Biblio

Remotely connected devices have been adopted in several industrial control systems (ICS) recently due to the advancement in the Industrial Internet of Things (IIoT). This led to new security vulnerabilities because of the expansion of the attack surface. Moreover, cybersecurity incidents in critical infrastructures are increasing. In the ICS, RS-485 cables are widely used in its network for serial communication between each component. However, almost 30 years ago, most of the industrial network protocols implemented over RS-485 such as Modbus were designed without security features. Therefore, anomaly detection is required in industrial control networks to secure communication in the systems. The goal of this paper is to study unsupervised anomaly detection in RS-485 traffic using autoencoders. Five threat scenarios in the physical layer of the industrial control network are proposed. The novelty of our method is that RS-485 traffic is collected indirectly by an analog-to-digital converter. In the experiments, multilayer perceptron (MLP), 1D convolutional, Long Short-Term Memory (LSTM) autoencoders are trained to detect anomalies. The results show that three autoencoders effectively detect anomalous traffic with F1-scores of 0.963, 0.949, and 0.928 respectively. Due to the indirect traffic collection, our method can be practically applied in the industrial control network.

Digitalization has occurred in almost all industries, one of them is health industry. Patients” medical records are now easier to be accessed and managed as all related data are stored in data storages or repositories. However, this system is still under development as number of patients still increasing. Lack of standardization might lead to patients losing their right to control their own data. Therefore, implementing private blockchain system with Self-Sovereign Identity (SSI) concept for identity management in health industry is a viable notion. With SSI, the patients will be benefited from having control over their own medical records and stored with higher security protocol. While healthcare providers will benefit in Know You Customer (KYC) process, if they handle new patients, who move from other healthcare providers. It will eliminate and shorten the process of updating patients' medical records from previous healthcare providers. Therefore, we suggest several flows in implementing blockchain for digital identity in healthcare industry to help overcome lack of patient's data control and KYC in current system. Nevertheless, implementing blockchain on health industry requires full attention from surrounding system and stakeholders to be realized.

The success of the web and the consequent rise in data sharing have made network security a challenge. Attackers from all around the world target PC installations. When an attack is successful, an electronic device's security is jeopardised. The intrusion implicitly includes any sort of behaviours that purport to think twice about the respectability, secrecy, or accessibility of an asset. Information is shielded from unauthorised clients' scrutiny by the integrity of a certain foundation. Accessibility refers to the framework that gives users of the framework true access to information. The word "classification" implies that data within a given frame is shielded from unauthorised access and public display. Consequently, a PC network is considered to be fully completed if the primary objectives of these three standards have been satisfactorily met. To assist in achieving these objectives, Intrusion Detection Systems have been developed with the fundamental purpose of scanning incoming traffic on computer networks for malicious intrusions.

One of the most challenging issues facing Internet of Medical Things (IoMT) cyber defense is the complexity of their ecosystem coupled with the development of cyber-attacks. Medical equipments lack built-in security and are increasingly becoming connected. Moving beyond traditional security solutions becomes a necessity to protect patients and organizations. In order to effectively deal with the security risks of networked medical devices in such a complex and heterogeneous system, we need to measure security risks and prioritize mitigation actions. In this context, we propose a Fuzzy AHP-based method to assess security attributes of connected medical devices and compare different device models against a selected profile with regards to the user requirements. The proposal aims to empower user security awareness to make well-educated decisions.

With the advent of ease of access to the internet and an increase in digital literacy among citizens, digitization of the banking sector has throttled. Countries are now aiming for a cashless society. The introduction of a Unified Payment Interface (UPI) by the National Payments Corporation of India (NPCI) in April 2016 is a game-changer for cashless models. UPI payment model is currently considered the world’s most advanced payment system, and we see many countries adopting this cashless payment mode. With the increase in its popularity, there arises the increased need to strengthen the security posture of the payment solution. In this work, we explore the privacy challenges in the existing data flow of UPI models and propose approaches to preserve the privacy of customers using the Unified Payments Interface.

