Biblio

In this paper, we present a flexible Variable Precision Computation Array (VPCA) component for different accelerators, which leverages a sparsification scheme for activations and a low bits serial-parallel combination computation unit for improving the efficiency and resiliency of accelerators. The VPCA can dynamically decompose the width of activation/weights (from 32bit to 3bit in different accelerators) into 2-bits serial computation units while the 2bits computing units can be combined in parallel computing for high throughput. We propose an on-the-fly compressing and calculating strategy SLE-CLC (single lane encoding, cross lane calculation), which could further improve performance of 2-bit parallel computing. The experiments results on image classification datasets show VPCA can outperforms DaDianNao, Stripes, Loom-2bit by 4.67×, 2.42×, 1.52× without other overhead on convolution layers.

Steganography is the concept to conceal information and the data by embedding it as secret data into various digital medium in order to achieve higher security. To achieve this, many steganographic algorithms are already proposed. The ability of human eyes as well as invisibility remain the most important and prominent factor for the security and protection. The most commonly used security measure of data hiding within imagesYet it is ineffective against Steganalysis and lacks proper verifications. Thus the proposed system of Image Steganography using PVD (Pixel Value Differentiating) proves to be a better choice. It compresses and embeds data in images at the pixel value difference calculated between two consecutive pixels. To increase the security, another technique called Edge Detection is used along with PVD to embed data at the edges. Edge Detection techniques like Canny algorithm are used to find the edges in an image horizontally as well as vertically. The edge pixels in an image can be used to handle more bits of messages, because more pixel value shifts can be handled by the image edge area.

Edge computing (EC) is a new developing computing technology where data are collected, and analysed nearer to the edge or sources of the data. Cloud to the edge, intelligent applications and analytics are part of the IoT applications and technology. Edge computing technology aims to bring cloud computing features near to edge devices. For time-sensitive applications in cloud computing, architecture massive volume of data is generated at the edge and stored and analysed in the cloud. Cloud infrastructure is a composition of data centres and large-scale networks, which provides reliable services to users. Traditional cloud computing is inefficient due to delay in response, network delay and congestion as simultaneous transactions to the cloud, which is a centralised system. This paper presents a literature review on cloud-based edge computing technologies for delay-sensitive applications and suggests a conceptual model of edge computing architecture. Further, the paper also presents the implementation of QoS support edge computing paradigm in Python for further research to improve the latency and throughput for time-sensitive applications.

Vehicular ad-hoc networks (VANETs) are one of the most stochastic networks in terms of node movement patterns. Due to the high speed of vehicles, nodes form temporary clusters and shift between clusters rapidly, which limits the usable computational complexity for quality of service (QoS) and security enhancements. Hence, VANETs are one of the most insecure networks and are prone to various attacks like Masquerading, Distributed Denial of Service (DDoS) etc. Various algorithms have been proposed to safeguard VANETs against these attacks, which vary concerning security and QoS performance. These algorithms include linear rule-checking models, software-defined network (SDN) rules, blockchain-based models, etc. Due to such a wide variety of model availability, it becomes difficult for VANET designers to select the most optimum security framework for the network deployment. To reduce the complexity of this selection, the paper reviews statistically investigate a wide variety of modern VANET-based security models. These models are compared in terms of security, computational complexity, application and cost of deployment, etc. which will assist network designers to select the most optimum models for their application. Moreover, the paper also recommends various improvements that can be applied to the reviewed models, to further optimize their performance.

We propose a voting ensemble of models trained by using block-wise transformed images with secret keys against black-box attacks. Although key-based adversarial defenses were effective against gradient-based (white-box) attacks, they cannot defend against gradient-free (black-box) attacks without requiring any secret keys. In the proposed ensemble, a number of models are trained by using images transformed with different keys and block sizes, and then a voting ensemble is applied to the models. Experimental results show that the proposed defense achieves a clean accuracy of 95.56 % and an attack success rate of less than 9 % under attacks with a noise distance of 8/255 on the CIFAR-10 dataset.

