Secure & Lightweight Distance-Bounding

Distance-bounding is a practical solution to be used in security-sensitive contexts, mainly to prevent relay attacks. The main challenge when designing such protocols is maintaining their inexpensive cryptographic nature, whilst being able to protect against as many, if not all, of the classical threats posed in their context. Moreover, in distance-bounding, some subtle security shortcomings related to the PRF (pseudorandom function) assumption and ingenious attack techniques based on observing verifiers' outputs have recently been put forward. Also, the recent terrorist-fraud by Hancke somehow recalls once more the need to account for noisy communications in the security analysis of distance-bounding. In this paper, we attempt to incorporate the lessons taught by these new developments in our distance-bounding protocol design. The result is a new class of protocols, with increasing levels of security, accommodating the latest advances; at the same time, we preserve the lightweight nature of the design throughout the whole class

Interim research assessment 2003-2005 - Computer Science

This report primarily serves as a source of information for the 2007 Interim Research Assessment Committee for Computer Science at the three technical universities in the Netherlands. The report also provides information for others interested in our research activities

Practical and Provably Secure Distance-Bounding

From contactless payments to remote car unlocking, many applications are vulnerable to relay attacks. Distance bounding protocols are the main practical countermeasure against these attacks. In this paper, we present a formal analysis of SKI, which recently emerged as the first family of lightweight and provably secure distance bounding protocols. More precisely, we explicate a general formalism for distance-bounding protocols, which lead to this practical and provably secure class of protocols (and it could lead to others). We prove that SKI and its variants are provably secure, even under the real-life setting of noisy communications, against the main types of relay attacks: distance-fraud and generalised versions of mafia- and terrorist-fraud. To attain resistance to terrorist-fraud, we reinforce the idea of using secret sharing, combined with the new notion of a leakage scheme. In view of resistance to generalised mafia-frauds (and terrorist-frauds), we present the notion of circular-keying for pseudorandom functions (PRFs); this notion models the employment of a PRF, with possible linear reuse of the key. We also identify the need of PRF masking to fix common mistakes in existing security proofs/claims. Finally, we enhance our design to guarantee resistance to terrorist-fraud in the presence of noise

The impact of information security awareness training on information security behaviour

Information Security awareness initiatives are seen as critical to any information security programme. But, how do we determine the effectiveness of these awareness initiatives? We could get our employees to write a test after the awareness to determine how well they understand the policies, but this does not show how they affect the employee’s on the job behaviour. Does awareness training have a direct influence on the security behaviour of individuals, and what is the direct benefit of awareness training? This research report aims to answer the question: To what extent does information security awareness training influence information security behaviour? Technologies meant to provide security ultimately depend on the effective implementation and operation of these technologies by people. Thus awareness of policies is needed by all individuals in an organisation to ensure that policies are well understood and not misinterpreted. Some researchers have maintained that educating users is futile mainly because it is believed that it is difficult to teach users complex security issues and, secondly, because if security is seen as secondary by the user they will not pay enough attention to it. This research found that, firstly, there is a shortage of in-depth information security awareness research and that behavioural concepts are not properly taken into account for security awareness programmes. There is a shortage of theoretical models explaining how awareness training affects behaviour. Secondly, this research tested a proposed model empirically using system-generated data as indicators of behaviour in a pretest-posttest experimental design. It was found that security awareness training was effective in terms of end-users retaining security knowledge. However, there was no evidence to suggest that security awareness by itself is sufficient to ensure compliant behaviour by endusers. Security awareness training is a necessary, integral component that could influence compliant behaviour, but is not adequate to do so fully. Practitioners must insist that their security awareness programmes are measured in terms of effectiveness and focus on behavioural aspects to complement traditional security awareness initiatives

