Tamper detection in RFID-enabled supply chains using fragile watermarking

While mainstream RFID research has been focused on solving privacy issues, security in general and data tampering in specific is still an open question. This paper analyzes potential security threats especially data tampering in RFID-enabled supply chains and proposes solutions how these threats might be addressed using fragile watermarking technologies. We first survey RFID system and its security problems, and then explain the importance of fragile watermarking schemes for RFID systems and possible applications using fragile watermarking to detect and locate any modification in RFID systems. Finally we suggest possible solutions using fragile watermarking for RFID-enabled supply chain

Tamper Detection in the EPC Network Using Digital Watermarking

National Science Foundation of China [70971112, 70902042

Distortion-Free Watermarking Approach for Relational Database Integrity Checking

Nowadays, internet is becoming a suitable way of accessing the databases. Such data are exposed to various types of attack with the aim to confuse the ownership proofing or the content protection. In this paper, we propose a new approach based on fragile zero watermarking for the authentication of numeric relational data. Contrary to some previous databases watermarking techniques which cause some distortions in the original database and may not preserve the data usability constraints, our approach simply seeks to generate the watermark from the original database. First, the adopted method partitions the database relation into independent square matrix groups. Then, group-based watermarks are securely generated and registered in a trusted third party. The integrity verification is performed by computing the determinant and the diagonal’s minor for each group. As a result, tampering can be localized up to attribute group level. Theoretical and experimental results demonstrate that the proposed technique is resilient against tuples insertion, tuples deletion, and attributes values modification attacks. Furthermore, comparison with recent related effort shows that our scheme performs better in detecting multifaceted attacks

Active and passive approaches for image authentication

Ph.DDOCTOR OF PHILOSOPH

Copyright protection of scalar and multimedia sensor network data using digital watermarking

This thesis records the research on watermarking techniques to address the issue of copyright protection of the scalar data in WSNs and image data in WMSNs, in order to ensure that the proprietary information remains safe between the sensor nodes in both. The first objective is to develop LKR watermarking technique for the copyright protection of scalar data in WSNs. The second objective is to develop GPKR watermarking technique for copyright protection of image data in WMSN

A multi-layer approach to designing secure systems: from circuit to software

In the last few years, security has become one of the key challenges in computing systems. Failures in the secure operations of these systems have led to massive information leaks and cyber-attacks. Case in point, the identity leaks from Equifax in 2016, Spectre and Meltdown attacks to Intel and AMD processors in 2017, Cyber-attacks on Facebook in 2018. These recent attacks have shown that the intruders attack different layers of the systems, from low-level hardware to software as a service(SaaS). To protect the systems, the defense mechanisms should confront the attacks in the different layers of the systems. In this work, we propose four security mechanisms for computing systems: (i ) using backside imaging to detect Hardware Trojans (HTs) in Application Specific Integrated Circuits (ASICs) chips, (ii ) developing energy-efficient reconfigurable cryptographic engines, (iii) examining the feasibility of malware detection using Hardware Performance Counters (HPC). Most of the threat models assume that the root of trust is the hardware running beneath the software stack. However, attackers can insert malicious hardware blocks, i.e. HTs, into the Integrated Circuits (ICs) that provide back-doors to the attackers or leak confidential information. HTs inserted during fabrication are extremely hard to detect since their overheads in performance and power are below the variations in the performance and power caused by manufacturing. In our work, we have developed an optical method that identifies modified or replaced gates in the ICs. We use the near-infrared light to image the ICs because silicon is transparent to near-infrared light and metal reflects infrared light. We leverage the near-infrared imaging to identify the locations of each gate, based on the signatures of metal structures reflected by the lowest metal layer. By comparing the imaged results to the pre-fabrication design, we can identify any modifications, shifts or replacements in the circuits to detect HTs. With the trust of the silicon, the computing system must use secure communication channels for its applications. The low-energy cost devices, such as the Internet of Things (IoT), leverage strong cryptographic algorithms (e.g. AES, RSA, and SHA) during communications. The cryptographic operations cause the IoT devices a significant amount of power. As a result, the power budget limits their applications. To mitigate the high power consumption, modern processors embed these cryptographic operations into hardware primitives. This also improves system performance. The hardware unit embedded into the processor provides high energy-efficiency, low energy cost. However, hardware implementations limit flexibility. The longevity of theIoTs can exceed the lifetime of the cryptographic algorithms. The replacement of the IoT devices is costly and sometimes prohibitive, e.g., monitors in nuclear reactors.In order to reconfigure cryptographic algorithms into hardware, we have developed a system with a reconfigurable encryption engine on the Zedboard platform. The hardware implementation of the engine ensures fast, energy-efficient cryptographic operations. With reliable hardware and secure communication channels in place, the computing systems should detect any malicious behaviors in the processes. We have explored the use of the Hardware Performance Counters (HPCs) in malware detection. HPCs are hardware units that count micro-architectural events, such as cache hits/misses and floating point operations. Anti-virus software is commonly used to detect malware but it also introduces performance overhead. To reduce anti-virus performance overhead, many researchers propose to use HPCs with machine learning models in malware detection. However, it is counter-intuitive that the high-level program behaviors can manifest themselves in low-level statics. We perform experiments using 2 ∼ 3 × larger program counts than the previous works and perform a rigorous analysis to determine whether HPCs can be used to detect malware. Our results show that the False Discovery Rate of malware detection can reach 20%. If we deploy this detection system on a fresh installed Windows 7 systems, among 1,323 binaries, 198 binaries would be flagged as malware

