Models of Reliability of Fault-Tolerant Software Under Cyber-Attacks

This paper offers a new approach to modelling the effect of cyber-attacks on reliability of software used in industrial control applications. The model is based on the view that successful cyber-attacks introduce failure regions, which are not present in non-compromised software. The model is then extended to cover a fault tolerant architecture, such as the 1-out-of-2 software, popular for building industrial protection systems. The model is used to study the effectiveness of software maintenance policies such as patching and "cleansing" ("proactive recovery") under different adversary models ranging from independent attacks to sophisticated synchronized attacks on the channels. We demonstrate that the effect of attacks on reliability of diverse software significantly depends on the adversary model. Under synchronized attacks system reliability may be more than an order of magnitude worse than under independent attacks on the channels. These findings, although not surprising, highlight the importance of using an adequate adversary model in the assessment of how effective various cyber-security controls are

Advanced Threat Intelligence: Interpretation of Anomalous Behavior in Ubiquitous Kernel Processes

Targeted attacks on digital infrastructures are a rising threat against the confidentiality, integrity, and availability of both IT systems and sensitive data. With the emergence of advanced persistent threats (APTs), identifying and understanding such attacks has become an increasingly difficult task. Current signature-based systems are heavily reliant on fixed patterns that struggle with unknown or evasive applications, while behavior-based solutions usually leave most of the interpretative work to a human analyst. This thesis presents a multi-stage system able to detect and classify anomalous behavior within a user session by observing and analyzing ubiquitous kernel processes. Application candidates suitable for monitoring are initially selected through an adapted sentiment mining process using a score based on the log likelihood ratio (LLR). For transparent anomaly detection within a corpus of associated events, the author utilizes star structures, a bipartite representation designed to approximate the edit distance between graphs. Templates describing nominal behavior are generated automatically and are used for the computation of both an anomaly score and a report containing all deviating events. The extracted anomalies are classified using the Random Forest (RF) and Support Vector Machine (SVM) algorithms. Ultimately, the newly labeled patterns are mapped to a dedicated APT attacker–defender model that considers objectives, actions, actors, as well as assets, thereby bridging the gap between attack indicators and detailed threat semantics. This enables both risk assessment and decision support for mitigating targeted attacks. Results show that the prototype system is capable of identifying 99.8% of all star structure anomalies as benign or malicious. In multi-class scenarios that seek to associate each anomaly with a distinct attack pattern belonging to a particular APT stage we achieve a solid accuracy of 95.7%. Furthermore, we demonstrate that 88.3% of observed attacks could be identified by analyzing and classifying a single ubiquitous Windows process for a mere 10 seconds, thereby eliminating the necessity to monitor each and every (unknown) application running on a system. With its semantic take on threat detection and classification, the proposed system offers a formal as well as technical solution to an information security challenge of great significance.The financial support by the Christian Doppler Research Association, the Austrian Federal Ministry for Digital and Economic Affairs, and the National Foundation for Research, Technology and Development is gratefully acknowledged

A systematic design approach to IOT security for legacy production machinery

The Internet of Things (IoT) is an emerging topic of rapidly growing technical importance for the industry. The aim is to connect objects with unique identifiers and combine them with internet connectivity for data transfer. This advanced connectivity has significant potential in the workshop-level upgrade of existing legacy equipment to unlock new features and economic benefits especially for monitoring and control applications However, the introduction of the Industrial Internet of Things (IIoT) brings new additional security and integrity risks for the industrial environment in the form of network, communication, software and hardware security risks. This thesis addresses such fundamental new risks at their root by introducing a novel approach for IoT-enabled monitoring of legacy production machinery, which consist of five stages, incorporating security by design features. The first two phases of this novel approach aim to analyse current monitoring practices and security and vulnerability issues related to the application domain. The proposed approach applies three more stages which make the domain-relevant analysis to become application specific. These include a detailed model of the application context on legacy production machinery monitoring, together with its interfaces and functionality, implementing threat mitigations combined with a new modular IoT DAQ unit mechanism, validated by functional tests against Denial of Service (DoS) and clone attacks. Thus, to be effective, the design approach is further developed with application-specific functionality. This research demonstrates an instance of this innovative riskaverse design thinking through introducing an IoT device design which is applicable to a wide set of industrial scenarios. A practical showcase example of a specific implementation of the generic IoT design is given through a concrete industrial application that upgrades existing legacy machine tool equipment. The reported work establishes a novel viewpoint for the understanding of IoT security risks and their consequent mitigation, opening a new space of riskaverse designs that can bring significant confidence in data, safety, and security of IoT-enabled industry.Manufacturin

