A Comprehensive Survey of Attacks without Physical Access Targeting Hardware Vulnerabilities in IoT/IIoT Devices, and Their Detection Mechanisms

With the advances in the field of the Internet of Things (IoT) and Industrial IoT (IIoT), these devices are increasingly used in daily life or industry. To reduce costs related to the time required to develop these devices, security features are usually not considered. This situation creates a major security concern. Many solutions have been proposed to protect IoT/IIoT against various attacks, most of which are based on attacks involving physical access. However, a new class of attacks has emerged targeting hardware vulnerabilities in the micro-architecture that do not require physical access. We present attacks based on micro-architectural hardware vulnerabilities and the side effects they produce in the system. In addition, we present security mechanisms that can be implemented to address some of these attacks. Most of the security mechanisms target a small set of attack vectors or a single specific attack vector. As many attack vectors exist, solutions must be found to protect against a wide variety of threats. This survey aims to inform designers about the side effects related to attacks and detection mechanisms that have been described in the literature. For this purpose, we present two tables listing and classifying the side effects and detection mechanisms based on the given criteria.

ETHOS: a multi-label hate speech detection dataset

Online hate speech is a recent problem in our society that is rising at a steady pace by leveraging the vulnerabilities of the corresponding regimes that characterise most social media platforms. This phenomenon is primarily fostered by offensive comments, either during user interaction or in the form of a posted multimedia context. Nowadays, giant corporations own platforms where millions of users log in every day, and protection from exposure to similar phenomena appears to be necessary to comply with the corresponding legislation and maintain a high level of service quality. A robust and reliable system for detecting and preventing the uploading of relevant content will have a significant impact on our digitally interconnected society. Several aspects of our daily lives are undeniably linked to our social profiles, making us vulnerable to abusive behaviours. As a result, the lack of accurate hate speech detection mechanisms would severely degrade the overall user experience, although its erroneous operation would pose many ethical concerns. In this paper, we present ‘ETHOS’ (multi-labEl haTe speecH detectiOn dataSet), a textual dataset with two variants: binary and multi-label, based on YouTube and Reddit comments validated using the Figure-Eight crowdsourcing platform. Furthermore, we present the annotation protocol used to create this dataset: an active sampling procedure for balancing our data in relation to the various aspects defined. Our key assumption is that, even gaining a small amount of labelled data from such a time-consuming process, we can guarantee hate speech occurrences in the examined material.

ACAMA: Deep Learning-Based Detection and Classification of Android Malware Using API-Based Features

As a great number of IoT and mobile devices are used in our daily lives, the security of mobile devices is being important than ever. If mobile devices which play a key role in connecting devices are exploited by malware to perform malicious behaviors, this can cause serious damage to other devices as well. Hence, a huge research effort has been put forward to prevent such situation. Among them, many studies attempted to detect malware based on APIs used in malware. In general, they showed the high accuracy in detecting malware, but they could not classify malware into detailed categories because their detection mechanisms do not consider the characteristics of each malware category. In this paper, we propose a malware detection and classification approach, named ACAMA, that can detect malware and categorize them with high accuracy. To show the effectiveness of ACAMA, we implement and evaluate it with previously proposed approaches. Our evaluation results demonstrate that ACAMA detects malware with 26% higher accuracy than a previous work. In addition, we show that ACAMA can successfully classify applications that another previous work, AVClass, cannot classify.

Middle- and far-infrared detector based on the plane collection of graphene strips

A concept of a middle- and far-infrared detector has been proposed. The detector is built as a planar collection of parallel graphene strips of different length and width. The feature of the detector scheme is the concurrent utilization of two different detection mechanisms: excitation in the given frequency range of low-frequency interband transitions inherent in armchair graphene strips and antenna resonances of strongly slowed-down surface waves (plasmon polaritons). It has been shown that matching these two resonances results in the essential detector signal amplification, thus providing an alternative way how to solve the problem of the low efficiency of resonant graphene antennas. An approach is proposed to analyze the design of such detectors, as well as to discuss the ways of tuning the both mechanisms.

Interaction of excitons with Cherenkov radiation in WSe2 beyond the non-recoil approximation

Cherenkov radiation from electrons propagating in materials with a high refractive index have applications in particle-detection mechanisms and could be used for high-yield coherent electron beam-driven photon sources. However, the theory of the Cherenkov radiation has been treated up to now using the non-recoil approximation, which neglects the effect of electron deceleration in materials. Here, we report on the effect of electron-beam deceleration on the radiated spectrum and exciton-photon interactions in nm-thick 〖WSe〗_2 crystals. The calculation of the Cherenkov radiation is performed by simulating the kinetic energy of an electron propagating in a thick sample using the Monto Carlo method combined with the Lienard-Wiechert retarded potential. Using this approach, we numerically investigate the interaction between the excitons and generated photons (Cherenkov radiation) beyond the non-recoil approximation and are able to reproduce experimental cathodoluminescence spectra. Our findings pave the way for an accurate design of particle scintillators and detectors, based on the strong-coupling phenomenon.

