Biblio

Blockchain is developing rapidly in various domains for its security. Nowadays, one of the most crucial fundamental concerns is internet security. Blockchain is a novel solution to enhance the security of network applications. However, there are no precise frameworks to secure the Internet of Vehicle (IoV) using Blockchain technology. In this paper, a blockchain-based smart internet of vehicle (BSIoV) framework has been proposed due to the cooperative, collaborative, transparent, and secure characteristics of Blockchain. The main contribution of the proposed work is to connect vehicle-related authorities together to fix a secure and transparent vehicle-to-everything (V2X) communication through the peer-to-peer network connection and provide secure services to the intelligent transport systems. A key management strategy has been included to identify a vehicle in this proposed system. The proposed framework can also provide a significant solution for the data security and safety of the connected vehicles in blockchain network.

Ensuring security to IoT devices is important in order to provide privacy and quality of services. Proposing a security solution is considered an important step towards achieving protection, however, proving the soundness of the solution is also crucial. In this paper, we propose a methodology for the performance evaluation of lightweight IoT-based authentication protocols based on execution time. Then, a formal verification test is conducted on a lightweight protocol proposed in the literature. The formal verification test conducted with Scyther tool proofs that the model provides mutual authentication, authorization, integrity, confidentiality, non-repudiation, and accountability. The protocol also was proven to provide protection from various attacks.

Volume, Variety, Velocity, Veracity & Value of data has drawn the attention of many analysts in the last few years. Performance optimization and comparison are the main challenges we face when we talk about the humongous volume of data. Data Analysts use data for activities like forecasting or deep learning and to process these data various tools are available which helps to achieve this task with minimum efforts. Recommendation System plays a crucial role while running any business such as a shopping website or travel agency where the system recommends the user according to their search history, likes, comments, or their past order/booking details. Recommendation System works on various strategies such as Content Filtering, Collaborative Filtering, Neighborhood Methods, or Matrix Factorization methods. For achieving maximum efficiency and accuracy based on the data a specific strategy can be the best case or the worst case for that scenario. Matrix Factorization is the key point of interest in this work. Matrix Factorization strategy includes multiplication of user matrix and item matrix in-order to get a rating matrix that can be recommended to the users. Matrix Multiplication can be achieved by using various algorithms such as Naive Algorithm, Strassen Algorithm, Coppersmith - Winograd (CW) Algorithm. In this work, a new algorithm is proposed to achieve less amount of time and space complexity used in-order for performing matrix multiplication which helps to get the results much faster. By using the Matrix Factorization strategy with various Matrix Multiplication Algorithm we are going to perform a comparative analysis of the same to conclude the proposed algorithm is more efficient.

Expressing Keystroke Dynamics (KD) in form of sound opens new avenues to apply sound analysis techniques on KD. However this mapping is not straight-forward as varied feature space, differences in magnitudes of features and human interpretability of the music bring in complexities. We present a musical interface to KD by mapping keystroke features to music features. Music elements like melody, harmony, rhythm, pitch and tempo are varied with respect to the magnitude of their corresponding keystroke features. A pitch embedding technique makes the music discernible among users. Using the data from 30 users, who typed fixed strings multiple times on a desktop, shows that these auditory signals are distinguishable between users by both standard classifiers (SVM, Random Forests and Naive Bayes) and humans alike.

Physical layer authentication scheme for node authentication using the time-reversal (TR) process and the location-specific key feature of the channel impulse response (CIR) in an underwater time-varying multipath environment is proposed. TR is a well-known signal focusing technique in signal processing; this focusing effect is used by the database maintaining node to authenticate the sensor node by convolving the estimated CIR from a probe signal with its database of CIRs. Maximum time-reversal resonating strength (MTRRS) is calculated to make an authentication decision. This work considers a static underwater acoustic sensor network (UASN) under the “Alice- Bob-Eve” scenario. The performance of the proposed scheme is expressed by the Probability of Detection (PD) and the Probability of False Alarm (PFA).