Autonomy, Efficiency, Privacy and Traceability in Blockchain-enabled IoT Data Marketplace

Personal data generated from IoT devices is a new economic asset that individuals can trade to generate revenue on the emerging data marketplaces. Blockchain technology can disrupt the data marketplace and make trading more democratic, trustworthy, transparent and secure. Nevertheless, the adoption of blockchain to create an IoT data marketplace requires consideration of autonomy and efficiency, privacy, and traceability. Conventional centralized approaches are built around a trusted third party that conducts and controls all management operations such as managing contracts, pricing, billing, reputation mechanisms etc, raising concern that providers lose control over their data. To tackle this issue, an efficient, autonomous and fully-functional marketplace system is needed, with no trusted third party involved in operational tasks. Moreover, an inefficient allocation of buyers’ demands on battery-operated IoT devices poses a challenge for providers to serve multiple buyers’ demands simultaneously in real-time without disrupting their SLAs (service level agreements). Furthermore, a poor privacy decision to make personal data accessible to unknown or arbitrary buyers may have adverse consequences and privacy violations for providers. Lastly, a buyer could buy data from one marketplace and without the knowledge of the provider, resell bought data to users registered in other marketplaces. This may either lead to monetary loss or privacy violation for the provider. To address such issues, a data ownership traceability mechanism is essential that can track the change in ownership of data due to its trading within and across marketplace systems. However, data ownership traceability is hard because of ownership ambiguity, undisclosed reselling, and dispersal of ownership across multiple marketplaces. This thesis makes the following novel contributions. First, we propose an autonomous and efficient IoT data marketplace, MartChain, offering key mechanisms for a marketplace leveraging smart contracts to record agreement details, participant ratings, and data prices in blockchain without involving any mediator. Second, MartChain is underpinned by an Energy-aware Demand Selection and Allocation (EDSA) mechanism for optimally selecting and allocating buyers' demands on provider’s IoT devices while satisfying the battery, quality and allocation constraints. EDSA maximizes the revenue of the provider while meeting the buyers’ requirements and ensuring the completion of the selected demands without any interruptions. The proof-of-concept implementation on the Ethereum blockchain shows that our approach is viable and benefits the provider and buyer by creating an autonomous and efficient real-time data trading model. Next, we propose KYBChain, a Know-Your-Buyer in the privacy-aware decentralized IoT data marketplace that performs a multi-faceted assessment of various characteristics of buyers and evaluates their privacy rating. Privacy rating empowers providers to make privacy-aware informed decisions about data sharing. Quantitative analysis to evaluate the utility of privacy rating demonstrates that the use of privacy rating by the providers results in a decrease of data leakage risk and generated revenue, correlating with the classical risk-utility trade-off. Evaluation results of KYBChain on Ethereum reveal that the overheads in terms of gas consumption, throughput and latency introduced by our privacy rating mechanism compared to a marketplace that does not incorporate a privacy rating system are insignificant relative to its privacy gains. Finally, we propose TrailChain which generates a trusted trade trail for tracking the data ownership spanning multiple decentralized marketplaces. Our solution includes mechanisms for detecting any unauthorized data reselling to prevent privacy violations and a fair resell payment sharing scheme to distribute payment among data owners for authorized reselling. We performed qualitative and quantitative evaluations to demonstrate the effectiveness of TrailChain in tracking data ownership using four private Ethereum networks. Qualitative security analysis demonstrates that TrailChain is resilient against several malicious activities and security attacks. Simulations show that our method detects undisclosed reselling within the same marketplace and across different marketplaces. Besides, it also identifies whether the provider has authorized the reselling and fairly distributes the revenue among the data owners at marginal overhead