Biblio

Wireless-fidelity (Wi-Fi) and Bluetooth are examples of modern wireless communication technologies that employ radio waves as the primary channel for data transmission. but it ought to find alternatives over the limitation and interference in the radio frequency (RF) band. For viable alternatives, visible light communication (VLC) technology comes to play as Light Fidelity (Li-Fi) which uses visible light as a channel for delivering very high-speed communication in a Wi-Fi way. In terms of availability, bandwidth, security and efficiency, Li-Fi is superior than Wi-Fi. In this paper, we present a Li-Fi-based indoor communication system. prototype model has been proposed for single user scenario using visible light portion of electromagnetic spectrum. This system has been designed for audio data communication in between the users in transmitter and receiver sections. LED and photoresistor have been used as optical source and receiver respectively. The electro-acoustic transducer provides the required conversion of electrical-optical signal in both ways. This system might overcome problems like radio-frequency bandwidth scarcity However, its major problem is that it only works when it is pointed directly at the target.

As an emerging technology, IoT is rapidly revolutionizing the global communication network with billions of new devices deployed and connected with each other. Many of these devices collect and transfer a large amount of sensitive or mission critical data, making security a top priority. Compared to traditional Internet, IoT networks often operate in open and harsh environment, and may experience frequent delays, traffic loss and attacks; Meanwhile, IoT devices are often severally constrained in computational power, storage space, network bandwidth, and power supply, which prevent them from deploying traditional security schemes. Authentication is an important security mechanism that can be used to identify devices or users. Due to resource constrains of IoT networks, it is highly desirable for the authentication scheme to be lightweight while also being highly effective. In this paper, we developed and evaluated a hash-chain-based multi-node mutual authentication algorithm. Nodes on a network all share a common secret key and broadcast to other nodes in range. Each node may also add to the hash chain and rebroadcast, which will be used to authenticate all nodes in the network. This algorithm has a linear running time and complexity of O(n), a significant improvement from the O(nˆ2) running time and complexity of the traditional pairwise multi-node mutual authentication.

Internet of Things devices collect and share data (IoT). Internet connections and emerging technologies like IoT offer privacy and security challenges, and this trend is anticipated to develop quickly. Internet of Things intrusions are everywhere. Businesses are investing more to detect these threats. Institutes choose accurate testing and verification procedures. In recent years, IoT utilisation has increasingly risen in healthcare. Where IoT applications gained popular among technologists. IoT devices' energy limits and scalability raise privacy and security problems. Experts struggle to make IoT devices more safe and private. This paper provides a machine-learning-based IDS for IoT network threats (ML-IDS). This study aims to implement ML-supervised IDS for IoT. We're going with a centralised, lightweight IDS. Here, we compare seven popular categorization techniques on three data sets. The decision tree algorithm shows the best intrusion detection results.

With the development of Internet of Things (IoT) technology, the transaction behavior of IoT devices has gradually increased, which also brings the problem of transaction data security and transaction processing efficiency. As one of the research hotspots in the field of data security, blockchain technology has been widely applied in the maintenance of transaction records and the construction of financial payment systems. However, the proportion of microtransactions in the Internet of Things poses challenges to the coupling of blockchain and IoT devices. This paper proposes a three-party scalable architecture based on “IoT device-edge server-blockchain”. In view of the characteristics of micropayment, the verification mechanism of the execution results of the off-chain transaction is designed, and the bridge node is designed in the off-chain architecture, which ensures the finality of the blockchain to the transaction. According to system evaluation, this scalable architecture improves the processing efficiency of micropayments on blockchain, while ensuring its decentration equal to that of blockchain. Compared with other blockchain-based IoT device payment schemes, our architecture is more excellent in activity.

The design of efficient and secure cryptographic algorithms is a fundamental problem of cryptography. Due to the tight cost and constrained resources devices such as Radio-Frequency IDentification (RFID), wireless sensors, smart cards, health-care devices, lightweight cryptography has received a great deal of attention. Recent research mainly focused on designing optimized cryptographic algorithms which trade offs between security performance, time consuming, energy consumption and cost. In this paper, we present two chaotic stream ciphers based on chaos and we report the results of a comparative performance evaluation study. Compared to other crypto-systems of the literature, we demonstrate that our designed stream ciphers are suitable for practical secure applications of the Internet of Things (IoT) in a constrained resource environment.