Physical layer security has gained importance with the widespread use of wireless communication systems. Multiantenna systems and multi-point transmission techniques in 5G and beyond are promising techniques not only for enhancing data rates, but also physical layer security. Coordinated multipoint transmission is used for enhancing the service quality and decreasing inter-cell interference especially for cell-edge users. In this study, analysis of physical layer security enhancement via multi-antenna technologies and coordinated multi-point for 5G and beyond networks is provided. The proposed scheme is evaluated on calculations from real-life mobile network topologies. As a figure of performance, the secure and successful detection probability is computed with varying antenna array size, number of coordinated transmission points, and different service requirements.

Brushless synchronous generators (BSG) are typically used to produce an island network whose voltage is close to the nominal voltage of the generator. Generators are often used also in test-field applications where also zero output voltage is needed. The exciter construction and magnetic remanence may lead to a situation where the non-loaded generator terminal voltage cannot be controlled close to zero but a significant voltage is always generated because the exciter remanence. A new brushless synchronous generator excitation and de-excitation converter topology for test applications is proposed. The purpose is to achieve full voltage control from zero to nominal level without modifications to the generator. Insulated-gate bipolar transistor (IGBT) and Field-Programmable Gate Array (FPGA) technology are used to achieve the required fast and accurate control. In the work, simulation models were first derived to characterize the control performance. The proposed converter topology was then verified with the simulation model and tested empirically with a 400 kVA brushless synchronous generator. The results indicate that the exciter remanence and self-excitation can be controlled through the exciter stationary field winding when the proposed converter topology controls the field winding current. Consequently, in highly dynamical situations, the system is unaffected by mechanical stresses and wear in the generator.

It is important to ensure source location privacy (SLP) protection in safety-critical monitoring applications. Also, to achieve effective long-term monitoring, it is essential to design SLP protocols with high energy efficiency and energy balancing. Therefore, this study proposes a new phantom with angle (PwA) protocol. The PwA protocol employs dynamic routing paths which are designed to achieve SLP protection with energy efficiency and energy balancing. Analysis results reveal that the PwA protocol exhibits superior performance features to outperform existing protocols by achieving high levels of SLP protection for time petime periods. The results confirm that the PwA protocol is practical in long-term monitoring systems.riods. The results confirm that the PwA protocol is practical in long-term monitoring systems.

Wireless Sensor Networks (WSNs) and their implementations have been the subject of numerous studies over the last two decades. WSN gathers, processes, and distributes wireless data to the database storage center. This study aims to explain the four main components of sensor nodes and the mechanism of WSN's. WSNs have 5 available types that will be discussed and explained in this paper. In addition to that, shortest path routing will be thoroughly analyzed. In “The Protocol”. Reconfigurable logic applications have grown in number and complexity. Shortest path routing is a method of finding paths through a network with the least distance or other cost metric. The efficiency of the shortest path protocol mechanism and the reliability of encryption are both present which adds security and accuracy of location privacy and message delivery. There are different forms of key management, such as symmetric and asymmetric encryption, each with its own set of processing techniques. The use of encryption technique to secure sensor nodes is addressed, as well as how we overcame the problem with the aid of advanced techniques. Our major findings are that adding more security doesn't cost much and by cost we mean energy consumption, throughput and latency.

Industrial Control System (ICS) communication transmits monitoring and control data between industrial processes and the control station. ICS systems cover various domains of critical infrastructure such as the power plants, water and gas distribution, or aerospace traffic control. Security of ICS systems is usually implemented on the perimeter of the network using ICS enabled firewalls or Intrusion Detection Systems (IDSs). These techniques are helpful against external attacks, however, they are not able to effectively detect internal threats originating from a compromised device with malicious software. In order to mitigate or eliminate internal threats against the ICS system, we need to monitor ICS traffic and detect suspicious data transmissions that differ from common operational communication. In our research, we obtain ICS monitoring data using standardized IPFIX flows extended with meta data extracted from ICS protocol headers. Unlike other anomaly detection approaches, we focus on modelling the semantics of ICS communication obtained from the IPFIX flows that describes typical conversational patterns. This paper presents a technique for modelling ICS conversations using frequency prefix trees and Deterministic Probabilistic Automata (DPA). As demonstrated on the attack scenarios, these models are efficient to detect common cyber attacks like the command injection, packet manipulation, network scanning, or lost connection. An important advantage of our approach is that the proposed technique can be easily integrated into common security information and event management (SIEM) systems with Netflow/IPFIX support. Our experiments are performed on IEC 60870-5-104 (aka IEC 104) control communication that is widely used for the substation control in smart grids.

