Biblio

Extracting knowledge from unstructured data silos, a legacy of old applications, is mandatory for improving the governance of today's cities and fostering the creation of smart cities. Texts in natural language often compose such data. Nevertheless, the inference of useful information from a linguistic-computational analysis of natural language data is an open challenge. In this paper, we propose a clustering method to analyze textual data employing the unsupervised machine learning algorithms k-means and hierarchical clustering. We assess different vector representation methods for text, similarity metrics, and the number of clusters that best matches the data. We evaluate the methods using a real database of a public record service of security occurrences. The results show that the k-means algorithm using Euclidean distance extracts non-trivial knowledge, reaching up to 93% accuracy in a set of test samples while identifying the 12 most prevalent occurrence patterns.

Blockchain has revolutionized supply chain system security, especially with Internet of Things integration. Deploying blockchain in the supply chain incorporates immutability, transparency, and traceability mechanisms that promote secure data sharing and interactions between stakeholders in trustless environments. A blockchain-based supply chain as a layered architecture consists of three main layers: supply chain, blockchain, and IoT. This type of system is safer and more transparent, with better traceability than traditional supply chain; however, the system faces several security issues. This paper briefly discusses the primary security challenges related to blockchain-based supply chain systems.

Radio Frequency Identification (RFID) improves the efficiency of managing assets in supply chain applications throughout an entire life cycle or while in transport. Transfer of ownership of RFID-tagged items involves replacing information authorizing the old owner with information authorizing the new owner. In this work, we present a two-party, multiple tag, single-owner protocol for ownership transfer: 2P-mtOTP. This two-party protocol depends only on the communication among the two owners and the tags. Further, 2P-mtOTP is robust to attacks on its security, and it preserves the privacy of the owners and tags. We analyze our work in comparison to recent ownership transfer protocols in terms of security, privacy, and efficiency.

This paper describes a prototype of a novel Permanent Magnetic Elastomer (PME) sheet based skin sensor for robotic applications. Its working principle is to use a Hall effect transducer to measure the change of magnetic field. PME is a polymer that has Neodymium particles distributed inside it, after strong magnetization for anisotropy, the PME acquires strong remanent magnetization that can be comparable to that of a permanent magnet, in this work, we made improvement of the strength of the magnetic field of PME, so it achieved magnetic strength as high as 25 mT when there is no deformation. When external forces apply on the sensor, the deformation of PME causes a change in the magnetic field due to the change in the alignment of the magnetic particles. Compared with other soft magnetic sensors that employ similar technology, we implemented linear regression method to simplify the calibration, so we focus on the point right above the magnetometer. An MLX90393 chip is installed at the bottom of the PME as the magnetometer. Experimental results show that it can measure forces from 0.01–10 N. Calibration is confirmed effective even for shear directions when the surface of PME is less than 15 x 15 mm.

The 6G wireless communication networks are being studied to build a powerful networking system with global coverage, enhanced spectral/energy/cost efficiency, better intelligent level and security. This paper presents a four-in-one networking paradigm named 3CL-Net that would broaden and strengthen the capabilities of current networking by introducing ubiquitous computing, caching, and intelligence over the communication connection to build 6G-required capabilities. To evaluate the practicability of 3CL-Net, this paper designs a platform based on the 3CL-Net architecture. The platform adopts leader-followers structure that could support all functions of 3CL-Net, but separate missions of 3CL-Net into two parts. Moreover, this paper has implemented part of functions as a prototype, on which some experiments are carried out. The results demonstrate that 3CL-Net is potential to be a practical and effective network paradigm to meet future requirements, meanwhile, 3CL-Net could motivate designs of related platforms as well.

Metaverse opens up a new social networking paradigm where people can experience a real interactive feeling without physical space constraints. Social interactions are gradually evolving from text combined with pictures and videos to 3-dimensional virtual reality, making the social experience increasingly physical, implying that more metaverse applications with immersive experiences will be developed in the future. However, the increasing data dimensionality and volume for new metaverse applications present a significant challenge in data acquisition, security, and sharing. Furthermore, metaverse applications require high capacity and ultrareliability for the wireless system to guarantee the quality of user experience, which cannot be addressed in the current fifth-generation system. Therefore, reaching the metaverse is dependent on the revolution in the sixth-generation (6G) wireless communication, which is expected to provide low-latency, high-throughput, and secure services. This article provides a comprehensive view of metaverse applications and investigates the fundamental technologies for the 6G toward metaverse.

