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In power-constrained wireless networks, extending network lifespan can be achieved through effective energy harvesting techniques. Developing energy-efficient routing protocols in such networks is complex due to the unstable and unpredictable harvested energy. In this regard, reconfigurable Intelligent Surfaces (RIS), capable of manipulating the RF signals, offer enhanced energy harvesting, which in turn minimizes the nodes’ battery recharging time (BRT). Our study presents a novel RIS-assisted routing protocol for multi-hop energy harvesting-based wireless networks. The protocol assigns channels to each hop on the chosen path. It incorporates a rate calculation algorithm that takes into consideration the harvested power, BRT, and channel conditions. More specifically, it assigns a specific rate to every available channel, taking into account the energy available at each node, the estimated time required for battery recharging, and the link status between nodes. By selecting the highest rate per hop, the protocol enhances network throughput while considering energy limits and channel quality. The proposed approach is evaluated and compared with the benchmark, the Min hop scheme, using MATLAB simulations, demonstrating its superior performance.

In the area of machine learning (ML) training data optimization through the construction of compact data, the focus of this paper is presented. The concept of compact data design, aimed at creating an optimized dataset that maximizes benefits without the need to manage a vast amount of complex data, is introduced. Improvements in the methods for optimizing ML training have been incorporated into the development of artificial intelligence (AI) systems. The introduction of understanding ML training datasets as a facet of Explainable AI (XAI), comprehensible to humans, has been made. Among the methods of XAI, the evaluation of input feature importance stands out as a way to enhance the accuracy of complex ML models. The innovative method of compact data design for optimizing ML training through dataset reduction is proposed. The performance of an ML-based malware detection system, along with its variant utilizing compact data, has been assessed, demonstrating the maintenance of 99% accuracy. By applying a 76% reduced input dataset, the speed of ML training with the novel compact data design could be maximized, suggesting that an ML system trained in this manner could achieve statistically equivalent accuracy with only 57% of the original data sample size.

Augmented Reality (AR) has experienced a significant resurgence in popularity and interest in recent years. Despite numerous surveys and reviews in the field, information remains scattered and challenging to consolidate for newcomers. This paper addresses this gap with a comprehensive study of AR’s state of the art. We begin with an introduction to AR and related terminologies. We then describe three enabling of AR and four enhancing technologies. A survey then covers different types of commercial AR hardware with detailed specifications and elaborations. We then address AR software and discuss its basic and advanced features, mapping them to software with feature matrices. Based on a thematic literature analysis, we extract a comprehensive set of guidelines for developing AR solutions into seven themes, from ideation to best practices for implementation and deployment. Finally, we explore some essential challenges in the field of AR. This paper serves as an essential resource for those looking to understand what AR is, what is needed to get started, how to approach development, and what the future holds for AR. By consolidating essential information and providing a solid foundation, this paper aims to help researchers and developers capitalize on the current cycle of interest in AR.

Machine learning models are routinely integrated into process mining pipelines to carry out tasks like data transformation, noise reduction, anomaly detection, classification, and prediction. Often, the design of such models is based on some ad-hoc assumptions about the corresponding data distributions, which are not necessarily in accordance with the non-parametric distributions typically observed with process data. Moreover, mainstream machine-learning approaches tend to ignore the challenges posed by concurrency in operational processes. Data encoding is a key element to smooth the mismatch between these assumptions but its potential is poorly exploited. In this paper, we argue that a deeper understanding of the challenges associated with training machine learning models on process data is essential for establishing a robust integration of process mining and machine learning. Our analysis aims to lay the groundwork for a methodology that aligns machine learning with process mining requirements. We encourage further research in this direction to advance the field and effectively address these critical issues.

