Component Hiding Using Identification and Boundary Blurring Techniques

Protecting software from adversarial attacks is extremely important for DoD technologies. When systems are compromised, the possibility exists for recovery costing millions of dollars and countless labor hours. Circuits implemented on embedded systems utilizing FPGA technology are the result of downloading software for instantiating circuits with specific functions or components. We consider the problem of component hiding a form of software protection. Component identification is a well studied problem. However, we use component identification as a metric for driving the cost of reverse engineering to an unreasonable level. We contribute to protection of software and circuitry by implementing a Java based component identification tool. With this tool, we can characterize time required for carrying out adversarial attacks on unaltered boolean circuitry. To counter component identification methods we utilize boundary blurring techniques which are either semantic preserving or semantic changing in order to prevent component identification methods. Furthermore, we will show these techniques can drive adversarial cost to unreasonable levels preventing compromise of critical systems

SyzTrust: State-aware Fuzzing on Trusted OS Designed for IoT Devices

Trusted Execution Environments (TEEs) embedded in IoT devices provide a deployable solution to secure IoT applications at the hardware level. By design, in TEEs, the Trusted Operating System (Trusted OS) is the primary component. It enables the TEE to use security-based design techniques, such as data encryption and identity authentication. Once a Trusted OS has been exploited, the TEE can no longer ensure security. However, Trusted OSes for IoT devices have received little security analysis, which is challenging from several perspectives: (1) Trusted OSes are closed-source and have an unfavorable environment for sending test cases and collecting feedback. (2) Trusted OSes have complex data structures and require a stateful workflow, which limits existing vulnerability detection tools. To address the challenges, we present SyzTrust, the first state-aware fuzzing framework for vetting the security of resource-limited Trusted OSes. SyzTrust adopts a hardware-assisted framework to enable fuzzing Trusted OSes directly on IoT devices as well as tracking state and code coverage non-invasively. SyzTrust utilizes composite feedback to guide the fuzzer to effectively explore more states as well as to increase the code coverage. We evaluate SyzTrust on Trusted OSes from three major vendors: Samsung, Tsinglink Cloud, and Ali Cloud. These systems run on Cortex M23/33 MCUs, which provide the necessary abstraction for embedded TEEs. We discovered 70 previously unknown vulnerabilities in their Trusted OSes, receiving 10 new CVEs so far. Furthermore, compared to the baseline, SyzTrust has demonstrated significant improvements, including 66% higher code coverage, 651% higher state coverage, and 31% improved vulnerability-finding capability. We report all discovered new vulnerabilities to vendors and open source SyzTrust.Comment: To appear in the IEEE Symposium on Security and Privacy (IEEE S&P) 2024, San Francisco, CA, US

A real time demonstrative analysis of lightweight payload encryption in resource constrained devices based on mqtt