Machine Learning and Security of Non-Executable Files

Computer malware is a well-known threat in security which, despite the enormous time and effort invested in fighting it, is today more prevalent than ever. Recent years have brought a surge in one particular type: malware embedded in non-executable file formats, e.g., PDF, SWF and various office file formats. The result has been a massive number of infections, owed primarily to the trust that ordinary computer users have in these file formats. In addition, their feature-richness and implementation complexity have created enormous attack surfaces in widely deployed client software, resulting in regular discoveries of new vulnerabilities. The traditional approach to malware detection – signature matching, heuristics and behavioral profiling – has from its inception been a labor-intensive manual task, always lagging one step behind the attacker. With the exponential growth of computers and networks, malware has become more diverse, wide-spread and adaptive than ever, scaling much faster than the available talent pool of human malware analysts. An automated and scalable approach is needed to fill the gap between automated malware adaptation and manual malware detection, and machine learning is emerging as a viable solution. Its branch called adversarial machine learning studies the security of machine learning algorithms and the special conditions that arise when machine learning is applied for security. This thesis is a study of adversarial machine learning in the context of static detection of malware in non-executable file formats. It evaluates the effectiveness, efficiency and security of machine learning applications in this context. To this end, it introduces 3 data-driven detection methods developed using very large, high quality datasets. PJScan detects malicious PDF files based on lexical properties of embedded JavaScript code and is the fastest method published to date. SL2013 extends its coverage to all PDF files, regardless of JavaScript presence, by analyzing the hierarchical structure of PDF logical building blocks and demonstrates excellent performance in a novel long-term realistic experiment. Finally, Hidost generalizes the hierarchical-structure-based feature set to become the first machine-learning-based malware detector operating on multiple file formats. In a comprehensive experimental evaluation on PDF and SWF, it outperforms other academic methods and commercial antivirus systems in detection effectiveness. Furthermore, the thesis presents a framework for security evaluation of machine learning classifiers in a case study performed on an independent PDF malware detector. The results show that the ability to manipulate a part of the classifier’s feature set allows a malicious adversary to disguise malware so that it appears benign to the classifier with a high success rate. The presented methods are released as open-source software.Schadsoftware ist eine gut bekannte Sicherheitsbedrohung. Trotz der enormen Zeit und des Aufwands die investiert werden, um sie zu beseitigen, ist sie heute weiter verbreitet als je zuvor. In den letzten Jahren kam es zu einem starken Anstieg von Schadsoftware, welche in nicht-ausführbaren Dateiformaten, wie PDF, SWF und diversen Office-Formaten, eingebettet ist. Die Folge war eine massive Anzahl von Infektionen, ermöglicht durch das Vertrauen, das normale Rechnerbenutzer in diese Dateiformate haben. Außerdem hat die Komplexität und Vielseitigkeit dieser Dateiformate große Angriffsflächen in weitverbreiteter Klient-Software verursacht, und neue Sicherheitslücken werden regelmäßig entdeckt. Der traditionelle Ansatz zur Erkennung von Schadsoftware – Mustererkennung, Heuristiken und Verhaltensanalyse – war vom Anfang an eine äußerst mühevolle Handarbeit, immer einen Schritt hinter den Angreifern zurück. Mit dem exponentiellen Wachstum von Rechenleistung und Netzwerkgeschwindigkeit ist Schadsoftware diverser, zahlreicher und schneller-anpassend geworden als je zuvor, doch die Verfügbarkeit von menschlichen Schadsoftware-Analysten kann nicht so schnell skalieren. Ein automatischer und skalierbarer Ansatz ist gefragt, und maschinelles Lernen tritt als eine brauchbare Lösung hervor. Ein Bereich davon, Adversarial Machine Learning, untersucht die Sicherheit von maschinellen Lernverfahren und die besonderen Verhältnisse, die bei der Anwendung von machinellem Lernen für Sicherheit entstehen. Diese Arbeit ist eine Studie von Adversarial Machine Learning im Kontext statischer Schadsoftware-Erkennung in nicht-ausführbaren Dateiformaten. Sie evaluiert die Wirksamkeit, Leistungsfähigkeit und Sicherheit von maschinellem Lernen in diesem Kontext. Zu diesem Zweck stellt sie 3 datengesteuerte Erkennungsmethoden vor, die alle auf sehr großen und diversen Datensätzen entwickelt wurden. PJScan erkennt bösartige PDF-Dateien anhand lexikalischer Eigenschaften von eingebettetem JavaScript-Code und ist die schnellste bisher veröffentliche Methode. SL2013 erweitert die Erkennung auf alle PDF-Dateien, unabhängig davon, ob sie JavaScript enthalten, indem es die hierarchische Struktur von logischen PDF-Bausteinen analysiert. Es zeigt hervorragende Leistung in einem neuen, langfristigen und realistischen Experiment. Schließlich generalisiert Hidost den auf hierarchischen Strukturen basierten Merkmalsraum und wurde zum ersten auf maschinellem Lernen basierten Schadsoftware-Erkennungssystem, das auf mehreren Dateiformaten anwendbar ist. In einer umfassenden experimentellen Evaulierung auf PDF- und SWF-Formaten schlägt es andere akademische Methoden und kommerzielle Antiviren-Lösungen bezüglich Erkennungswirksamkeit. Überdies stellt diese Doktorarbeit ein Framework für Sicherheits-Evaluierung von auf machinellem Lernen basierten Klassifikatoren vor und wendet es in einer Fallstudie auf eine unabhängige akademische Schadsoftware-Erkennungsmethode an. Die Ergebnisse zeigen, dass die Fähigkeit, nur einen Teil von Features, die ein Klasifikator verwendet, zu manipulieren, einem Angreifer ermöglicht, Schadsoftware in Dateien so einzubetten, dass sie von der Erkennungsmethode mit hoher Erfolgsrate als gutartig fehlklassifiziert wird. Die vorgestellten Methoden wurden als Open-Source-Software veröffentlicht

