevärd ostadighet och är allmänt omgivningsberoende. Därför anses Bluetooth RSSI inte lämplig för PKE tillämpningar även om andra metoder för avståndsmätning med Bluetooth kan ha högre prestanda. Det föreslås att ’Immobility Detection’ tillämpas av tillverkare omgående medans andra potentiellt mer effektiva strategier utreds.