Cooperative communications is a core research area in wireless vehicular networks (WVNs), thanks to its capability to provide a certain degree of fading mitigation and to improve spectral efficiency. In a cooperative scenario, the intercept probability of the system can be reduced by optimizing the relay selection scheme in order to select the optimal relay from a set of available relays for data transmission. However, due to the mobility of WVNs, the best relay is often selected in practice based on outdated channel state information (CSI), which in turn affects the overall system performance. Therefore, there is a need for a robust relay selection scheme (RSS) that guarantees a satisfactory overall achievable performance in the presence of an outdated CSI. Motivated by this and considering the advantageous features of autoregressive moving average (ARMA), the proposed contribution models a cooperative vehicular communication scenario with relay selection as a Markov decision process (MDP) and proposes two deep Q-networks (DQNs), namely DQN-RSS and DQN-RSS-ARMA. In the proposed framework, two deep reinforcement learning (RL)-based RSS are trained based on the intercept probability, aiming to select the optimal vehicular relay from a set of multiple relays. We then compare the proposed RSS with the conventional methods and evaluate the performance of the network from the security point of view. Simulation results show that DQN-RSS and DQN-RSS-ARMA perform better than conventional approaches, as they reduce intercept probability by approximately 15% and 30%, respectively, compared to the standard ARMA approach.

Abstract Traditionally, cyber-attack detection relies on reactive, assistive techniques, where pattern-matching algorithms help human experts to scan system logs and network traffic for known virus or malware signatures. Recent research has introduced effective Machine Learning (ML) models for cyber-attack detection, promising to automate the task of detecting, tracking and blocking malware and intruders. Much less effort has been devoted to cyber-attack prediction, especially beyond the short-term time scale of hours and days. Approaches that can forecast attacks likely to happen in the longer term are desirable, as this gives defenders more time to develop and share defensive actions and tools. Today, long-term predictions of attack waves are mostly based on the subjective perceptiveness of experienced human experts, which can be impaired by the scarcity of cyber-security expertise. This paper introduces a novel ML-based approach that leverages unstructured big data and logs to forecast the trend of cyber-attacks at a large scale, years in advance. To this end, we put forward a framework that utilises a monthly dataset of major cyber incidents in 36 countries over the past 11 years, with new features extracted from three major categories of big data sources, namely the scientific research literature, news, blogs, and tweets. Our framework not only identifies future attack trends in an automated fashion, but also generates a threat cycle that drills down into five key phases that constitute the life cycle of all 42 known cyber threats.

The highly dynamic nature of cognitive radio (CR) systems and their stringent latency requirements pose a significant challenge in the realization of efficient intelligent transport systems (ITS). In this paper, we investigate relay selection and opportunistic spectrum access in conjunction with blockchain technology in a secure manner. Specifically, we propose a cross-layer method for secure relay selection, where secondary relays (SRs) are granted access to available spectrum bands based on the balance of their respective virtual wallets. These virtual wallets, which are built based on the SRs' secrecy capacity and their behavior in the network, are the predominant factors that allow SRs to participate in an auction model. To quantify the trustworthiness of the SRs, we formulate a mathematical framework to evaluate the trust value of each SR, which is then leveraged for rewarding or penalizing the SR. Also, we develop an offline blockchain framework to store the information of participating relays and make it available for future operations, to detect reputable and non-reputable relays in the presence of multiple eavesdroppers. The stored reputations of the participating relays are used to develop a self-learning algorithm to exclude the non-reputable relays from the selection group. Finally, we present thorough numerical results to demonstrate the superiority of the proposed system in terms of security, credibility, and integrity.

The major aim of this paper is to explain the data poisoning attacks using label-flipping during the training stage of the electroencephalogram (EEG) signal-based human emotion evaluation systems deploying Machine Learning models from the attackers’ perspective. Human emotion evaluation using EEG signals has consistently attracted a lot of research attention. The identification of human emotional states based on EEG signals is effective to detect potential internal threats caused by insider individuals. Nevertheless, EEG signal-based human emotion evaluation systems have shown several vulnerabilities to data poison attacks. Besides, due to the instability and complexity of the EEG signals, it is challenging to explain and analyze how data poison attacks influence the decision process of EEG signal-based human emotion evaluation systems. In this paper, from the attackers’ side, data poison attacks occurring in the training phases of six different Machine Learning models including Random Forest, Adaptive Boosting (AdaBoost), Extra Trees, XGBoost, Multilayer Perceptron (MLP), and K-Nearest Neighbors (KNN) intrude on the EEG signal-based human emotion evaluation systems using these Machine Learning models. This seeks to reduce the performance of the aforementioned Machine Learning models with regard to the classification task of 4 different human emotions using EEG signals. The findings of the experiments demonstrate that the suggested data poison assaults are model-independently successful, although various models exhibit varying levels of resilience to the attacks. In addition, the data poison attacks on the EEG signal-based human emotion evaluation systems are explained with several Explainable Artificial Intelligence (XAI) methods including Shapley Additive Explanation (SHAP) values, Local Interpretable Model-agnostic Explanations (LIME), and Generated Decision Trees. And the codes of this paper are publicly available on GitHub.

