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Digital networked communications are the key to all Internet-of-Things applications, especially to smart metering systems and the smart grid. In order to ensure a safe operation of systems and the privacy of users, the transport layer security (TLS) protocol, a mature and well standardized solution for secure communications, may be used. We implemented the TLS protocol in its latest version in a way suitable for embedded and resource-constrained systems. This paper outlines the challenges and opportunities of deploying TLS in smart metering and smart grid applications and presents performance results of our TLS implementation. Our analysis shows that given an appropriate implementation and configuration, deploying TLS in constrained smart metering systems is possible with acceptable overhead.
The increasing number of transistors being clocked at high frequencies of modern microprocessors lead to an increasing power consumption, which calls for an active dynamic thermal management. In a research project a system environment has been developed, which includes thermal modeling of the microprocessor in the board system, a software environment to control the characteristics of the system’s timing behavior, and a modified Linux scheduler, which is enhanced with a prediction controller. Measurement results are shown for this development for a Freescale i.MX6Q quad-core microprocessor.
Covert channels have been known for a long time because of their versatile forms of appearance. For nearly every technical improvement or change in technology, such channels have been (re-)created or known methods have been adapted. For example, the introduction of hyperthreading technology has introduced new possibilities for covert communication between malicious processes because they can now share the arithmetic logical unit as well as the L1 and L2 caches, which enable establishing multiple covert channels. Even virtualization, which is known for its isolation of multiple machines, is prone to covert- and side-channel attacks because of the sharing of resources. Therefore, it is not surprising that cloud computing is not immune to this kind of attacks. Moreover, cloud computing with multiple, possibly competing users or customers using the same shared resources may elevate the risk of illegitimate communication. In such a setting, the “air gap” between physical servers and networks disappears, and only the means of isolation and virtual separation serve as a barrier between adversary and victim. In the work at hand, we will provide a survey on vulnerable spots that an adversary could exploit trying to exfiltrate private data from target virtual machines through covert channels in a cloud environment. We will evaluate the feasibility of example attacks and point out proposed mitigation solutions in case they exist.
Exploiting Dissent: Towards Fuzzing-based Differential Black Box Testing of TLS Implementations
(2017)
The Transport Layer Security (TLS) protocol is one of the most widely used security protocols on the internet. Yet do implementations of TLS keep on suffering from bugs and security vulnerabilities. In large part is this due to the protocol's complexity which makes implementing and testing TLS notoriously difficult. In this paper, we present our work on using differential testing as effective means to detect issues in black-box implementations of the TLS handshake protocol. We introduce a novel fuzzing algorithm for generating large and diverse corpuses of mostly-valid TLS handshake messages. Stimulating TLS servers when expecting a ClientHello message, we find messages generated with our algorithm to induce more response discrepancies and to achieve a higher code coverage than those generated with American Fuzzy Lop, TLS-Attacker, or NEZHA. In particular, we apply our approach to OpenssL, BoringSSL, WolfSSL, mbedTLS, and MatrixSSL, and find several real implementation bugs; among them a serious vulnerability in MatrixSSL 3.8.4. Besides do our findings point to imprecision in the TLS specification. We see our approach as present in this paper as the first step towards fully interactive differential testing of black-box TLS protocol implementations. Our software tools are publicly available as open source projects.
The authors claim that location information of stationary ICT components can never be unclassified. They describe how swarm-mapping crowd sourcing is used by Apple and Google to worldwide harvest geo-location information on wireless access points and mobile telecommunication systems' base stations to build up gigantic databases with very exclusive access rights. After having highlighted the known technical facts, in the speculative part of this article, the authors argue how this may impact cyber deterrence strategies of states and alliances understanding the cyberspace as another domain of geostrategic relevance. The states and alliances spectrum of activities due to the potential existence of such databases may range from geopolitical negotiations by institutions understanding international affairs as their core business, mitigation approaches at a technical level, over means of cyber deterrence-by-retaliation.
A physical unclonable function (PUF) is a hardware circuit that produces a random sequence based on its manufacturing-induced intrinsic characteristics. In the past decade, silicon-based PUFs have been extensively studied as a security primitive for identification and authentication. The emerging field of printed electronics (PE) enables novel application fields in the scope of the Internet of Things (IoT) and smart sensors. In this paper, we design and evaluate a printed differential circuit PUF (DiffC-PUF). The simulation data are verified by Monte Carlo analysis. Our design is highly scalable while consisting of a low number of printed transistors. Furthermore, we investigate the best operating point by varying the PUF challenge configuration and analyzing the PUF security metrics in order to achieve high robustness. At the best operating point, the results show areliability of 98.37% and a uniqueness of 50.02%, respectively. This analysis also provides useful and comprehensive insights into the design of hybrid or fully printed PUF circuits. In addition, the proposed printed DiffC-PUF core has been fabricated with electrolyte-gated field-effect transistor technology to verify our design in hardware.