Electronic control units (ECU) have been widely used in modern resource-constrained automotive systems, com-municating through the controller area network (CAN) bus. However, they are still facing man-in-the-middle attacks in CAN bus due to the absence of a more effective authenti-cation/encryption mechanism. In this paper, to defend against the attacks more effectively, we propose a unified lightweight authenticated encryption that integrates recent prevalent cryp-tography standardization Isap and Ascon.First, we reuse the common permutation block of ISAP and Asconto support authenticated encryption and encryption/decryption. Second, we provide a flexible and independent switch between authenticated encryption and encryption/decryption to support specific application requirements. Third, we adopt standard CAESAR hardware API as the interface standard to support compatibility between different interfaces or platforms. Experimental results show that our proposed unified lightweight authenticated encryption can reduce 26.09% area consumption on Xilinx Artix-7 FPGA board compared with the state-of-the-arts. In addition, the encryption overhead of the proposed design for transferring one CAN data frame is \textbackslashmathbf10.75 \textbackslashmu s using Asconand \textbackslashmathbf72.25 \textbackslashmu s using ISAP at the frequency of 4 MHz on embedded devices.

Current intrusion detection techniques cannot keep up with the increasing amount and complexity of cyber attacks. In fact, most of the traffic is encrypted and does not allow to apply deep packet inspection approaches. In recent years, Machine Learning techniques have been proposed for post-mortem detection of network attacks, and many datasets have been shared by research groups and organizations for training and validation. Differently from the vast related literature, in this paper we propose an early classification approach conducted on CSE-CIC-IDS2018 dataset, which contains both benign and malicious traffic, for the detection of malicious attacks before they could damage an organization. To this aim, we investigated a different set of features, and the sensitivity of performance of five classification algorithms to the number of observed packets. Results show that ML approaches relying on ten packets provide satisfactory results.

The growing number of cybersecurity incidents and the always increasing complexity of cybersecurity attacks is forcing the industry and the research community to develop robust and effective methods to detect and respond to network attacks. Many tools are either built upon a large number of rules and signatures which only large third-party vendors can afford to create and maintain, or are based on complex artificial intelligence engines which, in most cases, still require personalization and fine-tuning using costly service contracts offered by the vendors.This paper introduces an open-source network traffic monitoring system based on the concept of cyberscore, a numerical value that represents how a network activity is considered relevant for spotting cybersecurity-related events. We describe how this technique has been applied in real-life networks and present the result of this evaluation.

In this work we propose a novel deep learning approach for ultra-low bitrate video compression for video conferencing applications. To address the shortcomings of current video compression paradigms when the available bandwidth is extremely limited, we adopt a model-based approach that employs deep neural networks to encode motion information as keypoint displacement and reconstruct the video signal at the decoder side. The overall system is trained in an end-to-end fashion minimizing a reconstruction error on the encoder output. Objective and subjective quality evaluation experiments demonstrate that the proposed approach provides an average bitrate reduction for the same visual quality of more than 60% compared to HEVC.

Web evolution and Web 2.0 social media tools facilitate communication and support the online economy. On the other hand, these tools are actively used by extremist, terrorist and criminal groups. These malicious groups use these new communication channels, such as forums, blogs and social networks, to spread their ideologies, recruit new members, market their malicious goods and raise their funds. They rely on anonymous communication methods that are provided by the new Web. This malicious part of the web is called the “dark web”. Dark web analysis became an active research area in the last few decades, and multiple research studies were conducted in order to understand our enemy and plan for counteract. We have conducted a systematic literature review to identify the state-of-art and open research areas in dark web analysis. We have filtered the available research papers in order to obtain the most relevant work. This filtration yielded 28 studies out of 370. Our systematic review is based on four main factors: the research trends used to analyze dark web, the employed analysis techniques, the analyzed artifacts, and the accuracy and confidence of the available work. Our review results have shown that most of the dark web research relies on content analysis. Also, the results have shown that forum threads are the most analyzed artifacts. Also, the most significant observation is the lack of applying any accuracy metrics or validation techniques by most of the relevant studies. As a result, researchers are advised to consider using acceptance metrics and validation techniques in their future work in order to guarantee the confidence of their study results. In addition, our review has identified some open research areas in dark web analysis which can be considered for future research work.