Browsers collects information for better user experience by allowing JavaScript's and other extensions. Advertiser and other trackers take advantage on this useful information to tracked users across the web from remote devices on the purpose of individual unique identifications the so-called browser fingerprinting. Our work explores the diversity and stability of browser fingerprint by modifying the rule-based algorithm. Browser fingerprint rely only from the gathered data through browser, it is hard to tell that this piece of information still the same when upgrades and or downgrades are happening to any browsers and software's without user consent, which is stability and diversity are the most important usage of generating browser fingerprint. We implemented device fingerprint to identify consenting visitors in our website and evaluate individual devices attributes by calculating entropy of each selected attributes. In this research, it is noted that we emphasize only on data collected through a web browser by employing twenty (20) attributes to identify promising high value information to track how device information evolve and consistent in a period of time, likewise, we manually selected device information for evaluation where we apply the modified rules. Finally, this research is conducted and focused on the devices having the closest configuration and device information to test how devices differ from each other after several days of using on the basis of individual user configurations, this will prove in our study that every device is unique.

This work investigates the vulnerability of Gaussian Mixture Model (GMM) i-vector based speaker verification systems to adversarial attacks, and the transferability of adversarial samples crafted from GMM i-vector based systems to x-vector based systems. In detail, we formulate the GMM i-vector system as a scoring function of enrollment and testing utterance pairs. Then we leverage the fast gradient sign method (FGSM) to optimize testing utterances for adversarial samples generation. These adversarial samples are used to attack both GMM i-vector and x-vector systems. We measure the system vulnerability by the degradation of equal error rate and false acceptance rate. Experiment results show that GMM i-vector systems are seriously vulnerable to adversarial attacks, and the crafted adversarial samples are proved to be transferable and pose threats to neural network speaker embedding based systems (e.g. x-vector systems).

Threat Actor Attribution is the task of identifying an attacker responsible for an attack. This often requires expert analysis and involves a lot of time. There had been attempts to detect a threat actor using machine learning techniques that use information obtained from the analysis of malware samples. These techniques will only be able to identify the attack, and it is trivial to guess the attacker because various attackers may adopt an attack method. A state-of-the-art method performs attribution of threat actors from text reports using Machine Learning and NLP techniques using Threat Intelligence reports. We use the same set of Threat Reports of Advanced Persistent Threats (APT). In this paper, we propose a Deep Learning architecture to attribute Threat actors based on threat reports obtained from various Threat Intelligence sources. Our work uses Neural Networks to perform the task of attribution and show that our method makes the attribution more accurate than other techniques and state-of-the-art methods.

Antisparse coding aims at spreading the information uniformly over representation coefficients and can be expressed as the solution of an ℓ∞-norm regularized problem. In this paper, we propose a new methodology, coined "safe squeezing", accelerating the computation of antisparse representations. The idea consists in identifying saturated entries of the solution via simple tests and compacting their contribution to achieve some form of dimensionality reduction. Numerical experiments show that the proposed approach leads to significant computational gain.

The use of Automatic Dependent Surveillance - Broadcast (ADS-B) for aircraft tracking and flight management operations is widely used today. However, ADS-B is prone to several cyber-security threats due to the lack of data authentication and encryption. Recently, Blockchain has emerged as new paradigm that can provide promising solutions in decentralized systems. Furthermore, software containers and Microservices facilitate the scaling of Blockchain implementations within cloud computing environment. When fused together, these technologies could help improve Air Traffic Control (ATC) processing of ADS-B data. In this paper, a Blockchain implementation within a Microservices framework for ADS-B data verification is proposed. The aim of this work is to enable data feeds coming from third-party receivers to be processed and correlated with that of the ATC ground station receivers. The proposed framework could mitigate ADS- B security issues of message spoofing and anomalous traffic data. and hence minimize the cost of ATC infrastructure by throughout third-party support.