This work proposed a unified approach to increase the explainability of the predictions made by Convolution Neural Networks (CNNs) on medical images using currently available Explainable Artificial Intelligent (XAI) techniques. This method in-cooperates multiple techniques such as LISA aka Local Interpretable Model Agnostic Explanations (LIME), integrated gradients, Anchors and Shapley Additive Explanations (SHAP) which is Shapley values-based approach to provide explanations for the predictions provided by Blackbox models. This unified method increases the confidence in the black-box model’s decision to be employed in crucial applications under the supervision of human specialists. In this work, a Chest X-ray (CXR) classification model for identifying Covid-19 patients is trained using transfer learning to illustrate the applicability of XAI techniques and the unified method (LISA) to explain model predictions. To derive predictions, an image-net based Inception V2 model is utilized as the transfer learning model.

In this paper, we propose a new ordered statistics decoding (OSD) for linear block codes, which is referred to as local constraint-based OSD (LC-OSD). Distinguished from the conventional OSD, which chooses the most reliable basis (MRB) for re-encoding, the LC-OSD chooses an extended MRB on which local constraints are naturally imposed. A list of candidate codewords is then generated by performing a serial list Viterbi algorithm (SLVA) over the trellis specified with the local constraints. To terminate early the SLVA for complexity reduction, we present a simple criterion which monitors the ratio of the bound on the likelihood of the unexplored candidate codewords to the sum of the hard-decision vector’s likelihood and the up-to-date optimal candidate’s likelihood. Simulation results show that the LC-OSD can have a much less number of test patterns than that of the conventional OSD but cause negligible performance loss. Comparisons with other complexity-reduced OSDs are also conducted, showing the advantages of the LC-OSD in terms of complexity.

Internet speeds and technological advancements have made individuals increasingly concerned about their personal information being compromised by criminals. There have been a slew of new steganography and data concealment methods suggested in recent years. Steganography is the art of hiding information in plain sight (text, audio, image and video). Unauthorized users now have access to steganographic analysis software, which may be used to retrieve the carrier files valuable secret information. Unfortunately, because to their inefficiency and lack of security, certain steganography techniques are readily detectable by steganalytical detectors. We present a video steganography technique based on the linear block coding concept that is safe and secure. Data is protected using a binary graphic logo but also nine uncompressed video sequences as cover data and a secret message. It's possible to enhance the security by rearranging pixels randomly in both the cover movies and the hidden message. Once the secret message has been encoded using the Hamming algorithm (7, 4) before being embedded, the message is even more secure. The XOR function will be used to add the encoded message's result to a random set of values. Once the message has been sufficiently secured, it may be inserted into the video frames of the cover. In addition, each frame's embedding region is chosen at random so that the steganography scheme's resilience can be improved. In addition, our experiments have shown that the approach has a high embedding efficiency. The video quality of stego movies is quite close to the original, with a PSNR (Pick Signal to Noise Ratio) over 51 dB. Embedding a payload of up to 90 Kbits per frame is also permissible, as long as the quality of the stego video is not noticeably degraded.

In advanced metering infrastructure (AMI), the customers' power consumption data is considered private but needs to be revealed to data-driven attack detection frameworks. In this paper, we present a system for privacy-preserving anomaly-based data falsification attack detection over fully homomorphic encrypted (FHE) data, which enables computations required for the attack detection over encrypted individual customer smart meter's data. Specifically, we propose a homomorphic look-up table (LUT) based FHE approach that supports privacy preserving anomaly detection between the utility, customer, and multiple partied providing security services. In the LUTs, the data pairs of input and output values for each function required by the anomaly detection framework are stored to enable arbitrary arithmetic calculations over FHE. Furthermore, we adopt a private information retrieval (PIR) approach with FHE to enable approximate search with LUTs, which reduces the execution time of the attack detection service while protecting private information. Besides, we show that by adjusting the significant digits of inputs and outputs in our LUT, we can control the detection accuracy and execution time of the attack detection, even while using FHE. Our experiments confirmed that our proposed method is able to detect the injection of false power consumption in the range of 11–17 secs of execution time, depending on detection accuracy.

