CPS Attacks Mitigation Approaches on Power Electronic Systems with Security Challenges for Smart Grid Applications: A Review

This paper presents an inclusive review of the cyber-physical (CP) attacks, vulnerabilities, mitigation approaches on the power electronics and the security challenges for the smart grid applications. With the rapid evolution of the physical systems in the power electronics applications for interfacing renewable energy sources that incorporate with cyber frameworks, the cyber threats have a critical impact on the smart grid performance. Due to the existence of electronic devices in the smart grid applications, which are interconnected through communication networks, these networks may be subjected to severe cyber-attacks by hackers. If this occurs, the digital controllers can be physically isolated from the control loop. Therefore, the cyber-physical systems (CPSs) in the power electronic systems employed in the smart grid need special treatment and security. In this paper, an overview of the power electronics systems security on the networked smart grid from the CP perception, as well as then emphases on prominent CP attack patterns with substantial influence on the power electronics components operation along with analogous defense solutions. Furthermore, appraisal of the CPS threats attacks mitigation approaches, and encounters along the smart grid applications are discussed. Finally, the paper concludes with upcoming trends and challenges in CP security in the smart grid applications

Threat Landscape and Good Practice Guide for Software Defined Networks/5G

5G represents the next major phase of mobile telecommunication systems and network architectures beyond the current 4G standards, aiming at extreme broadband and ultra-robust, low latency connectivity, to enable the programmable connectivity for the Internet of Everything2. Despite the significant debate on the technical specifications and the technological maturity of 5G, which are under discussion in various fora3, 5G is expected to affect positively and significantly several industry sectors ranging from ICT to industry sectors such as car and other manufacturing, health and agriculture in the period up to and beyond 2020. 5G will be driven by the influence of software on network functions, known as Software Defined Networking (SDN) and Network Function Virtualization (NFV). The key concept that underpins SDN is the logical centralization of network control functions by decoupling the control and packet forwarding functionality of the network. NFV complements this vision through the virtualization of these functionalities based on recent advances in general server and enterprise IT virtualization. Considering the technological maturity of the technologies that 5G can leverage on, SDN is the one that is moving faster from development to production. To realize the business potential of SDN/5G, a number of technical issues related to the design and operation of Software Defined Networks need to be addressed. Amongst them, SDN/5G security is one of the key issues, that needs to be addressed comprehensively in order to avoid missing the business opportunities arising from SDN/5G. In this report, we review threats and potential compromises related to the security of SDN/5G networks. More specifically, this report contains a review of the emerging threat landscape of 5G networks with particular focus on Software Defined Networking. It also considers security of NFV and radio network access. To provide a comprehensive account of the emerging threat SDN/5G landscape, this report has identified related network assets and the security threats, challenges and risks arising for these assets. Driven by the identified threats and risks, this report has also reviewed and identified existing security mechanisms and good practices for SDN/5G/NFV, and based on these it has analysed gaps and provided technical, policy and organizational recommendations for proactively enhancing the security of SDN/5G

Secure Authenticated Key Exchange for Enhancing the Security of Routing Protocol for Low-Power and Lossy Networks

The current Routing Protocol for Low Power and Lossy Networks (RPL) standard provides three security modes Unsecured Mode (UM), Preinstalled Secure Mode (PSM), and Authenticated Secure Mode (ASM). The PSM and ASM are designed to prevent external routing attacks and specific replay attacks through an optional replay protection mechanism. RPL\u27s PSM mode does not support key replacement when a malicious party obtains the key via differential cryptanalysis since it considers the key to be provided to nodes during the configuration of the network. This thesis presents an approach to implementing a secure authenticated key exchange mechanism for RPL, which ensures the integrity and authentication of the received key while providing tamper-proof data communication for IoTs in insecure circumstances. Moreover, the proposed approach allows the key to be updated regularly, preventing an attacker from obtaining the key through differential cryptanalysis. However, it is observed that the proposed solution imposes an increase in the cost of communication, computation, power consumption, and memory usage for the network nodes

Security and Privacy in RFID Applications

Concerns about privacy and security may limit the deployment of RFID technology and its benefits, therefore it is important they are identified and adequately addressed. System developers and other market actors are aware of the threats and are developing a number of counter measures. RFID systems can never be absolutely secure but effort needs to be made to ensure a proper balance between the risks and the costs of counter measures. The approach taken to privacy and security should depend on the application area and the context of a specific application. In this chapter, we selected and discussed four application areas, but there are many others where privacy and security issues are relevant.JRC.J.4-Information Societ