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Kısıtlı cihazların kaynakları, yani bellek (ROM ve RAM), CPU ve pil ömrü (varsa) sınırlıdır. Genellikle, veri toplayan sensörler, makinadan makineye (M2M) veya servisleri ve elektrikli ev aletlerini kontrol eden akıllı cihazlar için puanlar. Bu tür aygıtlar bir ağa bağlandığında "nesnelerin Internet'i" nin (IoT) bir parçasını oluştururlar. Message Queue Telemetry Transport (yani MQTT), hafif, açık, basit, istemci-sunucu yayın/abone mesajlaşma taşıma protokolüdür. Güvenilir iletişim için üç Hizmet Kalitesi (QoS) seviyesini destekleyen çoğu kaynak kısıtlamalı IoT cihazı için kullanışlıdır ve verimlidir. Cihazdan Cihaza (D2D) ve nesnelerin Internet'i (IoT) bağlamları gibi kısıtlı ortamlarda iletişim için gerekli olan bir protokoldür. MQTT protokolü, güvenli soket katmanı (SSL) sertifikalarına dayalı taşıma katmanı güvenliği (TLS) dışında somut güvenlik mekanizmalarından yoksundur. Bununla birlikte, bu güvenlik protokollerinin en hafif değildir ve özellikle kısıtlı cihazlar için ağ yüklerini artırır. IoT cihazlarının yaklaşık %70'inde özellikle de istemci tarafında veri şifrelemesi yoktur ve TLS için mükemmel bir alternatif olabilir. Bu tezde, farklı Hizmet Kalitesi (QoS) ve veri yüklerin değişken boyutu için kısıtlı bir cihaz üzerinde MQTT protokolünün ağ performansı üzerindeki etkisini göstermek için bir deney düzeneği tasarlanmıştır. Bu çalışmanın yeni kısmı, yüklerin istemci tarafında şifrelenmesini ve ağ performansı üzerindeki etkisini kapsıyor. Denemelerde, verilere 128-bits ileileri şifreleme standardı (AES) hafif bir şifreleme uygulanmıştır. Mesajlar, farklı yük boyutlarına dayanan bir komisyoncu sunucusu aracılığıyla gerçek kablolu alt uçtakı yayıncılık istemcisi ve düşük uçtakı abone istemcisi üzerinden MQTT'deki üç farklı QoS seviyesini kullanarak aktarılır. Paketler, şifreleme ve şifre çözme işlem süresinin ölçülmesiyle birlikte uçtan uca gecikme, verimlilik ve mesaj kaybı analiz etmek için yakalanır. Deney sonuçlarına göre, şifrelenmemiş (şifresiz metin) yükün daha düşük bir ağ yük etkisine sahip olduğu ve bu nedenle, yüzde kaybı ve mesaj tesliminde, şifreli yüke göre MQTT'yi kullanarak nispeten daha iyi bir ağ performansı ürettiği sonucuna varılmıştır.Constrained devices are limited in resources namely, memory (ROM and RAM), CPU and battery life (if available). They are often used as sensors that collects data, machine to machine (M2M) or smart devices that control services and electrical appliances. When such devices are connected to a network they form what is called "things" and in a whole, they form part of the "Internet of Things" (IoT). Message Queue Telemetry Transport (MQTT) is a common light weight, open, simple, client-server publish/subscribe messaging transport protocol useful and efficient for most resource constrained IoT devices that supports three Quality of Service (QoS) levels for reliable communication. It is an essential protocol for communication in constrained environments such as Device to Device (D2D) and Internet of Things (IoT) contexts. MQTT protocol is devoid of concrete security mechanisms apart from Transport Layer Security (TLS) based on Secure Socket Layer (SSL) certificates. However, this is not the lightest of security protocols and increases network overheads especially for constrained devices. About 70 % of most ordinary IoT devices also lack data encryption especially at the client-end which could have been a perfect alternative for TLS. In this thesis, an experimental setup is designed to demonstrate the effect on network performance of MQTT protocol on a constrained device for different Quality of Service (QoS) and variable size of payloads. The novel part of this study covers client-side encryption of payloads and its effect over network performance. In the experiments, a lightweight encryption of 128-bits Advanced Encryption Standard (AES) is applied on the data. The messages are transferred using the three different QoS levels in MQTT over real wired low-end publish client and low-end subscriber client via a broker server based on different payload sizes. The packets are captured to analyze end-to-end latency, throughput and message loss along with the measurement of encryption and decryption processing time. According to the results of the experiment, it was concluded that, non-encrypted (plaintext) payload have a lower network load effect and hence produces a relatively better network performance using MQTT in terms of percentage loss and message delivery than the encrypted payload

Development of a model for smart card based access control in multi-user, multi-resource, multi-level access systems

The primary focus of this research is an examination of the issues involved in the granting of access in an environment characterised by multiple users, multiple resources and multiple levels of access permission. Increasing levels of complexity in automotive systems provides opportunities for improving the integration and efficiency of the services provided to the operator. The vehicle lease / hire environment provided a basis for evaluating conditional access to distributed, mobile assets where the principal medium for operating in this environment is the Smart Card. The application of Smart Cards to existing vehicle management systems requires control of access to motor vehicles, control of vehicle operating parameters and secure storage of operating information. The issues addressed include examination of the characteristics of the operating environment, development of a model and design, simulation and evaluation of a multiple application Smart Card. The functions provided by the card include identification and authentication, secure hash and encryption functions which may be applied, in general, to a wide range of access problems. Evaluation of the algorithms implemented indicate that the Smart Card design may be provably secure under single use conditions and conditionally secure under multiple use conditions. The simulation of the card design provided data to support further research and shows the design is practical and able to be implemented on current Smart Card types

Techniques for the reverse engineering of banking malware

Malware attacks are a significant and frequently reported problem, adversely affecting the productivity of organisations and governments worldwide. The well-documented consequences of malware attacks include financial loss, data loss, reputation damage, infrastructure damage, theft of intellectual property, compromise of commercial negotiations, and national security risks. Mitiga-tion activities involve a significant amount of manual analysis. Therefore, there is a need for automated techniques for malware analysis to identify malicious behaviours. Research into automated techniques for malware analysis covers a wide range of activities. This thesis consists of a series of studies: an anal-ysis of banking malware families and their common behaviours, an emulated command and control environment for dynamic malware analysis, a technique to identify similar malware functions, and a technique for the detection of ransomware. An analysis of the nature of banking malware, its major malware families, behaviours, variants, and inter-relationships are provided in this thesis. In doing this, this research takes a broad view of malware analysis, starting with the implementation of the malicious behaviours through to detailed analysis using machine learning. The broad approach taken in this thesis differs from some other studies that approach malware research in a more abstract sense. A disadvantage of approaching malware research without domain knowledge, is that important methodology questions may not be considered. Large datasets of historical malware samples are available for countermea-sures research. However, due to the age of these samples, the original malware infrastructure is no longer available, often restricting malware operations to initialisation functions only. To address this absence, an emulated command and control environment is provided. This emulated environment provides full control of the malware, enabling the capabilities of the original in-the-wild operation, while enabling feature extraction for research purposes. A major focus of this thesis has been the development of a machine learn-ing function similarity method with a novel feature encoding that increases feature strength. This research develops techniques to demonstrate that the machine learning model trained on similarity features from one program can find similar functions in another, unrelated program. This finding can lead to the development of generic similar function classifiers that can be packaged and distributed in reverse engineering tools such as IDA Pro and Ghidra. Further, this research examines the use of API call features for the identi-fication of ransomware and shows that a failure to consider malware analysis domain knowledge can lead to weaknesses in experimental design. In this case, we show that existing research has difficulty in discriminating between ransomware and benign cryptographic software. This thesis by publication, has developed techniques to advance the disci-pline of malware reverse engineering, in order to minimize harm due to cyber-attacks on critical infrastructure, government institutions, and industry.Doctor of Philosoph