This research proposal defines the aim to explore the perceptions and experiences of employees through a global pandemic. In an exploratory qualitative study, utilising thematic analysis, the intention is to take an interpretivist position to examine participants' accounts of working from home during a pandemic, by looking through the psychological contract (Rousseau, 1996a) lens to better understand this phenomenon. This research serves to offer a potential line of enquiry when it comes to the human factors of cyber and information security behaviour and any themes which may overlap with psychological contract breaches (PCB). Previous research has suggested that the psychological contract can impact on employee commitment towards their organisations (Jabeen, Behery and Hossam, 2015), and employees' attitudes towards cyber security affect the frequency in engaging in risky online behaviours, (Hadlington, 2018), this study aims to draw out any themes around these areas through semi-structured interviews with employees in a global law firm.

Nowadays, biometric is a most technique to authenticate /identify human been, because its resistance against theft, loss or forgetfulness. However, biometric is subject to different transmission attacks. Today, the protection of the sensitive biometric information is a big challenge, especially in current wireless networks such as internet of things where the transmitted data is easy to sniffer. For that, this paper proposes an Eigens-Fingerprint-based biometric cryptosystem, where the biometric feature vectors are extracted by the Principal Component Analysis technique with an appropriate quantification. The key-binding principle incorporated with bit-wise and byte-wise correcting code is used for encrypting data and sharing key. Several recognition rates and computation time are used to evaluate the proposed system. The findings show that the proposed cryptosystem achieves a high security without decreasing the accuracy.

The increasing penetration of cyber systems into smart grids has resulted in these grids being more vulnerable to cyber physical attacks. The central challenge of higher order cyber-physical contingency analysis is the exponential blow-up of the attack surface due to a large number of attack vectors. This gives rise to computational challenges in devising efficient attack mitigation strategies. However, a system operator can leverage private information about the underlying network to maintain a strategic advantage over an adversary equipped with superior computational capability and situational awareness. In this work, we examine the following scenario: A malicious entity intrudes the cyber-layer of a power network and trips the transmission lines. The objective of the system operator is to deploy security measures in the cyber-layer to minimize the impact of such attacks. Due to budget constraints, the attacker and the system operator have limits on the maximum number of transmission lines they can attack or defend. We model this adversarial interaction as a resource-constrained attacker-defender game. The computational intractability of solving large security games is well known. However, we exploit the approximately modular behaviour of an impact metric known as the disturbance value to arrive at a linear-time algorithm for computing an optimal defense strategy. We validate the efficacy of the proposed strategy against attackers of various capabilities and provide an algorithm for a real-time implementation.

Centrality measures on graphs have found applications in a large number of domains including modeling the spread of an infection/disease, social network analysis, and transportation networks. As a result, parallel algorithms for computing various centrality metrics on graphs are gaining significant research attention in recent years. In this paper, we study parallel algorithms for the percolation centrality measure which extends the betweenness-centrality measure by incorporating a time dependent state variable with every node. We present parallel algorithms that compute the source-based and source-destination variants of the percolation centrality values of nodes in a network. Our algorithms extend the algorithm of Brandes, introduce optimizations aimed at exploiting the structural properties of graphs, and extend the algorithmic techniques introduced by Sariyuce et al. [26] in the context of centrality computation. Experimental studies of our algorithms on an Intel Xeon(R) Silver 4116 CPU and an Nvidia Tesla V100 GPU on a collection of 12 real-world graphs indicate that our algorithmic techniques offer a significant speedup.

