Biblio

The ever-evolving capabilities of cyber attackers force security administrators to focus on the early identification of emerging threats. Targeted cyber attacks usually consist of several phases, from initial reconnaissance of the network environment to final impact on objectives. This paper investigates the identification of multi-step cyber threat scenarios using kill chain and attack graphs. Kill chain and attack graphs are threat modeling concepts that enable determining weak security defense points. We propose a novel kill chain attack graph that merges kill chain and attack graphs together. This approach determines possible chains of attacker’s actions and their materialization within the protected network. The graph generation uses a categorization of threats according to violated security properties. The graph allows determining the kill chain phase the administrator should focus on and applicable countermeasures to mitigate possible cyber threats. We implemented the proposed approach for a predefined range of cyber threats, especially vulnerability exploitation and network threats. The approach was validated on a real-world use case. Publicly available implementation contains a proof-of-concept kill chain attack graph generator.

Intelligent transportation systems, such as connected vehicles, are able to establish real-time, optimized and collision-free communication with the surrounding ecosystem. Introducing the internet of things (IoT) in connected vehicles relies on deployment of massive scale sensors, actuators, electronic control units (ECUs) and antennas with embedded software and communication technologies. Combined with the lack of designed-in security for sensors and ECUs, this creates challenges for security engineers and architects to identify, understand and analyze threats so that actions can be taken to protect the system assets. This paper proposes a novel STRIDE-based threat model for IoT sensors in connected vehicle networks aimed at addressing these challenges. Using a reference architecture of a connected vehicle, we identify system assets in connected vehicle sub-systems such as devices and peripherals that mostly involve sensors. Moreover, we provide a prioritized set of security recommendations, with consideration to the feasibility and deployment challenges, which enables practical applicability of the developed threat model to help specify security requirements to protect critical assets within the sensor network.

Over recent years, we have seen a significant rise in popularity of autonomous vehicle. Several researches have shown the severity of security threats that autonomous vehicles face -for example, Miller and Valasek (2015) were able to remotely take complete control over a 2014 Jeep Cherokee in a so called "Jeephack" [1]. This paper analyses the threats that the Electrical and Electronic (E/E) architecture of an autonomous vehicle has to face and rank those threats by severity. To achieve this, the Microsoft's STRIDE threat analysis technique was applied and 13 threats were identified. These are sorted by their Common Vulnerability Scoring System (CVSS) scores. Potential mitigation methods are then suggested for the five topmost severe threats.

Machine learning (ML) models are now a key component for many applications. However, machine learning based systems (MLBSs), those systems that incorporate them, have proven vulnerable to various new attacks as a result. Currently, there exists no systematic process for eliciting security requirements for MLBSs that incorporates the identification of adversarial machine learning (AML) threats with those of a traditional non-MLBS. In this research study, we explore the applicability of traditional threat modeling and existing attack libraries in addressing MLBS security in the requirements phase. Using an example MLBS, we examined the applicability of 1) DFD and STRIDE in enumerating AML threats; 2) Microsoft SDL AI/ML Bug Bar in ranking the impact of the identified threats; and 3) the Microsoft AML attack library in eliciting threat mitigations to MLBSs. Such a method has the potential to assist team members, even with only domain specific knowledge, to collaboratively mitigate MLBS threats.

Despite the surging development and utilization of IoT devices, the security of IoT devices is still in infancy. The security pitfalls of IoT devices have made it easy for hackers to take over IoT devices and use them for malicious activities like botnet attacks. With the rampant emergence of IoT devices, botnet attacks are surging. The botnet attacks are not only catastrophic for IoT device users but also for the rest of the world. Therefore, there is a crucial need to identify and mitigate the possible threats in IoT devices during the design phase. Threat modelling is a technique that is used to identify the threats in the earlier stages of the system design activity. In this paper, we propose a threat modelling approach to analyze and mitigate the botnet attacks in an IoT smart home use case. The proposed methodology identifies the development-level and application-level threats in smart home use case using STRIDE and VAST threat modelling methods. Moreover, we reticulate the identified threats with botnet attacks. Finally, we propose the mitigation techniques for all identified threats including the botnet threats.

Cyber-Physical Systems (CPS) attract growing interest from architects and attackers, given their potential effect on privacy and safety of ecosystems and users. Architectural tactics have been proposed as a design-time abstraction useful to guide and evaluate systems design decisions that address specific system qualities, but there is little published evidence of how Security Tactics help to mitigate security threats in the context of Cyber-Physical Systems. This article reports the principled derivation of architectural tactics for an actual SCADA-SAP bridge, where security was the key concern; the key inputs were (1) a well-known taxonomies of architectural tactics, and (2) a detailed record of trade-offs among these tactics. The project architects used client-specified quality attributes to identify relevant tactics in the taxonomy, and information on their trade-offs to guide top-level decisions on system global shape. We venture that all architectural tactics taxonomies should be enriched with explicit trade-offs, allowing architects to compare alternative solutions that seem equally good on principle but are not so in practice.

Complex embedded systems often contain hard to find vulnerabilities which, when exploited, have potential to cause severe damage to the operating environment and the user. Given that threats and vulnerabilities can exist within any layer of the complex eco-system, OEMs face a major challenge to ensure security throughout the device life-cycle To lower the potential risk and damage that vulnerabilities may cause, OEMs typically perform application threat analysis and security modelling. This process typically provides a high level guideline to solving security problems which can then be implemented during design and development. However, this concept presents issues where new threats or unknown vulnerability has been discovered. To address this issue, we propose a policy-based security modelling approach, which utilises a configurable policy engine to apply new policies that counter serious threats. By utilising this approach, the traditional security modelling approaches can be enhanced and the consequences of a new threat greatly reduced. We present a realistic use case of connected car, applying several attack scenarios. By utilising STRIDE threat modelling and DREAD risk assessment model, adequate policies are derived to protect the car assets. This approach poses advantages over the standard approach, allowing a policy update to counter a new threat, which may have otherwise required a product redesign to alleviate the issue under the traditional approach.