Backdoor detection systems for embedded devices

A system is said to contain a backdoor when it intentionally includes a means to trigger the execution of functionality that serves to subvert its expected security. Unfortunately, such constructs are pervasive in software and systems today, particularly in the firmware of commodity embedded systems and “Internet of Things” devices. The work presented in this thesis concerns itself with the problem of detecting backdoor-like constructs, specifically those present in embedded device firmware, which, as we show, presents additional challenges in devising detection methodologies. The term “backdoor”, while used throughout the academic literature, by industry, and in the media, lacks a rigorous definition, which exacerbates the challenges in their detection. To this end, we present such a definition, as well as a framework, which serves as a basis for their discovery, devising new detection techniques and evaluating the current state-of-the-art. Further, we present two backdoor detection methodologies, as well as corresponding tools which implement those approaches. Both of these methods serve to automate many of the currently manual aspects of backdoor identification and discovery. And, in both cases, we demonstrate that our approaches are capable of analysing device firmware at scale and can be used to discover previously undocumented real-world backdoors

Data-driven curation, learning and analysis for inferring evolving IoT botnets in the wild

The insecurity of the Internet-of-Things (IoT) paradigm continues to wreak havoc in consumer and critical infrastructure realms. Several challenges impede addressing IoT security at large, including, the lack of IoT-centric data that can be collected, analyzed and correlated, due to the highly heterogeneous nature of such devices and their widespread deployments in Internet-wide environments. To this end, this paper explores macroscopic, passive empirical data to shed light on this evolving threat phenomena. This not only aims at classifying and inferring Internet-scale compromised IoT devices by solely observing such one-way network traffic, but also endeavors to uncover, track and report on orchestrated "in the wild" IoT botnets. Initially, to prepare the effective utilization of such data, a novel probabilistic model is designed and developed to cleanse such traffic from noise samples (i.e., misconfiguration traffic). Subsequently, several shallow and deep learning models are evaluated to ultimately design and develop a multi-window convolution neural network trained on active and passive measurements to accurately identify compromised IoT devices. Consequently, to infer orchestrated and unsolicited activities that have been generated by well-coordinated IoT botnets, hierarchical agglomerative clustering is deployed by scrutinizing a set of innovative and efficient network feature sets. By analyzing 3.6 TB of recent darknet traffic, the proposed approach uncovers a momentous 440,000 compromised IoT devices and generates evidence-based artifacts related to 350 IoT botnets. While some of these detected botnets refer to previously documented campaigns such as the Hide and Seek, Hajime and Fbot, other events illustrate evolving threats such as those with cryptojacking capabilities and those that are targeting industrial control system communication and control services

A systematic development of a secure architecture for the European Rail Traffic Management System

The European Rail Traffic Management System (ERTMS) is a new signalling scheme that is being implemented worldwide with the aim of improving interoperability and cross-border operation. It is also an example of an Industrial Control System, a safety-critical system which, in recent years, has been subject to a number of attacks and threats. In these systems, safety is the primary concern of the system designers, whilst security is sometimes an afterthought. It is therefore prudent to assure the security for current and future threats, which could affect the safe operation of the railway. In this thesis, we present a systematic security analysis of parts of the ERTMS standard, firstly reviewing the security offered by the protocols used in ERTMS using the ProVerif tool. We will then assess the custom MAC algorithm used by the platform and identify issues that exist in each of the ERTMS protocol layers, and aim to propose solutions to those issues. We also identify a challenge presented by the introduction of ERTMS to National Infrastructure Managers surrounding key management, where we also propose a novel key management scheme, TRAKS, which reduces its complexity. We then define a holistic process for asset owners to carry out their own security assessments for their architectures and consider the unique challenges that are presented by Industrial Control Systems and how these can be mitigated to ensure security of these systems. Drawing conclusions from these analyses, we introduce the notion of a `secure architecture' and review the current compliance of ERTMS against this definition, identifying the changes required in order for it to have a secure architecture, both now and also in the future