Cloud service providers have been shifting their workloads to microservices to take advantage of their modularity, flexibility, agility, and scalability. However, numerous obstacles remain to achieving the most out of microservice deployments, especially in terms of a Quality of Service (QoS). One possible approach to overcoming these obstacles is to perform autoscaling, which is the ability of cloud infrastructure and services to scale themselves up or down by changing their resource pool. There are two major categories of autoscaling: reactive and proactive. In reactive autoscaling, a feedback loop based on current workload resource usage is implemented to guide resource scaling. One disadvantage of reactive autoscaling is that it may result in inconsistencies between workload demand and resource allocation. In proactive autoscaling, a prediction model is used to guide the future allocation of resources according to current workload metrics. In this paper, a novel proactive autoscaling method is introduced where a two-state, machine-learning Random Forest (RF) model is designed to forecast the future CPU and memory utilization values required by the microservice workload. These predicted values are then used to adjust the resource pool both vertically (hardware resources) and horizontally (microservice replicas). The RF proactive autoscaler has been implemented on a home-grown, open-source microservice prototyping platform and verified using real-world workloads. The experiments show that the RF proactive autoscaler outperforms state-of-the-art ones in terms of allocated resources and latency. The increase in the utilization of allocated resources can reach 90% and the improvement in end-to-end latency, measured by the $95^{th}$ percentile, can reach 95%.

Mobile Edge Computing (MEC) has been proposed as an efficient solution for Mobile crowdsensing (MCS). It allows the parallel collection and processing of data in real time in response to a requested task. A sensing task can be one-time or continuous, with multiple readings collected over time. Integrating MEC and continuous sensing in MCS is challenging due to many factors, including workers’ mobility, edge node placement, task location, Reputation, and data quality. In addition, guarantying cooperative communication in the presence of Anomalous data while maintaining a high quality of service (QoS) is a fundamental issue in continuous sensing. A stability-based edge node selection and anomaly detection-based decision-making framework for worker recruitment in continuous sensing is proposed to address these challenges. It can a) Select the most stable edge nodes in the area of interest (AoI), b) Dynamically cluster the workers according to their movement in the AoI, c) Locally detect and eliminate anomalies within the sensing data, and d) Adopt a feedback mechanism that ensures the cooperation between the edge nodes to eliminate untrustworthy workers in the whole sensing period and future tasks. A real-life dataset is used to evaluate the efficiency of the proposed framework. Results show that the framework outperforms the baselines by achieving higher QoS while introducing lower delay, energy consumption, and less resource consumption.

Multi-omics technologies are being increasingly utilized in angiogenesis research. Yet, computational methods have not been widely used for angiogenic target discovery and prioritization in this field, partly because (wet-lab) vascular biologists are insufficiently familiar with computational biology tools and the opportunities they may offer. With this review, written for vascular biologists who lack expertise in computational methods, we aspire to break boundaries between both fields and to illustrate the potential of these tools for future angiogenic target discovery. We provide a comprehensive survey of currently available computational approaches that may be useful in prioritizing candidate genes, predicting associated mechanisms, and identifying their specificity to endothelial cell subtypes. We specifically highlight tools that use flexible, machine learning frameworks for large-scale data integration and gene prioritization. For each purpose-oriented category of tools, we describe underlying conceptual principles, highlight interesting applications and discuss limitations. Finally, we will discuss challenges and recommend some guidelines which can help to optimize the process of accurate target discovery.

This study discusses the general overview of Timed Efficient Stream Loss-tolerant Authentication (TESLA) protocol, including its properties, key setups, and improvement protocols. The discussion includes a new proposed two-level infinite $\mu $ TESLA (TLI $\mu $ TESLA) protocol that solves the authentication delay and synchronization issues. We theoretically compared TLI $\mu $ TESLA with the previously proposed protocols in terms of security services and showed that the new protocol prevents excessive use of the buffer in the sensor node and reduces the DoS attacks on the network. In addition, it accelerates the authentication process of the broadcasted message with less delay and assures continuous receipt of packets compared to previous TESLA Protocols. We also addressed the challenges faced during the implementation of TESLA protocol and presented the recent solutions and parameter choices for improving the efficiency of the TESLA protocol. Moreover, we focused on utilizing biometric authentication as a promising approach to replace public cryptography in the authentication process.