Dual Connectivity is a key approach to achieving optimization of throughput and latency in heterogeneous networks. Originally a technique introduced by the 3rd Generation Partnership Project (3GPP) for terrestrial communications, it is not been widely explored in satellite systems. In this paper, Dual Connectivity is implemented in a multi-orbital satellite network, where a network model is developed by employing the diversity gains from Dual Connectivity and Carrier Aggregation for the enhancement of satellite uplink capacity. An introduction of software defined network controller is performed at the network layer coupled with a carefully designed hybrid resource allocation algorithm which is implemented strategically. The algorithm performs optimum dynamic flow control and traffic steering by considering the availability of resources and the channel propagation information of the orbital links to arrive at a resource allocation pattern suitable in enhancing uplink system performance. Simulation results are shown to evaluate the achievable gains in throughput and latency; in addition we provide useful insight in the design of multi-orbital satellite networks with implementable scheduler design.

The Controller area network (CAN) is the most extensively used in-vehicle network. It is set to enable communication between a number of electronic control units (ECU) that are widely found in most modern vehicles. CAN is the de facto in-vehicle network standard due to its error avoidance techniques and similar features, but it is vulnerable to various attacks. In this research, we propose a CAN bus intrusion detection system (IDS) based on convolutional neural networks (CNN). U-CAN is a segmentation model that is trained by monitoring CAN traffic data that are preprocessed using hamming distance and saliency detection algorithm. The model is trained and tested using publicly available datasets of raw and reverse-engineered CAN frames. With an F\_1 Score of 0.997, U-CAN can detect DoS, Fuzzy, spoofing gear, and spoofing RPM attacks of the publicly available raw CAN frames. The model trained on reverse-engineered CAN signals that contain plateau attacks also results in a true positive rate and false-positive rate of 0.971 and 0.998, respectively.

Neural program embeddings have demonstrated considerable promise in a range of program analysis tasks, including clone identification, program repair, code completion, and program synthesis. However, most existing methods generate neural program embeddings di-rectly from the program source codes, by learning from features such as tokens, abstract syntax trees, and control flow graphs. This paper takes a fresh look at how to improve program embed-dings by leveraging compiler intermediate representation (IR). We first demonstrate simple yet highly effective methods for enhancing embedding quality by training embedding models alongside source code and LLVM IR generated by default optimization levels (e.g., -02). We then introduce IRGEN, a framework based on genetic algorithms (GA), to identify (near-)optimal sequences of optimization flags that can significantly improve embedding quality. We use IRGEN to find optimal sequences of LLVM optimization flags by performing GA on source code datasets. We then extend a popular code embedding model, CodeCMR, by adding a new objective based on triplet loss to enable a joint learning over source code and LLVM IR. We benchmark the quality of embedding using a rep-resentative downstream application, code clone detection. When CodeCMR was trained with source code and LLVM IRs optimized by findings of IRGEN, the embedding quality was significantly im-proved, outperforming the state-of-the-art model, CodeBERT, which was trained only with source code. Our augmented CodeCMR also outperformed CodeCMR trained over source code and IR optimized with default optimization levels. We investigate the properties of optimization flags that increase embedding quality, demonstrate IRGEN's generalization in boosting other embedding models, and establish IRGEN's use in settings with extremely limited training data. Our research and findings demonstrate that a straightforward addition to modern neural code embedding models can provide a highly effective enhancement.

The authors' industry experiences suggest that compiler warnings, a lightweight version of program analysis, are valuable early bug detection tools. Significant costs are associated with patches and security bulletins for issues that could have been avoided if compiler warnings were addressed. Yet, the industry's attitude towards compiler warnings is mixed. Practices range from silencing all compiler warnings to having a zero-tolerance policy as to any warnings. Current published data indicates that addressing compiler warnings early is beneficial. However, support for this value theory stems from grey literature or is anecdotal. Additional focused research is needed to truly assess the cost-benefit of addressing warnings.

Today, social communication through the Internet has become more popular and has become a crucial part of our daily life. Naturally, sending and receiving various data through the Internet has also grown a lot. Keeping important data secure in transit has become a challenge for individuals and even organizations. Therefore, the trinity of confidentiality, integrity, and availability will be essential, and encryption will definitely be one of the best solutions to this problem. Of course, for image data, it will not be possible to use conventional encryption methods for various reasons, such as the redundancy of image data, the strong correlation of adj acent pixels, and the large volume of image data. Therefore, special methods were developed for image encryption. Among the prevalent methods for image encryption is the use of DNA sequences as well as chaos signals. In this paper, a cycling 3D chaotic map and DNA sequences are used to present a new method for color image encryption. Several experimental analyses were performed on the proposed method, and the results proved that the presented method is secure and efficient.