Model checking is one of the most commonly used technique in formal verification. However, the exponential scale state space renders exhaustive state enumeration inefficient even for a moderate System on Chip (SoC) design. In this paper, we propose a method that leverages symbolic execution to accelerate state space search and pinpoint security vulnerabilities. We automatically convert the hardware design to functionally equivalent C++ code and utilize the KLEE symbolic execution engine to perform state exploration through heuristic search. To reduce the search space, we symbolically represent essential input signals while making non-critical inputs concrete. Experiment results have demonstrated that our method can precisely identify security vulnerabilities at significantly lower computation cost.

This research investigates efficient architectures for the implementation of the CRYSTALS-Dilithium post-quantum digital signature scheme on reconfigurable hardware, in terms of speed, memory usage, power consumption and resource utilisation. Post quantum digital signature schemes involve a significant computational effort, making efficient hardware accelerators an important contributor to future adoption of schemes. This is work in progress, comprising the establishment of a comprehensive test environment for operational profiling, and the investigation of the use of novel architectures to achieve optimal performance.

In order to control users’ access to the information system, it is necessary to develop a security system that can work in real time and easily reconfigure. This problem can be solved using a fuzzy logic. In this paper the authors propose a fuzzy distribution system for access to the student assessment system, which takes into account the level of user access, identifier and the risk of attack during the request. This approach allows process fuzzy or incomplete information about the user and implement a sufficient level of confidential information protection.

An accurate state estimation (SE) considering increased uncertainty by the high penetration of renewable energy systems (RESs) is more and more important to enhance situational awareness, and the optimal and resilient operation of the renewable-rich power grids. However, it is anticipated that adversaries who plan to manipulate the target power grid will generate attacks that inject inaccurate data to the SE using the vulnerabilities of the devices and networks. Among potential attack types, false data injection attack (FDIA) is gaining popularity since this can bypass bad data detection (BDD) methods implemented in the SE systems. Although numerous FDIA detection methods have been recently proposed, the uncertainty of system configuration that arises by the continuously increasing penetration of RESs has been been given less consideration in the FDIA algorithms. To address this issue, this paper proposes a new FDIA detection scheme that is applicable to renewable energy-rich power grids. A deep learning framework is developed in particular by synergistically constructing a Bidirectional Long Short-Term Memory (Bi-LSTM) with modern smart grid characteristics. The developed framework is evaluated on the IEEE 14-bus system integrating several RESs by using several attack scenarios. A comparison of the numerical results shows that the proposed FDIA detection mechanism outperforms the existing deep learning-based approaches in a renewable energy-rich grid environment.

With the rapid development of blockchain technology, it provides a new technical solution for secure storage of data and trusted computing. However, in the actual application of data traceability, blockchain technology has an obvious disadvantage: the large amount of data stored in the blockchain system will lead to a long response time for users to query data. Higher query delay severely restricts the development of block chain technology in the traceability system. In order to solve this problem, we propose an efficient, secure and low storage overhead blockchain query scheme. Specifically, we design an index structure independent of Merkle tree to support efficient intra-block query, and create new fields in the block header to optimize inter-block query. Compared with several existing schemes, our scheme ensures the security of data. Finally, we simulate and evaluate our proposed scheme. The results show that the proposed scheme has better execution efficiency while reducing additional overhead.

With the advent of the era of Internet of Things (IoT), the increasing data volume leads to storage outsourcing as a new trend for enterprises and individuals. However, data breaches frequently occur, bringing significant challenges to the privacy protection of the outsourced data management system. There is an urgent need for efficient and secure data sharing schemes for the outsourced data management infrastructure, such as the cloud. Therefore, this paper designs a dual-server-based data sharing scheme with data privacy and high efficiency for the cloud, enabling the internal members to exchange their data efficiently and securely. Dual servers guarantee that none of the servers can get complete data independently by adopting secure two-party computation. In our proposed scheme, if the data is destroyed when sending it to the user, the data will not be restored. To prevent the malicious deletion, the data owner adds a random number to verify the identity during the uploading procedure. To ensure data security, the data is transmitted in ciphertext throughout the process by using searchable encryption. Finally, the black-box leakage analysis and theoretical performance evaluation demonstrate that our proposed data sharing scheme provides solid security and high efficiency in practice.

The excess buffering of packets in network elements, also referred to as bufferbloat, results in high latency. Considering the requirements of traffic generated by video conferencing systems like Zoom, cloud rendered gaming platforms like Google Stadia, or even video streaming services such as Netflix, Amazon Prime and YouTube, timeliness of such traffic is important. Ensuring low latency to IP flows with a high throughput calls for the application of Active Queue Management (AQM) schemes. This introduces yet another problem as the co-existence of scalable and classic congestion controls leads to the starvation of classic TCP flows. Technologies such as Low Latency Low Loss Scalable Throughput (L4S) and the corresponding dual queue coupled AQM, DualPI2, provide a robust solution to these problems. However, their deployment on hardware targets such as programmable switches is quite challenging due to the complexity of algorithms and architectural constraints of switching ASICs. In this study, we provide proof of concept implementations of two AQMs that enable the co-existence of scalable and traditional TCP traffic, namely DualPI2 and the preceding single-queue PI2 AQM, on an Intel Tofino switching ASIC. Given the fixed operation of the switch’s traffic manager, we investigate to what extent it is possible to implement a fully RFC-compliant version of the two AQMs on the Tofino ASIC. The study shows that an appropriate split between control and data plane operations is required while we also exploit fixed functionality of the traffic manager to support such solutions.