Data privacy regulations like the EU GDPR allow the use of hashing techniques to anonymize data that may contain personal information. However, cryptographic hashing is well-known to destroy any possibility of performing analytics. Homomorphic crypto-systems allow computing analytics over encrypted data, but cannot guarantee privacy compliance without being coupled with specific privacy-preservation provisions. In this work, we present a novel distance-preserving hashing scheme supporting both regulatory compliance and collaborative analytics. Our scheme achieves regulatory compliance by relying on standard cryptographic hashes while preserving a controllable notion of distance between data points.

This survey presents a comprehensive review of current literature on Explainable Artificial Intelligence (XAI) methods for cyber security applications. Due to the rapid development of Internet-connected systems and Artificial Intelligence in recent years, Artificial Intelligence including Machine Learning (ML) and Deep Learning (DL) has been widely utilized in the fields of cyber security including intrusion detection, malware detection, and spam filtering. However, although Artificial Intelligence-based approaches for the detection and defense of cyber attacks and threats are more advanced and efficient compared to the conventional signature-based and rule-based cyber security strategies, most ML-based techniques and DL-based techniques are deployed in the “black-box” manner, meaning that security experts and customers are unable to explain how such procedures reach particular conclusions. The deficiencies of transparencies and interpretability of existing Artificial Intelligence techniques would decrease human users’ confidence in the models utilized for the defense against cyber attacks, especially in current situations where cyber attacks become increasingly diverse and complicated. Therefore, it is essential to apply XAI in the establishment of cyber security models to create more explainable models while maintaining high accuracy and allowing human users to comprehend, trust, and manage the next generation of cyber defense mechanisms. Although there are papers reviewing Artificial Intelligence applications in cyber security areas and the vast literature on applying XAI in many fields including healthcare, financial services, and criminal justice, the surprising fact is that there are currently no survey research articles that concentrate on XAI applications in cyber security. Therefore, the motivation behind the survey is to bridge the research gap by presenting a detailed and up-to-date survey of XAI approaches applicable to issues in the cyber security field. Our work is the first to propose a clear roadmap for navigating the XAI literature in the context of applications in cyber security.

Continuous and low-cost broadcast authentication is a fundamental security service for distributed sensor networks. This paper presents a novel development of a continuous and low-overhead broadcast authentication protocol named enhanced Infinite timed-efficient stream-loss tolerant authentication (enhanced Inf-TESLA) protocol, based on the Inf-TESLA protocol, whose continuous authentication is limited to the duration of its keychains. The enhanced Inf-TESLA protocol satisfies important security properties, including lower communication and computational overhead; a continuous generation of keychains without the need to establish synchronization packets; scalability to a large network; and resistance to masquerading, modification, man-in-the-middle, and replay attacks. We also highlighted an unaddressed authentication issue in the last packets of the original TESLA protocol and proposed a corresponding solution. We performed a simulation analysis using JAVA and proved that, compared to the Inf-TESLA protocol, the enhanced Inf-TESLA protocol can continuously authenticate packets for the entire lifetime of the receiver. We also compared the enhanced Inf-TESLA protocol with the original TESLA protocol in terms of time complexity and critical authentication processes. The results revealed the superiority of the enhanced Inf-TESLA protocol over the original TESLA protocol in terms of the message authentication code (MAC) value generation time and packet authentication time, which we believe can significantly improve the lifetime and lower the energy expenditure of Internet of Things devices with limited power sources.

IoT devices have grown in popularity in recent years. Statistics show that the number of online IoT devices exceeded 35 billion in 2022. This rapid growth in adoption made these devices an obvious target for malicious actors. Attacks such as botnets and malware injection usually start with a phase of reconnaissance to gather information about the target IoT device before exploitation. In this paper, we introduce a machine-learning-based detection system for reconnaissance attacks based on an explainable ensemble model. Our proposed system aims to detect scanning and reconnaissance activity of IoT devices and counter these attacks at an early stage of the attack campaign. The proposed system is designed to be efficient and lightweight to operate in severely resource-constrained environments. When tested, the implementation of the proposed system delivered an accuracy of 99%. Furthermore, the proposed system showed low false positive and false negative rates at 0.6% and 0.05%, respectively, while maintaining high efficiency and low resource consumption.