Cautiously Optimistic Program Analyses for Secure and Reliable Software

Modern computer systems still have various security and reliability vulnerabilities. Well-known dynamic analyses solutions can mitigate them using runtime monitors that serve as lifeguards. But the additional work in enforcing these security and safety properties incurs exorbitant performance costs, and such tools are rarely used in practice. Our work addresses this problem by constructing a novel technique- Cautiously Optimistic Program Analysis (COPA). COPA is optimistic- it infers likely program invariants from dynamic observations, and assumes them in its static reasoning to precisely identify and elide wasteful runtime monitors. The resulting system is fast, but also ensures soundness by recovering to a conservatively optimized analysis when a likely invariant rarely fails at runtime. COPA is also cautious- by carefully restricting optimizations to only safe elisions, the recovery is greatly simplified. It avoids unbounded rollbacks upon recovery, thereby enabling analysis for live production software. We demonstrate the effectiveness of Cautiously Optimistic Program Analyses in three areas: Information-Flow Tracking (IFT) can help prevent security breaches and information leaks. But they are rarely used in practice due to their high performance overhead (>500% for web/email servers). COPA dramatically reduces this cost by eliding wasteful IFT monitors to make it practical (9% overhead, 4x speedup). Automatic Garbage Collection (GC) in managed languages (e.g. Java) simplifies programming tasks while ensuring memory safety. However, there is no correct GC for weakly-typed languages (e.g. C/C++), and manual memory management is prone to errors that have been exploited in high profile attacks. We develop the first sound GC for C/C++, and use COPA to optimize its performance (16% overhead). Sequential Consistency (SC) provides intuitive semantics to concurrent programs that simplifies reasoning for their correctness. However, ensuring SC behavior on commodity hardware remains expensive. We use COPA to ensure SC for Java at the language-level efficiently, and significantly reduce its cost (from 24% down to 5% on x86). COPA provides a way to realize strong software security, reliability and semantic guarantees at practical costs.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/170027/1/subarno_1.pd

Efficient and Secure Implementations of Lightweight Symmetric Cryptographic Primitives

This thesis is devoted to efficient and secure implementations of lightweight symmetric cryptographic primitives for resource-constrained devices such as wireless sensors and actuators that are typically deployed in remote locations. In this setting, cryptographic algorithms must consume few computational resources and withstand a large variety of attacks, including side-channel attacks. The first part of this thesis is concerned with efficient software implementations of lightweight symmetric algorithms on 8, 16, and 32-bit microcontrollers. A first contribution of this part is the development of FELICS, an open-source benchmarking framework that facilitates the extraction of comparative performance figures from implementations of lightweight ciphers. Using FELICS, we conducted a fair evaluation of the implementation properties of 19 lightweight block ciphers in the context of two different usage scenarios, which are representatives for common security services in the Internet of Things (IoT). This study gives new insights into the link between the structure of a cryptographic algorithm and the performance it can achieve on embedded microcontrollers. Then, we present the SPARX family of lightweight ciphers and describe the impact of software efficiency in the process of shaping three instances of the family. Finally, we evaluate the cost of the main building blocks of symmetric algorithms to determine which are the most efficient ones. The contributions of this part are particularly valuable for designers of lightweight ciphers, software and security engineers, as well as standardization organizations. In the second part of this work, we focus on side-channel attacks that exploit the power consumption or the electromagnetic emanations of embedded devices executing unprotected implementations of lightweight algorithms. First, we evaluate different selection functions in the context of Correlation Power Analysis (CPA) to infer which operations are easy to attack. Second, we show that most implementations of the AES present in popular open-source cryptographic libraries are vulnerable to side-channel attacks such as CPA, even in a network protocol scenario where the attacker has limited control of the input. Moreover, we describe an optimal algorithm for recovery of the master key using CPA attacks. Third, we perform the first electromagnetic vulnerability analysis of Thread, a networking stack designed to facilitate secure communication between IoT devices. The third part of this thesis lies in the area of side-channel countermeasures against power and electromagnetic analysis attacks. We study efficient and secure expressions that compute simple bitwise functions on Boolean shares. To this end, we describe an algorithm for efficient search of expressions that have an optimal cost in number of elementary operations. Then, we introduce optimal expressions for first-order Boolean masking of bitwise AND and OR operations. Finally, we analyze the performance of three lightweight block ciphers protected using the optimal expressions