This paper studies adaptive control of bilateral teleoperation systems with false data injection attacks. The model of bilateral teleoperation system with false data injection attacks is presented. An off-line identification approach based on the least squares is used to detect whether false data injection attacks occur or not in the communication channel. Two Bernoulli distributed variables are introduced to describe the packet dropouts and false data injection attacks in the network. An adaptive controller is proposed to deal stability of the system with false data injection attacks. Some sufficient conditions are proposed to ensure the globally asymptotical stability of the system under false data injection attacks by using Lyapunov functional methods. A bilateral teleoperation system with two degrees of freedom is used to show the effectiveness of gained results.

This paper deals with the implementation of robust control, based on the finite time Lyapunov stability theory, to solve the trajectory tracking problem of an unconventional quadrotor with rotating arms (also known as foldable drone). First, the model of this Unmanned Aerial Vehicle (UAV) taking into consideration the variation of the inertia, the Center of Gravity (CoG) and the control matrix is presented. The theoretical foundations of backstepping control enhanced by a Super-Twisting (ST) algorithm are then discussed. Numerical simulations are performed to demonstrate the effectiveness of the proposed control strategy. Finally, a qualitative and quantitative comparative study is made between the proposed controller and the classical backstepping controller. Overall, the results obtained show that the proposed control approach provides better performance in terms of accuracy and resilience.

Security and Controls with Data privacy in Internet of Things (IoT) devices is not only a present and future technology that is projected to connect a multitude of devices, but it is also a critical survival factor for IoT to thrive. As the quantity of communications increases, massive amounts of data are expected to be generated, posing a threat to both physical device and data security. In the Internet of Things architecture, small and low-powered devices are widespread. Due to their complexity, traditional encryption methods and algorithms are computationally expensive, requiring numerous rounds to encrypt and decode, squandering the limited energy available on devices. A simpler cryptographic method, on the other hand, may compromise the intended confidentiality and integrity. This study examines two lightweight encryption algorithms for Android devices: AES and RSA. On the other hand, the traditional AES approach generates preset encryption keys that the sender and receiver share. As a result, the key may be obtained quickly. In this paper, we present an improved AES approach for generating dynamic keys.

Operating systems are essential software components for any computer. The goal of computer system manu-facturers is to provide a safe operating system that can resist a range of assaults. APTs (Advanced Persistent Threats) are merely one kind of attack used by hackers to penetrate organisations (APT). Here, we will apply the MITRE ATT&CK approach to analyze the security of Windows and Linux. Using the results of a series of vulnerability tests conducted on Windows 7, 8, 10, and Windows Server 2012, as well as Linux 16.04, 18.04, and its most current version, we can establish which operating system offers the most protection against future assaults. In addition, we have shown adversarial reflection in response to threats. We used ATT &CK framework tools to launch attacks on both platforms.

Speech recognition technology has been applied to all aspects of our daily life, but it faces many security issues. One of the major threats is the adversarial audio examples, which may tamper the recognition results of the acoustic speech recognition system (ASR). In this paper, we propose an adversarial detection framework to detect adversarial audio examples. The method is based on the transformer self-attention mechanism. Spectrogram features are extracted from the audio and divided into patches. Position information are embedded and then fed into transformer encoder. Experimental results show that the method achieves good performance with the detection accuracy of above 96.5% under the white-box attacks and blackbox attacks, and noisy circumstances. Even when detecting adversarial examples generated by the unknown attacks, it also achieves satisfactory results.

As one of the defensive solutions against cyberattacks, an Intrusion Detection System (IDS) plays an important role in observing the network state and alerting suspicious actions that can break down the system. There are many attempts of adopting Machine Learning (ML) in IDS to achieve high performance in intrusion detection. However, all of them necessitate a large amount of labeled data. In addition, labeling attack data is a time-consuming and expensive human-labor operation, it makes existing ML methods difficult to deploy in a new system or yields lower results due to a lack of labels on pre-trained data. To address these issues, we propose a semi-supervised IDS model that leverages Generative Adversarial Networks (GANs) and Adversarial AutoEncoder (AAE), called a semi-supervised adversarial autoencoder (SAAE). Our SAAE experimental results on two public datasets for benchmarking ML-based IDS, including NF-CSE-CIC-IDS2018 and NF-UNSW-NB15, demonstrate the effectiveness of AAE and GAN in case of using only a small number of labeled data. In particular, our approach outperforms other ML methods with the highest detection rates in spite of the scarcity of labeled data for model training, even with only 1% labeled data.