Towards a Secure and Resilient Vehicle Design: Methodologies, Principles and Guidelines

The advent of autonomous and connected vehicles has brought new cyber security challenges to the automotive industry. It requires vehicles to be designed to remain dependable in the occurrence of cyber-attacks. A modern vehicle can contain over 150 computers, over 100 million lines of code, and various connection interfaces such as USB ports, WiFi, Bluetooth, and 4G/5G. The continuous technological advancements within the automotive industry allow safety enhancements due to increased control of, e.g., brakes, steering, and the engine. Although the technology is beneficial, its complexity has the side-effect to give rise to a multitude of vulnerabilities that might leverage the potential for cyber-attacks. Consequently, there is an increase in regulations that demand compliance with vehicle cyber security and resilience requirements that state vehicles should be designed to be resilient to cyber-attacks with the capability to detect and appropriately respond to these attacks. Moreover, increasing requirements for automotive digital forensic capabilities are beginning to emerge. Failures in automated driving functions can be caused by hardware and software failures as well as cyber security issues. It is imperative to investigate the cause of these failures. However, there is currently no clear guidance on how to comply with these regulations from a technical perspective.In this thesis, we propose a methodology to predict and mitigate vulnerabilities in vehicles using a systematic approach for security analysis; a methodology further used to develop a framework ensuring a resilient and secure vehicle design concerning a multitude of analyzed vehicle cyber-attacks. Moreover, we review and analyze scientific literature on resilience techniques, fault tolerance, and dependability for attack detection, mitigation, recovery, and resilience endurance. These techniques are then further incorporated into the above-mentioned framework. Finally, to meet requirements to hastily and securely patch the increasing number of bugs in vehicle software, we propose a versatile framework for vehicle software updates

Improving Security and Reliability of Physical Unclonable Functions Using Machine Learning

Physical Unclonable Functions (PUFs) are promising security primitives for device authenti-cation and key generation. Due to the noise influence, reliability is an important performance metric of PUF-based authentication. In the literature, lots of efforts have been devoted to enhancing PUF reliability by using error correction methods such as error-correcting codes and fuzzy extractor. Ho-wever, one property that most of these prior works overlooked is the non-uniform distribution of PUF response across different bits. This wok proposes a two-step methodology to improve the reliability of PUF under noisy conditions. The first step involves acquiring the parameters of PUF models by using machine lear-ning algorithms. The second step then utilizes these obtained parameters to improve the reliability of PUFs by selectively choosing challenge-response pairs (CRPs) for authentication. Two distinct algorithms for improving the reliability of multiplexer (MUX) PUF, i.e., total delay difference thresholding and sensitive bits grouping, are presented. It is important to note that the methodology can be easily applied to other types of PUFs as well. Our experimental results show that the relia-bility of PUF-based authentication can be significantly improved by the proposed approaches. For example, in one experimental setting, the reliability of an MUX PUF is improved from 89.75% to 94.07% using total delay difference thresholding, while 89.30% of generated challenges are stored. As opposed to total delay difference thresholding, sensitive bits grouping possesses higher efficiency, as it can produce reliable CRPs directly. Our experimental results show that the reliability can be improved to 96.91% under the same setting, when we group 12 bits in the challenge vector of a 128-stage MUX PUF. Besides, because the actual noise varies greatly in different conditions, it is hard to predict the error of of each individual PUF response bit. This wok proposes a novel methodology to improve the efficiency of PUF response error correction based on error-rates. The proposed method first obtains the PUF model by using machine learning techniques, which is then used to predict the error-rates. Intuitively, we are inclined to tolerate errors in PUF response bits with relatively higher error-rates. Thus, we propose to treat different PUF response bits with different degrees of error tolerance, according to their estimated error-rates. Specifically, by assigning optimized weights, i.e., 0, 1, 2, 3, and infinity to PUF response bits, while a small portion of high error rates responses are truncated; the other responses are duplicated to a limited number of bits according to error-rates before error correction and a portion of low error-rates responses bypass the error correction as direct keys. The hardware cost for error correction can also be reduced by employing these methods. Response weighting is capable of reducing the false negative and false positive simultaneously. The entropy can also be controlled. Our experimental results show that the response weighting algorithm can reduce not only the false negative from 20.60% to 1.71%, but also the false positive rate from 1.26 × 10−21 to 5.38 × 10−22 for a PUF-based authentication with 127-bit response and 13-bit error correction. Besides, three case studies about the applications of the proposed algorithm are also discussed. Along with the rapid development of hardware security techniques, the revolutionary gro-wth of countermeasures or attacking methods developed by intelligent and adaptive adversaries have significantly complicated the ability to create secure hardware systems. Thus, there is a critical need to (re)evaluate existing or new hardware security techniques against these state-of-the-art attacking methods. With this in mind, this wok presents a novel framework for incorporating active learning techniques into hardware security field. We demonstrate that active learning can significantly im-prove the learning efficiency of PUF modeling attack, which samples the least confident and the most informative challenge-response pair (CRP) for training in each iteration. For example, our ex-perimental results show that in order to obtain a prediction error below 4%, 2790 CRPs are required in passive learning, while only 811 CRPs are required in active learning. The sampling strategies and detailed applications of PUF modeling attack under various environmental conditions are also discussed. When the environment is very noisy, active learning may sample a large number of mis-labeled CRPs and hence result in high prediction error. We present two methods to mitigate the contradiction between informative and noisy CRPs. At last, it is critical to design secure PUF, which can mitigate the countermeasures or modeling attacking from intelligent and adaptive adversaries. Previously, researchers devoted to hiding PUF information by pre- or post processing of PUF challenge/response. However, these methods are still subject to side-channel analysis based hybrid attacks. Methods for increasing the non-linearity of PUF structure, such as feedforward PUF, cascade PUF and subthreshold current PUF, have also been proposed. However, these methods significantly degrade the reliability. Based on the previous work, this work proposes a novel concept, noisy PUF, which achieves modeling attack resistance while maintaining a high degree of reliability for selected CRPs. A possible design of noisy PUF along with the corresponding experimental results is also presented

