104 research outputs found

    Exploiting loop transformations for the protection of software

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    Il software conserva la maggior parte del know-how che occorre per svilupparlo. Poich\ue9 oggigiorno il software pu\uf2 essere facilmente duplicato e ridistribuito ovunque, il rischio che la propriet\ue0 intellettuale venga violata su scala globale \ue8 elevato. Una delle pi\uf9 interessanti soluzioni a questo problema \ue8 dotare il software di un watermark. Ai watermark si richiede non solo di certificare in modo univoco il proprietario del software, ma anche di essere resistenti e pervasivi. In questa tesi riformuliamo i concetti di robustezza e pervasivit\ue0 a partire dalla semantica delle tracce. Evidenziamo i cicli quali costrutti di programmazione pervasivi e introduciamo le trasformazioni di ciclo come mattone di costruzione per schemi di watermarking pervasivo. Passiamo in rassegna alcune fra tali trasformazioni, studiando i loro principi di base. Infine, sfruttiamo tali principi per costruire una tecnica di watermarking pervasivo. La robustezza rimane una difficile, quanto affascinante, questione ancora da risolvere.Software retains most of the know-how required fot its development. Because nowadays software can be easily cloned and spread worldwide, the risk of intellectual property infringement on a global scale is high. One of the most viable solutions to this problem is to endow software with a watermark. Good watermarks are required not only to state unambiguously the owner of software, but also to be resilient and pervasive. In this thesis we base resiliency and pervasiveness on trace semantics. We point out loops as pervasive programming constructs and we introduce loop transformations as the basic block of pervasive watermarking schemes. We survey several loop transformations, outlining their underlying principles. Then we exploit these principles to build some pervasive watermarking techniques. Resiliency still remains a big and challenging open issue

    Secure execution environments through reconfigurable lightweight cryptographic components

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    Software protection is one of the most important problems in the area of computing as it affects a multitude of players like software vendors, digital content providers, users, and government agencies. There are multiple dimensions to this broad problem of software protection. The most important ones are: (1) protecting software from reverse engineering. (2) protecting software from tamper (or modification). (3) preventing software piracy. (4) verification of integrity of the software;In this thesis we focus on these areas of software protection. The basic requirement to achieve these goals is to provide a secure execution environment, which ensures that the programs behave in the same way as it was designed, and the execution platforms respect certain types of wishes specified by the program;We take the approach of providing secure execution environment through architecture support. We exploit the power of reconfigurable components in achieving this. The first problem we consider is to provide architecture support for obfuscation. This also achieves the goals of tamper resistance, copy protection, and IP protection indirectly. Our approach is based on the intuition that the software is a sequence of instructions (and data) and if the sequence as well the contents are obfuscated then all the required goals can be achieved;The second problem we solve is integrity verification of the software particularly in embedded devices. Our solution is based on the intuition that an obfuscated (permuted) binary image without any dynamic traces reveals very little information about the IP of the program. Moreover, if this obfuscation function becomes a shared secret between the verifier and the embedded device then verification can be performed in a trustworthy manner;Cryptographic components form the underlying building blocks/primitives of any secure execution environment. Our use of reconfigurable components to provide software protection in both Arc 3 D and TIVA led us to an interesting observation about the power of reconfigurable components. Reconfigurable components provide the ability to use the secret (or key) in a much stronger way than the conventional cryptographic designs. This opened up an opportunity for us to explore the use of reconfigurable gates to build cryptographic functions

    Novel Computational Methods for Integrated Circuit Reverse Engineering

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    Production of Integrated Circuits (ICs) has been largely strengthened by globalization. System-on-chip providers are capable of utilizing many different providers which can be responsible for a single task. This horizontal structure drastically improves to time-to-market and reduces manufacturing cost. However, untrust of oversea foundries threatens to dismantle the complex economic model currently in place. Many Intellectual Property (IP) consumers become concerned over what potentially malicious or unspecified logic might reside within their application. This logic which is inserted with the intention of causing harm to a consumer has been referred to as a Hardware Trojan (HT). To help IP consumers, researchers have looked into methods for finding HTs. Such methods tend to rely on high-level information relating to the circuit, which might not be accessible. There is a high possibility that IP is delivered in the gate or layout level. Some services and image processing methods can be leveraged to convert layout level information to gate-level, but such formats are incompatible with detection schemes that require hardware description language. By leveraging standard graph and dynamic programming algorithms a set of tools is developed that can help bridge the gap between gate-level netlist access and HT detection. To help in this endeavor this dissertation focuses on several problems associated with reverse engineering ICs. Logic signal identification is used to find malicious signals, and logic desynthesis is used to extract high level details. Each of the proposed method have their results analyzed for accuracy and runtime. It is found that method for finding logic tends to be the most difficult task, in part due to the degree of heuristic\u27s inaccuracy. With minor improvements moderate sized ICs could have their high-level function recovered within minutes, which would allow for a trained eye or automated methods to more easily detect discrepancies within a circuit\u27s design

    Security of Electrical, Optical and Wireless On-Chip Interconnects: A Survey

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    The advancement of manufacturing technologies has enabled the integration of more intellectual property (IP) cores on the same system-on-chip (SoC). Scalable and high throughput on-chip communication architecture has become a vital component in today's SoCs. Diverse technologies such as electrical, wireless, optical, and hybrid are available for on-chip communication with different architectures supporting them. Security of the on-chip communication is crucial because exploiting any vulnerability would be a goldmine for an attacker. In this survey, we provide a comprehensive review of threat models, attacks, and countermeasures over diverse on-chip communication technologies as well as sophisticated architectures.Comment: 41 pages, 24 figures, 4 table

    Adversarial Deep Learning and Security with a Hardware Perspective

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    Adversarial deep learning is the field of study which analyzes deep learning in the presence of adversarial entities. This entails understanding the capabilities, objectives, and attack scenarios available to the adversary to develop defensive mechanisms and avenues of robustness available to the benign parties. Understanding this facet of deep learning helps us improve the safety of the deep learning systems against external threats from adversaries. However, of equal importance, this perspective also helps the industry understand and respond to critical failures in the technology. The expectation of future success has driven significant interest in developing this technology broadly. Adversarial deep learning stands as a balancing force to ensure these developments remain grounded in the real-world and proceed along a responsible trajectory. Recently, the growth of deep learning has begun intersecting with the computer hardware domain to improve performance and efficiency for resource constrained application domains. The works investigated in this dissertation constitute our pioneering efforts in migrating adversarial deep learning into the hardware domain alongside its parent field of research
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