Low-frequency oscillation (LFO) is a security and stability issue that the power system focuses on, measurement data play an important role in online monitoring and analysis of low-frequency oscillation parameters. Aiming at the problem that the measurement data containing noise affects the accuracy of modal parameter identification, a VMD-SSI modal identification algorithm is proposed, which uses the variational modal decomposition algorithm (VMD) for noise reduction combined with the stochastic subspace algorithm for identification. The VMD algorithm decomposes and reconstructs the initial signal with certain noise, and filters out the noise signal. Then, the optimized signal is input into stochastic subspace identification algorithm(SSI), the modal parameters is obtained. Simulation of a three-machine ninenode system verifies that the VMD-SSI mode identification algorithm has good anti-noise performance.

The discussion about forward error correction (FEC) used for 800G unamplified link (800LR) is ongoing. Aiming at two potential options for FEC bit error ratio (BER) threshold, we propose two FEC schemes, respectively based on channel-polarized (CP) multilevel coding (MLC) and bit interleaved coded modulation (BICM), with the same inner FEC code. The field-programmable gate array (FPGA) verification results indicate that with the same FEC overhead (OH), proposed CP-MLC outperforms BICM scheme with less resource and power consumption.

Modular exponentiation, especially for very large integers of hundreds or thousands of bits, is a commonly used function in popular cryptosystems such as RSA. The complexity of this algorithm is partly driven by the very large word sizes, which require many - often millions - of primitive operations in a CPU implementation, or a large amount of logic when accelerated by an ASIC. FPGAs, with their many embedded DSP resources have started to be used as well. In almost all cases, the calculations have required multiple - occasionally many - clock cycles to complete. Recently, blockchain algorithms have required very low-latency implementations of modular multiplications, motivating new implementations and approaches.In this paper we show nine different high performance modular exponentiation for 1024-bit operands, using a 1024-bit modular multiplication as it’s core. Rather than just showing a number of completed designs, our paper shows the evolution of architectures which lead to different resource mix options. This will allow the reader to apply the examples to different FPGA targets which may have differing ratios of logic, memory, and embedded DSP blocks. In one design, we show a 1024b modular multiplier requiring 83K ALMs and 2372 DSPs, with a delay of 21.21ns.

The correctness of user traffic forwarding paths is an important goal of trusted transmission. Many network security issues are related to it, i.e., denial-of-service attacks, route hijacking, etc. The current path-aware network architecture can effectively overcome this issue through path verification. At present, the main problems of path verification are high communication and high computation overhead. To this aim, this article proposes a lightweight real-time verification mechanism of intradomain forwarding paths in autonomous systems to achieve a path verification architecture with no communication overhead and low computing overhead. The problem situation is that a packet finally reaches the destination, but its forwarding path is inconsistent with the expected path. The expected path refers to the packet forwarding path determined by the interior gateway protocols. If the actual forwarding path is different from the expected one, it is regarded as an incorrect forwarding path. This article focuses on the most typical intradomain routing environment. A few routers are set as the verification routers to block the traffic with incorrect forwarding paths and raise alerts. Experiments prove that this article effectively solves the problem of path verification and the problem of high communication and computing overhead.

Nowadays, the MOBA game is the game type with the most audiences and players around the world. Recently, the League of Legends has become an official sport as an e-sport among 37 events in the 2022 Asia Games held in Hangzhou. As the development in the e-sport, analytical skills are also involved in this field. The topic of this research is to use the machine learning approach to analyze the data of the League of Legends and make a prediction about the result of the game. In this research, the method of machine learning is applied to the dataset which records the first 10 minutes in diamond-ranked games. Several popular machine learning (AdaBoost, GradientBoost, RandomForest, ExtraTree, SVM, Naïve Bayes, KNN, LogisticRegression, and DecisionTree) are applied to test the performance by cross-validation. Then several algorithms that outperform others are selected to make a voting classifier to predict the game result. The accuracy of the voting classifier is 72.68%.

In the recent development of the online cryptocurrency mining platform, Coinhive, numerous websites have employed “Cryptojacking.” They may need the unauthorized use of CPU resources to mine cryptocurrency and replace advertising income. Web cryptojacking technologies are the most recent attack in information security. Security teams have suggested blocking Cryptojacking scripts by using a blacklist as a strategy. However, the updating procedure of the static blacklist has not been able to promptly safeguard consumers because of the sharp rise in “Cryptojacking kidnapping”. Therefore, we propose a Cryptojacking identification technique based on analyzing the user's computer resources to combat the assault technology known as “Cryptojacking kidnapping.” Machine learning techniques are used to monitor changes in computer resources such as CPU changes. The experiment results indicate that this method is more accurate than the blacklist system and, in contrast to the blacklist system, manually updates the blacklist regularly. The misuse of online Cryptojacking programs and the unlawful hijacking of users' machines for Cryptojacking are becoming worse. In the future, information security undoubtedly addresses the issue of how to prevent Cryptojacking and abduction. The result of this study helps to save individuals from unintentionally becoming miners.