Distributed Ledger Technologies (DLTs), like Blockchain, are characterized by features such as transparency, traceability, and security by design. These features make the adoption of Blockchain attractive to enhance information security, privacy, and trustworthiness in very different contexts. This paper provides a comprehensive survey and aims at analyzing and assessing the use of Blockchain in the context of Distributed Trust and Reputation Management Systems (DTRMS). The analysis includes academic research as well as initiatives undertaken in the business domain. The paper defines two taxonomies for both Blockchain and DTRMS and applies a Formal Concept Analysis. Such an approach allowed us to identify the most recurrent and stable features in the current scientific landscape and several important implications among the two taxonomies. The results of the analysis have revealed significant trends and emerging practices in the current implementations that have been distilled into recommendations to guide Blockchain's adoption in DTRMS systems.

Over the past decade, the status of smart cities changed from a fancy area of research into a key policy-making consideration. The increased attention paid to smart cities by decision-makers and politicians prompted in turn a new wave of research in the domain. Urban computing is but one of the developments thus prompted. As this Special Section depicts, it is also one of the most promising domains of research likely to transform the debate on smart cities in the years to come. Urban computing offers a conceptual and methodological framework that integrates the plethora of increasingly sophisticated technologies and their applications in city/urban space. It offers a framework suitable to conceive of andbuild synergies, and to apply in the city/urban space context a variety of approaches and techniques. These include pervasive computing, big data analytics, crowdsourcing, and volunteered geographic information such as user behavior, brand popularity, recommender systems, and social media analytics. In this way, urban computing bears the promise that targeted solutions will be found for the challenges cities/urban spaces face. The objective of this Special Section in IEEE A CCESS was to explore that promise from a variety of complementary interdisciplinary perspectives, including computing/ICT, political economy, public policy, innovation, and entrepreneurship.

Blockchain technology is showing a significant potential to disrupt a number of information technology domains. One of the especially interesting areas for blockchain applications is smart grid. A number of early papers have been published in this area, however, there is no systematic analysis of the impact of blockchain technology on decentralization of smart grids. In this paper, we analyze the standard NIST conceptual model of smart grid domains with respect to the three critical blockchain features: decentralization, trust and incentive. We integrate our findings in order to produce a fully decentralized blockchain-enabled smart grid considering NIST conceptual model. The results of this paper should help smart grid developers and researchers to obtain a conceptual reference of the overall applicability of blockchain technology in smart grid domains and sub-domains. In addition this research will help to identify and guide smart grid blockchain development and research initiatives.

The loss or compromise of any safety critical industrial infrastructure can seriously impact the confidentiality, integrity, or delivery of essential services. Research has shown that such threats often come from malicious insiders. To identify these insiders, survey- and electrocardiogram-based approaches have been proposed; however, these approaches cannot effectively detect or predict any malicious insiders. Recently, electroencephalograms (EEGs) have been suggested as a potential alternative to detect these potential threats. Threat detection using EEG would be highly reliable as it overcomes the limitations of the previous methods. This study proposes a proof of concept for a system wherein a model trained using a deep learning algorithm is employed to evaluate EEG signals to detect insider threats. The algorithm can classify different mental states based on four category risk matrices. In particular, it analyses brainwave signals using long short-term memory (LSTM) designed to remember the previous mental states of each insider and compare them with the current brain state for associated risk-level classification. To evaluate the performance of the proposed system, we performed a comparative analysis using logistic regression (LR)-a predictive analysis technique used to describe the relationship between one dependent binary variable and one or more independent variables-on the same dataset. The experimental results obtained suggest that LSTM can achieve a classification accuracy of more than 80% compared to LR, which yields a classification accuracy of approximately 51%.