When storing face biometric samples in accordance with ISO/IEC 19794 as JPEG2000 encoded images, it is necessary to encrypt them for the sake of users’ privacy. Literature suggests selective encryption of JPEG2000 images as fast and efficient method for encryption, the trade-off is that some information is left in plaintext. This could be used by an attacker, in case the encrypted biometric samples are leaked. In this work, we will attempt to utilize a convolutional neural network to perform cryptanalysis of the encryption scheme. That is, we want to assess if there is any information left in plaintext in the selectively encrypted face images which can be used to identify the person. The chosen approach is to train CNNs for biometric face recognition not only with plaintext face samples but additionally conduct a refinement training with partially encrypted data. If this system can successfully utilize encrypted face samples for biometric matching, we can show that the information left in encrypted biometric face samples is information actually usable for biometric recognition.The method works and we can show that a supposedly secure biometric sample still contains identifying information on average over the whole database.

Learning movement control is a fundamental process integral to infant development. However, it is still unclear how infants learn to control leg movement. This work explores the potential of using socially assistive robots to provide real-time adaptive reinforcement learning for infants. Ten 6 to 8-month old typically-developing infants participated in a study where a robot provided reinforcement when the infant’s right leg acceleration fell within the range of 9 to 20 m/s2. If infants increased the proportion of leg accelerations in this band, they were categorized as "performers". Six of the ten participating infants were categorized as performers; the performer subgroup increased the magnitude of acceleration, proportion of target acceleration for right leg, and ratio of right/left leg acceleration peaks within the target acceleration band and their right legs increased movement intensity from the baseline to the contingency session. The results showed infants specifically adjusted their right leg acceleration in response to a robot- provided reward. Further study is needed to understand how to improve human-robot interaction policies for personalized interventions for young infants.

Systems for relative localization in multi-robot systems based on ultra-wideband (UWB) ranging have recently emerged as robust solutions for GNSS-denied environments. Scalability remains one of the key challenges, particularly in adhoc deployments. Recent solutions include dynamic allocation of active and passive localization modes for different robots or nodes in the system. with larger-scale systems becoming more distributed, key research questions arise in the areas of security and trustability of such localization systems. This paper studies the potential integration of collaborative-decision making processes with distributed ledger technologies. Specifically, we investigate the design and implementation of a methodology for running an UWB role allocation algorithm within smart contracts in a blockchain. In previous works, we have separately studied the integration of ROS2 with the Hyperledger Fabric blockchain, and introduced a new algorithm for scalable UWB-based localization. In this paper, we extend these works by (i) running experiments with larger number of mobile robots switching between different spatial configurations and (ii) integrating the dynamic UWB role allocation algorithm into Fabric smart contracts for distributed decision-making in a system of multiple mobile robots. This enables us to deliver the same functionality within a secure and trustable process, with enhanced identity and data access management. Our results show the effectiveness of the UWB role allocation for continuously varying spatial formations of six autonomous mobile robots, while demonstrating a low impact on latency and computational resources of adding the blockchain layer that does not affect the localization process.

With the influx of technology use and human-robot teams, it is important to understand how swift trust is developed within these teams. Given this influx, we plan to study how surface cues (i.e., observable characteristics) and imported information (i.e., knowledge from external sources or personal experiences) effect the development of swift trust. We hypothesize that human-like surface level cues and positive imported information will yield higher swift trust. These findings will help the assignment of human robot teams in the future.