Taste sensing and sugar detection mechanisms in Drosophila larval primary taste center

Despite the small number of gustatory sense neurons, Drosophila larvae are able to sense a wide range of chemicals. Although evidence for taste multimodality has been provided in single neurons, an overview of gustatory responses at the periphery is missing and hereby we explore whole-organ calcium imaging of the external taste center. We find that neurons can be activated by different combinations of taste modalities including of opposite hedonic valence and identify distinct temporal dynamics of response. Although sweet sensing has not been fully characterized so far in the external larval gustatory organ, we recorded responses elicited by sugar. Previous findings established that larval sugar sensing relies on the Gr43a pharyngeal receptor, but the question remains if external neurons contribute to this taste. Here we postulate that external and internal gustation use distinct and complementary mechanisms in sugar sensing and we identify external sucrose sensing neurons.

An New Efficient Cluster Based Detection Mechanisms for Distributed Denial of Services (DDoS) Attacks

Cluster aggregation of statistical anomaly detection is a mechanism for defending against denial of service attack (dos) and distributed denial-of-service (DDoS) attacks. DDoS attacks are treated as a congestioncontrol problem; because most of the congestion is occurred in the malicious hosts not follow the normal endto- end congestion control. Upstream routers are also notified to drop such packets in order that the router’s resources are used to route legitimate traffic hence term cluster aggregation. If the victim suspects that the cluster aggregations are solved by most of the clients, it increases the complexity of the cluster aggregation. This aggregation solving technique allows the traversal of the attack traffic throughout the intermediate routers before reaching the destination. In this proposal, the aggregation solving mechanism is cluster aggregation to the core routers rather than having at the victim. The router based cluster aggregation mechanism checks the host system whether it is legitimate or not by providing a aggregation to be solved by the suspected host.

Prognostics and Secure Health Management (PSHM) of Electronic Systems in Zero-Trust Environment

The Prognostics and Health Management (PHM) of electronic systems has reached high levels of maturity, with both generic and system-specific PHM techniques available. While these techniques are able to detect naturally occurring faults and predict their impact on the system lifetime, they might not be able to do so if the faults are maliciously induced. Maliciously induced faults could be due to hardware threats; i.e., electronic products that are recycled, remarked, defective, cloned, or tampered (through the insertion of hardware trojans). Increased outsourcing in the fabrication of electronic products has made them susceptible to the insertion of hardware threats in untrusted manufacturing facilities. In many cases, hardware threats are more destructive than software ones as they cannot be remedied by a software patch and are difficult to remove. Hardware threats can cause undesired system behavior such as information leakage, functional failure, maliciously induced aging, etc. The proliferation of hardware threats could outpace the implementation of their detection mechanisms. This might lead to a scenario where all products manufactured by untrusted manufacturing facilities are suspect until verified otherwise. This has parallels to Zero-Trust Architecture, a network security concept developed to help prevent data breaches by removing the notion of trust from an organization's network architecture. To extend the concept of Zero-Trust Architecture from the network to the hardware domain and to ensure hardware security, a paradigm shift from PHM to PSHM (Prognostics and Secure Health Management) is needed. This paper lays out a compelling case for the need for this shift and how the PHM community can adapt its research to ensure the safe, reliable, and secure operation of systems in this challenging new environment.

Secure IIoT-Enabled Industry 4.0

The Industrial Internet of things (IIoT) is the main driving force behind smart manufacturing, industrial automation, and industry 4.0. Conversely, industrial IoT as the evolving technological paradigm is also becoming a compelling target for cyber adversaries. Particularly, advanced persistent threats (APT) and especially botnets are the foremost promising and potential attacks that may throw the complete industrial IoT network into chaos. IIoT-enabled botnets are highly scalable, technologically diverse, and highly resilient to classical and conventional detection mechanisms. Subsequently, we propose a deep learning (DL)-enabled novel hybrid architecture that can efficiently and timely tackle distributed, multivariant, lethal botnet attacks in industrial IoT. The proposed approach is thoroughly evaluated on a current state-of-the-art, publicly available dataset using standard performance evaluation metrics. Moreover, our proposed technique has been precisely verified with our constructed hybrid DL-enabled architectures and current benchmark DL algorithms. Our devised mechanism shows promising results in terms of high detection accuracy with a trivial trade-off in speed efficiency, assuring the proposed scheme as an optimal and legitimate cyber defense in prevalent IIoTs. Besides, we have cross-validated our results to show utterly unbiased performance.