Global and local whole genome sequencing of SARS-CoV-2 enables the tracing of domestic and international transmissions. We sequenced Viral RNA from 37 sampled Covid-19 patients with RT-PCR-confirmed infections across the UAE and developed time-resolved phylogenies with 69 local and 3,894 global genome sequences. Furthermore, we investigated specific clades associated with the UAE cohort and, their global diversity, introduction events and inferred domestic and international virus transmissions between January and June 2020. The study comprehensively characterized the genomic aspects of the virus and its spread within the UAE and identified that the prevalence shift of the D614G mutation was due to the later introductions of the G-variant associated with international travel, rather than higher local transmissibility. For clades spanning different emirates, the most recent common ancestors pre-date domestic travel bans. In conclusion, we observe a steep and sustained decline of international transmissions immediately following the introduction of international travel restrictions.

The evolution of 5G and 6G networks has enhanced the ability of massive IoT devices to provide real-time monitoring and interaction with the surrounding environment. Despite recent advances, the necessary security services, such as immediate and continuous authentication, high scalability, and cybersecurity handling of IoT cannot be achieved in a single broadcast authentication protocol. This paper presents a new hybrid protocol called Hybrid Two-level µ-timed-efficient stream loss-tolerant authentication (Hybrid TLI-µTESLA) protocol, which maximizes the benefits of the previous TESLA protocol variants, including scalability support and immediate authentication of Multilevel-µTESLA protocol and continuous authentication with minimal computation overhead of enhanced Inf-TESLA protocol. The inclusion of three different keychains and checking criteria of the packets in the Hybrid TLI-µTESLA protocol enabled resistance against Masquerading, Modification, Man-in-the-Middle, Brute-force, and DoS attacks. A solution for the authentication problem in the first and last packets of the high-level and low-level keychains of the Multilevel-µTESLA protocol was also proposed. The simulation analysis was performed using Java, where we compared the Hybrid TLI-µTESLA protocol with other variants for time complexity and computation overhead at the sender and receiver sides. We also conducted a comparative analysis between two hash functions, SHA-2 and SHA-3, and assessed the feasibility of the proposed protocol in the forthcoming 6G technology. The results demonstrated the superiority of the proposed protocol over other variants in terms of immediate and continuous authentication, scalability, cybersecurity, lifetime, network performance, and compatibility with 5G and 6G IoT generations.

The application of emerging technologies, such as Artificial Intelligence (AI), entails risks that need to be addressed to ensure secure and trustworthy socio-technical infrastructures. Machine Learning (ML), the most developed subfield of AI, allows for improved decision-making processes. However, ML models exhibit specific vulnerabilities that conventional IT systems are not subject to. As systems incorporating ML components become increasingly pervasive, the need to provide security practitioners with threat modeling tailored to the specific AI-ML pipeline is of paramount importance. Currently, there exist no well-established approach accounting for the entire ML life-cycle in the identification and analysis of threats targeting ML techniques. In this paper, we propose an asset-centered methodology—STRIDE-AI—for assessing the security of AI-ML-based systems. We discuss how to apply the FMEA process to identify how assets generated and used at different stages of the ML life-cycle may fail. By adapting Microsoft’s STRIDE approach to the AI-ML domain, we map potential ML failure modes to threats and security properties these threats may endanger. The proposed methodology can assist ML practitioners in choosing the most effective security controls to protect ML assets. We illustrate STRIDE-AI with the help of a real-world use case selected from the TOREADOR H2020 project.

This paper introduces a framework for how to appropriately adopt and adjust machine learning (ML) techniques used to construct electrocardiogram (ECG)-based biometric authentication schemes. The proposed framework can help investigators and developers on ECG-based biometric authentication mechanisms define the boundaries of required datasets and get training data with good quality. To determine the boundaries of datasets, use case analysis is adopted. Based on various application scenarios on ECG-based authentication, three distinct use cases (or authentication categories) are developed. With more qualified training data given to corresponding machine learning schemes, the precision on ML-based ECG biometric authentication mechanisms are increased in consequence. The ECG time slicing technique with the R-peak anchoring is utilized in this framework to acquire ML training data with good quality. In the proposed framework four new measure metrics are introduced to evaluate the quality of the ML training and testing data. In addition, a Matlab toolbox, containing all proposed mechanisms, metrics, and sample data with demonstrations using various ML techniques, is developed and made publicly available for further investigation. For developing ML-based ECG biometric authentication, the proposed framework can guide researchers to prepare the proper ML setups and the ML training datasets along with three identified user case scenarios. For researchers adopting ML techniques to design new schemes in other research domains, the proposed framework is still useful for generating the ML-based training and testing datasets with good quality and utilizing new measure metrics.