Sybils are users created for carrying out nefarious actions in online social networks (OSNs) and threaten the security of OSNs. Therefore, Sybil detection is an urgent security task, and various detection methods have been proposed. Existing Sybil detection methods are based on the relationship (i.e., graph structure) of users in OSNs. Structure-based methods can be classified into two categories: Random Walk (RW)-based and Belief Propagation (BP)-based. However, although almost all methods have been experimentally evaluated in terms of their performance and robustness to noise, the theoretical understanding of them is insufficient. In this paper, we interpret the Sybil detection problem from the viewpoint of graph signal processing and provide a framework to formulate RW- and BPbased methods as low-pass filtering. This framework enables us to theoretically compare RW- and BP-based methods and explain why BP-based methods perform well for scale-free graphs, unlike RW-based methods. Furthermore, by this framework, we relate RW- and BP-based methods and Graph Neural Networks (GNNs) and discuss the difference among these methods. Finally, we evaluate the validity of this framework through numerical experiments.

Designing an efficient difficulty control algorithm is an essential problem in Proof-of-Work (PoW) based blockchains because the network hash rate is randomly changing. This paper proposes a general difficulty control algorithm and provides insights for difficulty adjustment rules for PoW based blockchains. The proposed algorithm consists a two-layer neural network. It has low memory cost, meanwhile satisfying the fast-updating and low volatility requirements for difficulty adjustment. Real data from Ethereum are used in the simulations to prove that the proposed algorithm has better performance for the control of the block difficulty.

This paper presents a new algorithm for image encryption by extending the Knight's Tour Problem (KTP). The idea behind the proposed algorithm is to generate a Knight Tour (KT) matrix (m,n) and then divide the image according to the size of knight tour matrix into several sub matrices. Finally, apply n-neighborhood addition modulo encryption algorithm according to the solution of KT matrix over each m × n partition of the image. The proposed algorithm provides image encryption without using the cover images. Results obtained from experiments have shown that the proposed algorithm is efficient, simple and does not disclose any information from encrypted image.

The method of extremal filtration implementing the decomposition of signals into alternating components is considered. The history of the method development is described, its mathematical substantiation is given. The method suggests signal decomposition based on the removal of known components locally determined by their extrema. The similarity of the method with empirical modes decomposition in terms of the result is shown, and their comparison is also carried out. The algorithm of extremal filtration has a simple mathematical basis that does not require the calculation of transcendental functions, which provides it with higher performance with comparable results. The advantages and disadvantages of the extremal filtration method are analyzed, and the possibility of its application for solving various technical problems is shown, i.e. the formation of diagnostic features, rapid analysis of signals, spectral and time-frequency analysis, etc. The methods for calculating spectral characteristics are described: by the parameters of the distinguished components, based on the approximation on the extrema by bell-shaped pulses. The method distribution in case of wavelet transform of signals is described. The method allows obtaining rapid evaluation of the frequencies and amplitudes (powers) of the components, which can be used as diagnostic features in solving problems of recognition, diagnosis and monitoring. The possibility of using extremal filtration in real-time systems is shown.

As a TEMPEST information security problem, electromagnetic emissions from the computer displays can be captured, and reconstructed using signal processing techniques. It is necessary to identify the display type to intercept the image of the display. To determine the display type not only significant for attackers but also for protectors to prevent display compromising emanations. This study relates to the identification of the display type using Support Vector Machines (SVM) from electromagnetic emissions emitted from computer displays. After measuring the emissions using receiver measurement system, the signals were processed and training/test data sets were formed and the classification performance of the displays was examined with the SVM. Moreover, solutions for a better classification under real conditions have been proposed. Thus, one of the important step of the display image capture can accomplished by automatically identification the display types. The performance of the proposed method was evaluated in terms of confusion matrix and accuracy, precision, F1-score, recall performance measures.