This study develops a framework for personalized care to tackle heart disease risk using an at-home system. The machine learning models used to predict heart disease are Logistic Regression, K - Nearest Neighbor, Support Vector Machine, Naive Bayes, Decision Tree, Random Forest and XG Boost. Timely and efficient detection of heart disease plays an important role in health care. It is essential to detect cardiovascular disease (CVD) at the earliest, consult a specialist doctor before the severity of the disease and start medication. The performance of the proposed model was assessed using the Cleveland Heart Disease dataset from the UCI Machine Learning Repository. Compared to all machine learning algorithms, the Random Forest algorithm shows a better performance accuracy score of 90.16%. The best model may evaluate patient fitness rather than routine hospital visits. The proposed work will reduce the burden on hospitals and help hospitals reach only critical patients.

Contrary to previous opinion, ‘frost shattering’ is not the only major contributor to rock weathering at mid latitudes and high elevations, more specifically along edges of bedrock escarpments. Lightning is also a significant contributor to land surface denudation. We can show this as lightning strikes on outcrops can dramatically alter the magnetic signature of rocks and is one of the main sources of noise in paleomagnetic studies. Igneous rocks in the highlands of Lesotho, southern Africa (\textgreater 3000 m elevation) provide an ideal study location, as flow lavas remain as prominent ridges that are relatively resistant to weathering. It is well known that lightning strikes can cause large remanent magnetization in rocks with little resultant variation in susceptibility. At two adjoining peaks in the Lesotho highlands, mapped freshly fractured rock correlates with areas of high magnetic intensity (remanent component), but little variation in susceptibility (related to the induced field), and is therefore a clear indicator of lightning damage. The majority of these mapped strike sites occur at the edges of topographic highs. Variations in magnetic intensity are correlated with the much lower resolution national lightning strikes dataset. These data confirm that high elevation edges of peak scarps are the focus of previous lightning strikes. This method of magnetic surveying compared with lightning strike data is a new method of confirming the locations of lightning strikes, and reduces the need for intensive paleomagnetic studies of the area to confirm remanence.

In this paper, we use selective laser melting (SLM) technology to fabricate AlNiCo magnetic materials, and the effects of laser processing parameters on the density and mechanical properties of AlNiCo magnetic materials were studied. We tested the magnetic properties of the heat-treated magnets. The results show that both laser power and scanning speed affect the forming. In this paper, the influence of laser power on the density of samples far exceeds the scanning speed. Through the experiment, we obtained the optimal range of process parameters: laser power (150 170W) and laser scanning speed (800 1000mm/s). Although the samples formed within this range have higher density, there are still many cracks, further research work should be done.

Detection of malware and security attacks is a complex process that can vary in its details and analysis activities. As part of the detection process, malware scanners try to categorize a malware once it is detected under one of the known malware categories (e.g. worms, spywares, viruses, etc.). However, many studies and researches indicate problems with scanners categorizing or identifying a particular malware under more than one malware category. This paper, and several others, show that machine learning can be used for malware detection especially with ensemble base prediction methods. In this paper, we evaluated several custom-built ensemble models. We focused on multi-label malware classification as individual or classical classifiers showed low accuracy in such territory.This paper showed that recent machine models such as ensemble and deep learning can be used for malware detection with better performance in comparison with classical models. This is very critical in such a dynamic and yet important detection systems where challenges such as the detection of unknown or zero-day malware will continue to exist and evolve.

Requirement Elicitation is a key phase in software development. The fundamental goal of security requirement elicitation is to gather appropriate security needs and policies from stakeholders or organizations. The majority of systems fail due to incorrect elicitation procedures, affecting development time and cost. Security requirement elicitation is a major activity of requirement engineering that requires the attention of developers and other stakeholders. To produce quality requirements during software development, the authors suggested a methodology for effective requirement elicitation. Many challenges surround requirement engineering. These concerns can be connected to scope, preconceptions in requirements, etc. Other difficulties include user confusion over technological specifics, leading to confusing system aims. They also don't realize that the requirements are dynamic and prone to change. To protect the privacy of medical images, the proposed image cryptosystem uses a CCM-generated chaotic key series to confuse and diffuse them. A hexadecimal pre-processing technique is used to increase the security of color images utilising a hyper chaos-based image cryptosystem. Finally, a double-layered security system for biometric photos is built employing chaos and DNA cryptography.