Roadmap on optical security

Postprint (author's final draft

Trading Indistinguishability-based Privacy and Utility of Complex Data

The collection and processing of complex data, like structured data or infinite streams, facilitates novel applications. At the same time, it raises privacy requirements by the data owners. Consequently, data administrators use privacy-enhancing technologies (PETs) to sanitize the data, that are frequently based on indistinguishability-based privacy definitions. Upon engineering PETs, a well-known challenge is the privacy-utility trade-off. Although literature is aware of a couple of trade-offs, there are still combinations of involved entities, privacy definition, type of data and application, in which we miss valuable trade-offs. In this thesis, for two important groups of applications processing complex data, we study (a) which indistinguishability-based privacy and utility requirements are relevant, (b) whether existing PETs solve the trade-off sufficiently, and (c) propose novel PETs extending the state-of-the-art substantially in terms of methodology, as well as achieved privacy or utility. Overall, we provide four contributions divided into two parts. In the first part, we study applications that analyze structured data with distance-based mining algorithms. We reveal that an essential utility requirement is the preservation of the pair-wise distances of the data items. Consequently, we propose distance-preserving encryption (DPE), together with a general procedure to engineer respective PETs by leveraging existing encryption schemes. As proof of concept, we apply it to SQL log mining, useful for database performance tuning. In the second part, we study applications that monitor query results over infinite streams. To this end, -event differential privacy is state-of-the-art. Here, PETs use mechanisms that typically add noise to query results. First, we study state-of-the-art mechanisms with respect to the utility they provide. Conducting the so far largest benchmark that fulfills requirements derived from limitations of prior experimental studies, we contribute new insights into the strengths and weaknesses of existing mechanisms. One of the most unexpected, yet explainable result, is a baseline supremacy. It states that one of the two baseline mechanisms delivers high or even the best utility. A natural follow-up question is whether baseline mechanisms already provide reasonable utility. So, second, we perform a case study from the area of electricity grid monitoring revealing two results. First, achieving reasonable utility is only possible under weak privacy requirements. Second, the utility measured with application-specific utility metrics decreases faster than the sanitization error, that is used as utility metric in most studies, suggests. As a third contribution, we propose a novel differential privacy-based privacy definition called Swellfish privacy. It allows tuning utility beyond incremental -event mechanism design by supporting time-dependent privacy requirements. Formally, as well as by experiments, we prove that it increases utility significantly. In total, our thesis contributes substantially to the research field, and reveals directions for future research