Similar to any spoof detection systems, power grid monitoring systems and devices are subject to various cyberattacks by determined and well-funded adversaries. Many well-publicized real-world cyberattacks on power grid systems have been publicly reported. Phasor Measurement Units (PMUs) networks with Phasor Data Concentrators (PDCs) are the main building blocks of the overall wide area monitoring and situational awareness systems in the power grid. The data between PMUs and PDC(s) are sent through the legacy networks, which are subject to many attack scenarios under with no, or inadequate, countermeasures in protocols, such as IEEE 37.118-2. In this paper, we consider a stealthier data spoofing attack against PMU networks, called a mirroring attack, where an adversary basically injects a copy of a set of packets in reverse order immediately following their original positions, wiping out the correct values. To the best of our knowledge, for the first time in the literature, we consider a more challenging attack both in terms of the strategy and the lower percentage of spoofed attacks. As part of our countermeasure detection scheme, we make use of novel framing approach to make application of a 2D Convolutional Neural Network (CNN)-based approach which avoids the computational overhead of the classical sample-based classification algorithms. Our experimental evaluation results show promising results in terms of both high accuracy and true positive rates even under the aforementioned stealthy adversarial attack scenarios.

Active consumers have now been empowered thanks to the smart grid concept. To avoid fossil fuels, the demand side must provide flexibility through Demand Response events. However, selecting the proper participants for an event can be complex due to response uncertainty. The authors design a Contextual Consumer Rate to identify the trustworthy participants according to previous performances. In the present case study, the authors address the problem of new players with no information. In this way, two different methods were compared to predict their rate. Besides, the authors also refer to the consumer privacy testing of the dataset with and without information that could lead to the participant identification. The results found to prove that, for the proposed methodology, private information does not have a high impact to attribute a rate.

Intrusion Detection System (IDS) is one of the applications to detect intrusions in the network. IDS aims to detect any malicious activities that protect the computer networks from unknown persons or users called attackers. Network security is one of the significant tasks that should provide secure data transfer. Virtualization of networks becomes more complex for IoT technology. Deep Learning (DL) is most widely used by many networks to detect the complex patterns. This is very suitable approaches for detecting the malicious nodes or attacks. Software-Defined Network (SDN) is the default virtualization computer network. Attackers are developing new technology to attack the networks. Many authors are trying to develop new technologies to attack the networks. To overcome these attacks new protocols are required to prevent these attacks. In this paper, a unique deep intrusion detection approach (UDIDA) is developed to detect the attacks in SDN. Performance shows that the proposed approach is achieved more accuracy than existing approaches.

Text steganography aims to embed hidden messages in text information while the goal of text steganalysis is to identify the existence of hidden information or further uncover the embedded message from the text. Steganalysis has received significant attention recently for the security and privacy purpose. In this paper, we develop unsupervised learning approaches for text steganalysis. In particular, two detection models based on deep learning have been proposed to detect hidden information that may be embedded in text from a global and a local perspective. Extensive studies have been carried out on the Chinese poetry text steganography datasets. It is seen that the proposed models show strong empirical performance in steganographic text detection.

Cyber-Physical Systems(CPS) have been experiencing a fast-growing process in recent decades, and related security issues also have become more important than ever before. To design an efficient defensive policy for operators and controllers is the utmost task to be considered. In this paper, a stochastic game-theoretic model is developed to study a CPS security problem by considering the interdependence between cyber and physical spaces of a CPS. The game model is solved with Minimax Q-learning for finding the mixed strategies equilibria. The numerical simulation revealed that the defensive factors and attack cost can affect the policies adopted by the system. From the perspective of the operator of a CPS, increasing successful defense probability in the phrase of disruption will help to improve the probability of defense strategy when there is a correlation between the cyber layer and the physical layer in a CPS. On the contrary side, the system defense probability will decrease as the total cost of the physical layer increases.

Cybersecurity is paramount for all public and private sectors for protecting their information systems, data, and digital assets from cyber-attacks; thus, relying on technology-based protections alone will not achieve this goal. This work examines the role of human factors in cybersecurity by looking at the top-tier conference on Human Factors in Cybersecurity over the past 6 years. A total of 24 articles were selected for the final analysis. Findings show that most of the authors used a quantitative method, where survey was the most used tool for collecting the data, and less attention has been paid to the theoretical research. Besides, three types of users were identified: university-level users, organizational-level users, and unspecified users. Culture is another less investigated aspect, and the samples were biased towards the western community. Moreover, 17 human factors are identified; human awareness, privacy perception, trust perception, behavior, and capability are the top five among them. Also, new insights and recommendations are presented.