Quantitative Evaluation of the Efficacy of Defence-in-Depth in Critical Infrastructures

This chapter reports on a model-based approach to assessing cyber-risks in a cyber-physical system (CPS), such as power-transmission systems. We demonstrate that quantitative cyber-risk assessment, despite its inherent difficulties, is feasible. In this regard: i) we give experimental evidence (using Monte-Carlo simulation) showing that the losses from a specific cyber-attack type can be established accurately using an abstract model of cyber-attacks – a model constructed without taking into account the details of the specific attack used in the study; ii) we establish the benefits from deploying defence-in-depth (DiD) against failures and cyber-attacks for two types of attackers: a) an attacker unaware of the nature of DiD, and b) an attacker who knows in detail the DiD they face in a particular deployment, and launches attacks sufficient to defeat DiD. This study provides some insight into the benefits of combining design-diversity – to harden some of the protection devices in a CPS – with periodic “proactive recovery” of protection devices. The results are discussed in the context of making evidence-based decisions about maximising the benefits from DiD in a particular CPS

Evolving Bitcoin Custody

The broad topic of this thesis is the design and analysis of Bitcoin custody systems. Both the technology and threat landscape are evolving constantly. Therefore, custody systems, defence strategies, and risk models should be adaptive too. We introduce Bitcoin custody by describing the different types, design principles, phases and functions of custody systems. We review the technology stack of these systems and focus on the fundamentals; key-management and privacy. We present a perspective we call the systems view. It is an attempt to capture the full complexity of a custody system, including technology, people, and processes. We review existing custody systems and standards. We explore Bitcoin covenants. This is a mechanism to enforce constraints on transaction sequences. Although previous work has proposed how to construct and apply Bitcoin covenants, these require modifying the consensus rules of Bitcoin, a notoriously difficult task. We introduce the first detailed exposition and security analysis of a deleted-key covenant protocol, which is compatible with current consensus rules. We demonstrate a range of security models for deleted-key covenants which seem practical, in particular, when applied in autonomous (user-controlled) custody systems. We conclude with a comparative analysis with previous proposals. Covenants are often proclaimed to be an important primitive for custody systems, but no complete design has been proposed to validate that claim. To address this, we propose an autonomous custody system called Ajolote which uses deleted-key covenants to enforce a vault sequence. We evaluate Ajolote with; a model of its state dynamics, a privacy analysis, and a risk model. We propose a threat model for custody systems which captures a realistic attacker for a system with offline devices and user-verification. We perform ceremony analysis to construct the risk model.Comment: PhD thesi

Nature-inspired survivability: Prey-inspired survivability countermeasures for cloud computing security challenges

As cloud computing environments become complex, adversaries have become highly sophisticated and unpredictable. Moreover, they can easily increase attack power and persist longer before detection. Uncertain malicious actions, latent risks, Unobserved or Unobservable risks (UUURs) characterise this new threat domain. This thesis proposes prey-inspired survivability to address unpredictable security challenges borne out of UUURs. While survivability is a well-addressed phenomenon in non-extinct prey animals, applying prey survivability to cloud computing directly is challenging due to contradicting end goals. How to manage evolving survivability goals and requirements under contradicting environmental conditions adds to the challenges. To address these challenges, this thesis proposes a holistic taxonomy which integrate multiple and disparate perspectives of cloud security challenges. In addition, it proposes the TRIZ (Teorija Rezbenija Izobretatelskib Zadach) to derive prey-inspired solutions through resolving contradiction. First, it develops a 3-step process to facilitate interdomain transfer of concepts from nature to cloud. Moreover, TRIZ’s generic approach suggests specific solutions for cloud computing survivability. Then, the thesis presents the conceptual prey-inspired cloud computing survivability framework (Pi-CCSF), built upon TRIZ derived solutions. The framework run-time is pushed to the user-space to support evolving survivability design goals. Furthermore, a target-based decision-making technique (TBDM) is proposed to manage survivability decisions. To evaluate the prey-inspired survivability concept, Pi-CCSF simulator is developed and implemented. Evaluation results shows that escalating survivability actions improve the vitality of vulnerable and compromised virtual machines (VMs) by 5% and dramatically improve their overall survivability. Hypothesis testing conclusively supports the hypothesis that the escalation mechanisms can be applied to enhance the survivability of cloud computing systems. Numeric analysis of TBDM shows that by considering survivability preferences and attitudes (these directly impacts survivability actions), the TBDM method brings unpredictable survivability information closer to decision processes. This enables efficient execution of variable escalating survivability actions, which enables the Pi-CCSF’s decision system (DS) to focus upon decisions that achieve survivability outcomes under unpredictability imposed by UUUR