The growing amount of data and advances in data science have created a need for a new kind of cloud platform that provides users with flexibility, strong security, and the ability to couple with supercomputers and edge devices through high-performance networks. We have built such a nation-wide cloud platform, called "mdx" to meet this need. The mdx platform's virtualization service, jointly operated by 9 national universities and 2 national research institutes in Japan, launched in 2021, and more features are in development. Currently mdx is used by researchers in a wide variety of domains, including materials informatics, geo-spatial information science, life science, astronomical science, economics, social science, and computer science. This paper provides an overview of the mdx platform, details the motivation for its development, reports its current status, and outlines its future plans.

The botnet-based network assault are one of the most serious security threats overlay the Internet this day. Although significant progress has been made in this region of research in recent years, it is still an ongoing and challenging topic to virtually direction the threat of botnets due to their continuous evolution, increasing complexity and stealth, and the difficulties in detection and defense caused by the limitations of network and system architectures. In this paper, we propose a novel and efficient botnet detection method, and the results of the detection method are validated with the CTU-13 dataset.

International regulations specified in WP.29 and international standards specified in ISO/SAE 21434 require security operations such as cyberattack detection and incident responses to protect vehicles from cyberattacks. To meet these requirements, many vehicle manufacturers are planning to install Intrusion Detection Systems (IDSs) in the Controller Area Network (CAN), which is a primary component of in-vehicle networks, in the coming years. Besides, many vehicle manufacturers and information security companies are developing technologies to identify attack paths related to IDS alerts to respond to cyberattacks appropriately and quickly. To develop the IDSs and the technologies to identify attack paths, it is essential to grasp normal communications performed on in-vehicle networks. Thus, our study aims to develop a technology that can easily grasp normal communications performed on in-vehicle networks. In this paper, we propose the first message source identification method that easily identifies CAN-IDs used by each Electronic Control Unit (ECU) connected to the CAN for message transmissions. We realize the proposed method by utilizing diagnostic communications and an IDS installed in the CAN (CAN-IDS). We evaluate the proposed method using an ECU installed in an actual vehicle and four kinds of simulated CAN-IDSs based on typical existing intrusion detection methods for the CAN. The evaluation results show that the proposed method can identify the CAN-ID used by the ECU for CAN message transmissions if a suitable simulated CAN-IDS for the proposed method is connected to the vehicle.

A new type of time modulated metamaterial (MTM) antenna array transmitter capable of realizing 2D directional modulation (DM) for physical layer (PHY) security is presented in this work. The proposed 2D DM MTM antenna array is formed by a time modulated corporate feed network loaded with composite right/left-handed (CRLH) leaky wave antennas (LWAs). By properly designing the on-off states of the switch for each antenna feeding branch as well as harnessing the frequency scanning characteristics of CRLH L WAs, 2D DM can be realized to form a PHY secured transmission link in the 2D space. Experimental results demonstrate the bit-error-rate (BER) is low only at a specific 2D angle for the orthogonal frequency-division multiplexing (OFDM) wireless data links.

The development of industrial automation tools and the integration of industrial and corporate networks in order to improve the quality of production management have led to an increase in the risks of successful cyberattacks and, as a result, to the necessity to solve the problems of practical information security of industrial control systems (ICS). Detection of cyberattacks of both known and unknown types is could be implemented as anomaly detection in dynamic information processes recorded during the operation of ICS. Anomaly detection methods do not require preliminary analysis and labeling of the training sample. In the context of detecting attacks on ICS, cluster analysis is used as one of the methods that implement anomaly detection. The application of hierarchical cluster analysis for clustering data of ICS information processes exposed to various cyberattacks is studied, the problem of choosing the level of the cluster hierarchy corresponding to the minimum set of clusters aggregating separately normal and abnormal data is solved. It is shown that the Ward method of hierarchical cluster division produces the best division into clusters. The next stage of the study involves solving the problem of classifying the formed minimum set of clusters, that is, determining which cluster is normal and which cluster is abnormal.