Time-Sensitive Networking (TSN) is the most promising time-deterministic wired communication approach for industrial applications. To extend TSN to "IEEE 802.11" wireless networks two challenging problems must be solved: synchronization and scheduling. This paper is focused on the first one. Even though a few solutions already meet the required synchronization accuracies, they are built on expensive hardware that is not suited for mass market products. While next Wi-Fi generation might support the required functionalities, this paper proposes a novel method that makes possible high-precision wireless synchronization using commercial low-cost components. With the proposed solution, a standard deviation of synchronization error of less than 500 ns can be achieved for many use cases and system loads on both CPU and network. This performance is comparable to modern wired real-time field busses, which makes the developed method a significant contribution for the extension of the TSN protocol to the wireless domain.
Hybrid low-voltage physical unclonable function based on inkjet-printed metal-oxide transistors
(2020)
Modern society is striving for digital connectivity that demands information security. As an emerging technology, printed electronics is a key enabler for novel device types with free form factors, customizability, and the potential for large-area fabrication while being seamlessly integrated into our everyday environment. At present, information security is mainly based on software algorithms that use pseudo random numbers. In this regard, hardware-intrinsic security primitives, such as physical unclonable functions, are very promising to provide inherent security features comparable to biometrical data. Device-specific, random intrinsic variations are exploited to generate unique secure identifiers. Here, we introduce a hybrid physical unclonable function, combining silicon and printed electronics technologies, based on metal oxide thin film devices. Our system exploits the inherent randomness of printed materials due to surface roughness, film morphology and the resulting electrical characteristics. The security primitive provides high intrinsic variation, is non-volatile, scalable and exhibits nearly ideal uniqueness.
Embedded Analog Physical Unclonable Function System to Extract Reliable and Unique Security Keys
(2020)
Internet of Things (IoT) enabled devices have become more and more pervasive in our everyday lives. Examples include wearables transmitting and processing personal data and smart labels interacting with customers. Due to the sensitive data involved, these devices need to be protected against attackers. In this context, hardware-based security primitives such as Physical Unclonable Functions (PUFs) provide a powerful solution to secure interconnected devices. The main benefit of PUFs, in combination with traditional cryptographic methods, is that security keys are derived from the random intrinsic variations of the underlying core circuit. In this work, we present a holistic analog-based PUF evaluation platform, enabling direct access to a scalable design that can be customized to fit the application requirements in terms of the number of required keys and bit width. The proposed platform covers the full software and hardware implementations and allows for tracing the PUF response generation from the digital level back to the internal analog voltages that are directly involved in the response generation procedure. Our analysis is based on 30 fabricated PUF cores that we evaluated in terms of PUF security metrics and bit errors for various temperatures and biases. With an average reliability of 99.20% and a uniqueness of 48.84%, the proposed system shows values close to ideal.
In the last decade, deep learning models for condition monitoring of mechanical systems increasingly gained importance. Most of the previous works use data of the same domain (e.g., bearing type) or of a large amount of (labeled) samples. This approach is not valid for many real-world scenarios from industrial use-cases where only a small amount of data, often unlabeled, is available.
In this paper, we propose, evaluate, and compare a novel technique based on an intermediate domain, which creates a new representation of the features in the data and abstracts the defects of rotating elements such as bearings. The results based on an intermediate domain related to characteristic frequencies show an improved accuracy of up to 32 % on small labeled datasets compared to the current state-of-the-art in the time-frequency domain.
Furthermore, a Convolutional Neural Network (CNN) architecture is proposed for transfer learning. We also propose and evaluate a new approach for transfer learning, which we call Layered Maximum Mean Discrepancy (LMMD). This approach is based on the Maximum Mean Discrepancy (MMD) but extends it by considering the special characteristics of the proposed intermediate domain. The presented approach outperforms the traditional combination of Hilbert–Huang Transform (HHT) and S-Transform with MMD on all datasets for unsupervised as well as for semi-supervised learning. In most of our test cases, it also outperforms other state-of-the-art techniques.
This approach is capable of using different types of bearings in the source and target domain under a wide variation of the rotation speed.