Due to the high popularity of Twitter, spammers tend to favor its use in spreading their commercial messages. In the context of detecting twitter spams, different statistical and behavioral analysis approaches were proposed. However, these techniques suffer from many limitations due to: 1) ongoing changes to Twitter's streaming API which constrains access to a user's list of followers/followees; 2) spammer's creativity in building diverse messages; 3) use of embedded links and new accounts; and 4) need for analyzing different characteristics about users without their consent. To address the aforementioned challenges, we propose a novel ontology-based approach for spam detection over Twitter during events by analyzing the relationship between ham user tweets versus spams. Our approach relies solely on public tweet messages while performing the analysis and classification tasks. In this context, ontologies are derived and used to generate a dictionary that validates real tweet messages from random topics. Similarity ratio among the dictionary and tweets is used to reflect the legitimacy of the messages. Experiments conducted on real tweet data illustrate that message-to-message techniques achieved a low detection rate compared with our ontology-based approach which outperforms them by approximately 200%, in addition to promising scalability for large data analysis.

Modern vehicles have evolved to support connected and self-driving capabilities. The concepts such as connected driving, cooperative driving, and intelligent transportation systems have resulted in an increase in the connectivity of vehicles and subsequently created new information security risks. The original vehicular ad-hoc network term is now emerged to a new term, Internet of Vehicles (IoV), which is a typical application of symmetry of Internet of Things (IoT). Vehicle manufacturers address some critical issues such as software bugs or security issues through remote updates, and this gives rise to concerns regarding the security of updated components. Moreover, aftermarket units such as those imposed by transportation authorities or insurance companies expose vehicles to high risk. Software testing aims to ensure that software products are reliable and behave as expected. Many commercial and open-source software products undergo formal certifications to increase users’ confidence in their accuracy, reliability, and security. There are different techniques for software certification, including test-based certification. Testcase repositories are available to support software testing and certification, such as the Linux Test Project for Linux kernel testing. Previous studies performed various testing and experimental evaluation of different parts of modern vehicles to assess the security risks. Due to the lack of trusted testcase repositories and a common approach for testing, testing efforts are performed individually. In this paper, we propose a blockchain-based approach for a testcase repository to support test-based software and security testing and overcome the lack of trusted testcase repositories. The novel concept Proof-of-Validation to manage global state is proposed to manage updates to the repository. The initial work in this study considers the LTP test suite as a use case for the testcase repository. This research work is expected to contribute to the further development in including evidence generation for testing verification.

This paper proposes a novel Deep Learning (DL)-based approach for classifying the radio-access technology (RAT) of wireless emitters. The approach improves computational efficiency and accuracy under harsh channel conditions with respect to existing approaches. Intelligent spectrum monitoring is a crucial enabler for emerging wireless access environments that supports sharing of (and dynamic access to) spectral resources between multiple RATs and user classes. Emitter classification enables monitoring the varying patterns of spectral occupancy across RATs, which is instrumental in optimizing spectral utilization and interference management and supporting efficient enforcement of access regulations. Existing emitter classification approaches successfully leverage convolutional neural networks (CNNs) to recognize RAT visual features in spectrograms and other time-frequency representations; however, the corresponding classification accuracy degrades severely under harsh propagation conditions, and the computational cost of CNNs may limit their adoption in resource-constrained network edge scenarios. In this work, we propose a novel emitter classification solution consisting of a Denoising Autoencoder (DAE), which feeds a CNN classifier with lower dimensionality, denoised representations of channel-corrupted spectrograms. We demonstrate—using a standard-compliant simulation of various RATs including LTE and four latest Wi-Fi standards—that in harsh channel conditions including non-line-of-sight, large scale fading, and mobility-induced Doppler shifts, our proposed solution outperforms a wide range of standalone CNNs and other machine learning models while requiring significantly less computational resources. The maximum achieved accuracy of the emitter classifier is 100%, and the average accuracy is 91% across all the propagation conditions.