Speech enhancement is often applied for speech-based systems due to the proneness of speech signals to additive background noise. While speech processing-based methods are traditionally used for speech enhancement, with advancements in deep learning technologies, many efforts have been made to implement them for speech enhancement. Using deep learning, the networks learn mapping functions from noisy data to clean ones and then learn to reconstruct the clean speech signals. As a consequence, deep learning methods can reduce what is so-called musical noise that is often found in traditional speech enhancement methods. Currently, one popular deep learning architecture for speech enhancement is generative adversarial networks (GAN). However, the cross-entropy loss that is employed in GAN often causes the training to be unstable. So, in many implementations of GAN, the cross-entropy loss is replaced with the least-square loss. In this paper, to improve the training stability of GAN using cross-entropy loss, we propose to use deep regret analytic generative adversarial networks (Dragan) for speech enhancements. It is based on applying a gradient penalty on cross-entropy loss. We also employ relativistic rules to stabilize the training of GAN. Then, we applied it to the least square and Dragan losses. Our experiments suggest that the proposed method improve the quality of speech better than the least-square loss on several objective quality metrics.

The security of manycore systems has become increasingly critical. In system-on-chips (SoCs), Hardware Trojans (HTs) manipulate the functionalities of the routing components to saturate the on-chip network, degrade performance, and result in the leakage of sensitive data. Existing HT detection techniques, including runtime monitoring and state-of-the-art learning-based methods, are unable to timely and accurately identify the implanted HTs, due to the increasingly dynamic and complex nature of on-chip communication behaviors. We propose AGAPE, a novel Generative Adversarial Network (GAN)-based anomaly detection and mitigation method against HTs for secured on-chip communication. AGAPE learns the distribution of the multivariate time series of a number of NoC attributes captured by on-chip sensors under both HT-free and HT-infected working conditions. The proposed GAN can learn the potential latent interactions among different runtime attributes concurrently, accurately distinguish abnormal attacked situations from normal SoC behaviors, and identify the type and location of the implanted HTs. Using the detection results, we apply the most suitable protection techniques to each type of detected HTs instead of simply isolating the entire HT-infected router, with the aim to mitigate security threats as well as reducing performance loss. Simulation results show that AGAPE enhances the HT detection accuracy by 19%, reduces network latency and power consumption by 39% and 30%, respectively, as compared to state-of-the-art security designs.

Blockchain and artificial intelligence are two technologies that, when combined, have the ability to help each other realize their full potential. Blockchains can guarantee the accessibility and consistent admittance to integrity safeguarded big data indexes from numerous areas, allowing AI systems to learn more effectively and thoroughly. Similarly, artificial intelligence (AI) can be used to offer new consensus processes, and hence new methods of engaging with Blockchains. When it comes to sensitive data, such as corporate, healthcare, and financial data, various security and privacy problems arise that must be properly evaluated. Interaction with Blockchains is vulnerable to data credibility checks, transactional data leakages, data protection rules compliance, on-chain data privacy, and malicious smart contracts. To solve these issues, new security and privacy-preserving technologies are being developed. AI-based blockchain data processing, either based on AI or used to defend AI-based blockchain data processing, is emerging to simplify the integration of these two cutting-edge technologies.

The recent 5G networks aim to provide higher speed, lower latency, and greater capacity; therefore, compared to the previous mobile networks, more advanced and intelligent network security is essential for 5G networks. To detect unknown and evolving 5G network intrusions, this paper presents an artificial intelligence (AI)-based network threat detection system to perform data labeling, data filtering, data preprocessing, and data learning for 5G network flow and security event data. The performance evaluations are first conducted on two well-known datasets-NSL-KDD and CICIDS 2017; then, the practical testing of proposed system is performed in 5G industrial IoT environments. To demonstrate detection against network threats in real 5G environments, this study utilizes the 5G model factory, which is downscaled to a real smart factory that comprises a number of 5G industrial IoT-based devices.

The aim of this paper is to examine noteworthy cyberattacks that have taken place against ICS and SCADA systems and to analyse them. This paper also proposes a new classification scheme based on the severity of the attack. Since the information revolution, computers and associated technologies have impacted almost all aspects of daily life, and this is especially true of the industrial sector where one of the leading trends is that of automation. This widespread proliferation of computers and computer networks has also made it easier for malicious actors to gain access to these systems and networks and carry out harmful activities.