Attacks by Jamming on wireless communication network can provoke Denial of Services. According to the communication system which is affected, the consequences can be more or less critical. In this paper, we propose to develop an algorithm which could be implemented at the reception stage of a communication terminal in order to detect the presence of jamming signals. The work is performed on Wi-Fi communication signals and demonstrates the necessity to have a specific signal processing at the reception stage to be able to detect the presence of jamming signals.

Network communication data are high-dimensional and spatiotemporal, and their information content is often degraded by common traffic analysis methods. For long-term network attack detection based on network flows, it is important to extract a discriminative, high-dimensional intrinsic representation of such flows. This work focuses on a hybrid deep neural network design using a combination of a convolutional neural network (CNN) and long short-term memory (LSTM) with graph similarity measures to learn high-dimensional representations from the network traffic. In particular, examining a set of network flows, we commence by constructing a temporal communication graph and then computing graph kernel matrices. Having obtained the kernel matrices, for each graph, we use the kernel value between graphs and calculate graph characterization vectors by graph signal processing. This vector can be regarded as a kernel-based similarity embedding vector of the graph that integrates structural similarity information and leverages efficient graph kernel using the graph Laplacian matrix. Our approach exploits graph structures as the additional prior information, the graph Laplacian matrix for feature extraction and hybrid deep learning models for long-term information learning on communication graphs. Experiments on two real-world network attack datasets show that our approach can extract more discriminative representations, leading to an improved accuracy in a supervised classification task. The experimental results show that our method increases the overall accuracy by approximately 10%-15%.

A digital watermarking technique is an alternative method to protect the intellectual property of digital images. This paper presents a hybrid blind watermarking technique formulated by combining RDWT with SVD considering a trade-off between imperceptibility and robustness. Watermark embedding locations are determined using a modified entropy of the host image. Watermark embedding is employed by examining the orthogonal matrix U obtained from the hybrid scheme RDWT-SVD. In the proposed scheme, the watermark image in binary format is scrambled by Arnold chaotic map to provide extra security. Our scheme is tested under different types of signal processing and geometrical attacks. The test results demonstrate that the proposed scheme provides higher robustness and less distortion than other existing schemes in withstanding JPEG2000 compression, cropping, scaling and other noises.

Typical techniques for tracking vital signs require body contact and most of these techniques are intrusive in nature. Body-contact methods might irritate the patient's skin and he/she might feel uncomfortable while sensors are touching his/her body. In this study, we present a new wireless (non-contact) method for monitoring human vital signs (breathing and heartbeat). We have demonstrated for the first time1 that vitals signs can be measured wirelessly through visible light signal reflected from a human subject, also referred to as visible light sensing (VLS). In this method, the breathing and heartbeat rates are measured without any body-contact device, using only a simple photodetector and a light source (e.g., LED). The light signal reflected from human subject is modulated by the physical motions during breathing and heartbeats. Signal processing tools such as filtering and Fourier transform are used to convert these small variations in the received light signal power to vitals data.We implemented the VLS-based non-contact vital signs monitoring system by using an off-the-shelf light source, a photodetector and a signal acquisition and processing unit. We observed more than 94% of accuracy as compared to a contact-based FDA (The Food and Drug Administration) approved devices. Additional evaluations are planned to assess the performance of the developed vitals monitoring system, e.g., different subjects, environments, etc. Non-contact vitals monitoring system can be used in various areas and scenarios such as medical facilities, residential homes, security and human-computer-interaction (HCI) applications.

Visible light communication (VLC) is a burgeoning field in wireless communications as it considers illumination and communication simultaneously. The broadcast nature of VLC makes it necessary to consider the security of underlying transmissions. A physical-layer security (PLS) scheme by introducing jamming LEDs is considered in this paper. The secrecy rate of an indoor VLC system with multiple LEDs, one legitimate receiver, and multiple eavesdroppers is investigated. Three distributions of input signal are assumed, i.e., truncated generalized normal distribution (TGN), uniform distribution, and exponential distribution. The results show that jamming can improve the secrecy performance efficiently. This paper also demonstrates that when the numbers of LEDs transmitting information-bearing signal and jamming signal are equal, the average secrecy rate can be maximized.