Advanced Techniques for Improving the Efficacy of Digital Forensics Investigations

Digital forensics is the science concerned with discovering, preserving, and analyzing evidence on digital devices. The intent is to be able to determine what events have taken place, when they occurred, who performed them, and how they were performed. In order for an investigation to be effective, it must exhibit several characteristics. The results produced must be reliable, or else the theory of events based on the results will be flawed. The investigation must be comprehensive, meaning that it must analyze all targets which may contain evidence of forensic interest. Since any investigation must be performed within the constraints of available time, storage, manpower, and computation, investigative techniques must be efficient. Finally, an investigation must provide a coherent view of the events under question using the evidence gathered. Unfortunately the set of currently available tools and techniques used in digital forensic investigations does a poor job of supporting these characteristics. Many tools used contain bugs which generate inaccurate results; there are many types of devices and data for which no analysis techniques exist; most existing tools are woefully inefficient, failing to take advantage of modern hardware; and the task of aggregating data into a coherent picture of events is largely left to the investigator to perform manually. To remedy this situation, we developed a set of techniques to facilitate more effective investigations. To improve reliability, we developed the Forensic Discovery Auditing Module, a mechanism for auditing and enforcing controls on accesses to evidence. To improve comprehensiveness, we developed ramparser, a tool for deep parsing of Linux RAM images, which provides previously inaccessible data on the live state of a machine. To improve efficiency, we developed a set of performance optimizations, and applied them to the Scalpel file carver, creating order of magnitude improvements to processing speed and storage requirements. Last, to facilitate more coherent investigations, we developed the Forensic Automated Coherence Engine, which generates a high-level view of a system from the data generated by low-level forensics tools. Together, these techniques significantly improve the effectiveness of digital forensic investigations conducted using them

Dynamic Protocol Reverse Engineering a Grammatical Inference Approach

Round trip engineering of software from source code and reverse engineering of software from binary files have both been extensively studied and the state-of-practice have documented tools and techniques. Forward engineering of protocols has also been extensively studied and there are firmly established techniques for generating correct protocols. While observation of protocol behavior for performance testing has been studied and techniques established, reverse engineering of protocol control flow from observations of protocol behavior has not received the same level of attention. State-of-practice in reverse engineering the control flow of computer network protocols is comprised of mostly ad hoc approaches. We examine state-of-practice tools and techniques used in three open source projects: Pidgin, Samba, and rdesktop . We examine techniques proposed by computational learning researchers for grammatical inference. We propose to extend the state-of-art by inferring protocol control flow using grammatical inference inspired techniques to reverse engineer automata representations from captured data flows. We present evidence that grammatical inference is applicable to the problem domain under consideration

Security Risk Management for the Internet of Things

In recent years, the rising complexity of Internet of Things (IoT) systems has increased their potential vulnerabilities and introduced new cybersecurity challenges. In this context, state of the art methods and technologies for security risk assessment have prominent limitations when it comes to large scale, cyber-physical and interconnected IoT systems. Risk assessments for modern IoT systems must be frequent, dynamic and driven by knowledge about both cyber and physical assets. Furthermore, they should be more proactive, more automated, and able to leverage information shared across IoT value chains. This book introduces a set of novel risk assessment techniques and their role in the IoT Security risk management process. Specifically, it presents architectures and platforms for end-to-end security, including their implementation based on the edge/fog computing paradigm. It also highlights machine learning techniques that boost the automation and proactiveness of IoT security risk assessments. Furthermore, blockchain solutions for open and transparent sharing of IoT security information across the supply chain are introduced. Frameworks for privacy awareness, along with technical measures that enable privacy risk assessment and boost GDPR compliance are also presented. Likewise, the book illustrates novel solutions for security certification of IoT systems, along with techniques for IoT security interoperability. In the coming years, IoT security will be a challenging, yet very exciting journey for IoT stakeholders, including security experts, consultants, security research organizations and IoT solution providers. The book provides knowledge and insights about where we stand on this journey. It also attempts to develop a vision for the future and to help readers start their IoT Security efforts on the right foot