Screening baggage against potential threats has become one of the prime aviation security concerns all over the world, where manual detection of prohibited items is a time-consuming and hectic process. Many researchers have developed autonomous systems to recognize baggage threats using security X-ray scans. However, all of these frameworks are vulnerable against screening cluttered and concealed contraband items. Furthermore, to the best of our knowledge, no framework possesses the capacity to recognize baggage threats across multiple scanner specifications without an explicit retraining process. To overcome this, we present a novel meta-transfer learning-driven tensor-shot detector that decomposes the candidate scan into dual-energy tensors and employs a meta-one-shot classification backbone to recognize and localize the cluttered baggage threats. In addition, the proposed detection framework can be well-generalized to multiple scanner specifications due to its capacity to generate object proposals from the unified tensor maps rather than diversified raw scans. We have rigorously evaluated the proposed tensor-shot detector on the publicly available SIXray and GDXray datasets (containing a cumulative of 1,067,381 grayscale and colored baggage X-ray scans). On the SIXray dataset, the proposed framework achieved a mean average precision (mAP) of 0.6457, and on the GDXray dataset, it achieved the precision and F1 score of 0.9441 and 0.9598, respectively. Furthermore, it outperforms state-of-the-art frameworks by 8.03% in terms of mAP, 1.49% in terms of precision, and 0.573% in terms of F1 on the SIXray and GDXray dataset, respectively.

Web service technology provides basic infrastructure for deploying collaborative business processes. Web Service security standards and protocols aim to provide secure communication and conversation between service providers and consumers. Still, for a client calling a Web service it is difficult to ascertain that a particular service instance satisfies—at execution time—specific non-functional properties. In this paper we introduce the notion of certified Web service assurance, characterizing how service consumers can specify the set of security properties that a service should satisfy. Also, we illustrate a mechanism to re-check non-functional properties when the execution context changes. To this end, we introduce the concept of context-aware certificate, and describe a dynamic, context-aware service discovery environment.

This paper proposes a methodology for sensor data interpretation that can combine sensor outputs with contexts represented as sets of annotated business rules. Sensor readings are interpreted to generate events labeled with the appropriate type and level of uncertainty. Then, the appropriate context is selected. Reconciliation of different uncertainty types is achieved by a simple technique that moves uncertainty from events to business rules by generating combs of standard Boolean predicates. Finally, context rules are evaluated together with the events to take a decision. The feasibility of our idea is demonstrated via a case study where a context-reasoning engine has been connected to simulated heartbeat sensors using prerecorded experimental data. We use sensor outputs to identify the proper context of operation of a system and trigger decision-making based on context information.

In the human neoplastic cell lines 5637 and HeLa, recombinant CXCL12 elicited, as expected, downstream signals via both G-protein-dependent and β-arrestin-dependent pathways responsible for inducing a rapid and a late wave, respectively, of ERK1/2 phosphorylation. In contrast, the structural variant [N33A]CXCL12 triggered no β-arrestin-dependent phosphorylation of ERK1/2, and signaled via G protein-dependent pathways alone. Both CXCL12 and [N33A]CXCL12, however, generated signals that transinhibited HER1 phosphorylation via intracellular pathways. 1) Prestimulation of CXCR4/HER1-positive 5637 or HeLa cells with CXCL12 modified the HB-EGF-dependent activation of HER1 by delaying the peak phosphorylation of tyrosine 1068 or 1173. 2) Prestimulation with the synthetic variant [N33A]CXCL12, while preserving CXCR4-related chemotaxis and CXCR4 internalization, abolished HER1 phosphorylation. 3) In cells knockdown of β-arrestin 2, CXCL12 induced a full inhibition of HER1 like [N33A]CXCL12 in non-silenced cells. 4) HER1 phosphorylation was restored as usual by inhibiting PCK, calmodulin or calcineurin, whereas the inhibition of CaMKII had no discernable effect. We conclude that both recombinant CXCL12 and its structural variant [N33A]CXCL12 may transinhibit HER1 via G-proteins/calmodulin/calcineurin, but [N33A]CXCL12 does not activate β-arrestin-dependent ERK1/2 phosphorylation and retains a stronger inhibitory effect. Therefore, we demonstrated that CXCL12 may influence the magnitude and the persistence of signaling downstream of HER1 in turn involved in the proliferative potential of numerous epithelial cancer. In addition, we recognized that [N33A]CXCL12 activates preferentially G-protein-dependent pathways and is an inhibitor of HER1.