Social media has been one of the most efficacious and precise by speakers of public opinion. A strategy which sanctions the utilization and illustration of twitter data to conclude public conviction is discussed in this paper. Sentiments on exclusive entities with diverse strengths and intenseness are stated by public, where these sentiments are strenuously cognate to their personal mood and emotions. To examine the sentiments from natural language texts, addressing various opinions, a lot of methods and lexical resources have been propounded. A path for boosting twitter sentiment classification using various sentiment proportions as meta-level features has been proposed by this article. Analysis of tweets was done on the product iPhone 6.

The analysis of applied tasks and methods of entropy signal processing are carried out in this article. The theoretical comments about the specific schemes of special processors for the determination of probability and correlation activity are given. The perspective of the influence of probabilistic entropy of C. Shannon as cipher signal receivers is reviewed. Examples of entropy-manipulated signals and system characteristics of the proposed special processors are given.

Key management is critical for the successful operation of a cryptographic system in wireless networks. Systems based on asymmetric keys require a dedicated infrastructure for key management and authentication which may not be practical for ad-hoc Underwater Acoustic Networks (UANs). In symmetric-key systems, key distribution is not easy to handle when new nodes join the network. In addition, when a key is compromised all nodes that use the same key are not secure anymore. Hence, it is desirable to have a dynamic way to generate new keys without relying on past keys. Physical Layer Security (PLS) uses correlated channel measurements between two underwater nodes to generate a cryptographic key without exchanging the key itself. In this study, we set up a network of two legitimate nodes and one eavesdropper operating in a shallow area off the coast of Portugal. We propose novel features based on the Channel Impulse Response (CIR) of the established acoustic link that could be used as an initial seed for a crypto-key generation algorithm. Our results show that the two nodes can independently generate 306 quantization bits after exchanging 187 probe signals. Furthermore, the eavesdropper fails to generate the same bits from her/his data even if she/he performs exactly the same signal processing steps of the legitimate nodes.

Smart Grid (SG) is regarded as complex electrical power system due to massive penetration of Renewable Energy Resources and Distribution Generations. The implementation of adjustable speed drives, advance power electronic devices, and electric arc furnaces are incorporated in SG (the transition from conventional power system). Moreover, SG is an advance, automated, controlled, efficient, digital, and intelligent system that ensures pertinent benefits, such as: (a) consumer empowerment, (b) advanced communication infrastructure, (c) user-friendly system, and (d) supports bi-directional power flow. Digital Signal Processing (DSP) is key tool for SG deployment and provides key solutions to a vast array of complex SG challenges. This research provides a comprehensive study on DSP interactions within SG. The prominent challenges posed by conventional grid, such as: (a) monitoring and control, (b) Electric Vehicles infrastructure, (c) cyber data injection attack, (d) Demand Response management and (e) cyber data injection attack are thoroughly investigated in this research.

A recent proposed physical layer encryption technique uses an all-optical setup based on spatial light modulators to split two or more wavelength division multiplexed (WDM) signals in several spectral slices and to shuffle these slices. As a result, eavesdroppers aimed to recover information from a single target signal need to handle all the signals involved in the shuffling process. In this work, computer simulations are used to analyse the case where the shuffled signals propagate through different optical routes. From a security point of view, this is an interesting possibility because it obliges eavesdroppers to tap different optical fibres/ cables. On the other hand, each shuffled signal experiences different physical impairments and the deleterious consequences of these effects must be carefully investigated. Our results indicate that, in a metropolitan area network environment, penalties caused by attenuation and dispersion differences may be easily compensated with digital signal processing algorithms that are presently deployed.