Wireless Sensor Networks (WSNs) have limited energy resource which requires authentic data transmission at a minimum cost. The major challenge is to deploy WSN with limited energy and lifetime of nodes while taking care of secure data communication. The transmission of data from the wireless channels may cause many losses such as fading, noise, bit error rate increases as well as deplete the energy resource from the nodes. To reduce the adverse effects of losses and to save power usage, error control coding (ECC) techniques are widely used and it also brings coding gain. Since WSN have limited energy resource so the selection of ECC is very difficult as both power consumption, as well as BER, has also taken into consideration. This research paper reviews different types of models, their applications, limitations of the sensor networks, and what are different types of future works going to overcome the limitations.

Ternary content addressable memories (TCAMs) widely utilized in network systems to enforce the labeling of packets. For example, they are used for packet forwarding, security, and software-defined networks (SDNs). TCAMs are typically deployed as standalone instruments or as an embedded intellectual property component on application-specific integrated circuits. However, field-programmable gate arrays (FPGAs) do not have TCAM bases. However, FPGAs’ versatility allows them to appeal for SDN deployment, and most FPGA vendors have SDN production kits. Those need to help TCAM features and then simulate TCAMs using the FPGA logic blocks. Several methods to reproduction TCAMs on FPGAs have been introduced in recent years. Some of them use a huge multiple storage blocks within modern FPGAs to incorporate TCAMs. A trouble while remembrances are that soft errors that corrupt stored bits can affect them. Memories may be covered by a parity test to identify errors or by an error correction code, although this involves extra bits in a word frame. This brief considers memory security used to simulate TCAMs. It is shown in particular that by leveraging the assumption its part of potential memory information is true, most single-bit errors can be resolved when memoirs are emulated with a parity bit.

In this paper, the adaptive event-triggered asymptotic tracking control with guaranteed performance for a single link robot manipulator (SLRM) system driven by the brush DC motor is studied. Fuzzy logic systems (FLS) is used to approximate unknown nonlinear functions. By introducing a finite time performance function (FTPF), the tracking error of the system can converge to the compact set of the origin in finite time. In addition, by introducing the smooth function and some positive integral functions, combined with the boundary estimation method and adaptive backstepping technique, the asymptotic tracking control of the system is realized. Meanwhile, event-triggered mechanism is introduced to reduce the network resources of the system. Finally, a practical example is given to prove the effectiveness of the theoretical research.

The mechanism of Fog computing is a distributed infrastructure to provide the computations as same as cloud computing. The fog computing environment provides the storage and processing of data in a distributed manner based on the locality. Fog servicing is better than cloud service for working with smart devices and users in a same locale. However the fog computing will inherit the features of the cloud, it also suffers from many security issues as cloud. One such security issue is authentication with efficient key management between the communicating entities. In this paper, we propose a secured two-way authentication scheme with efficient management of keys between the user mobile device and smart devices under the control of the fog server. We made use of operations such as one-way hash (SHA-512) functions, bitwise XOR, and fuzzy extractor function to make the authentication system to be better. We have verified the proposed scheme for its security effectiveness by using a well-used analysis tool ProVerif. We also proved that it can resist multiple attacks and the security overhead is reduced in terms of computation and communication cost as compared to the existing methods.

This work describes a concept for extending the Network Time Security (NTS) protocol to enable implementation- independent communication between the NTS key establishment (NTS-KE) server and the connected time server(s). It Alls a specification gap left by RFC 8915 for securing the Network Time Protocol (NTP) and enables the centralized and public deployment of an NTS key management server that can support both secured NTP and secured PTP.

The Z machine is a pulsed power generator located at Sandia National Laboratories in Albuquerque, New Mexico. It is capable of producing a \textbackslashtextgreater20 MA current pulse that is directed onto an experimental load. While a diverse array of experiments are conducted on the Z machine, including x-ray production and dynamic materials science experiments, the focus of this presentation are the Magnetic Liner Inertial Fusion (MagLIF) experiments. In these experiments, an axial magnetic field is applied to the load region, where a cylindrical, fuel-filled metal liner is imploded. We explore the effects of this field on the ability to efficiently couple the generator current to the load, and the extent to which this field interrupts the magnetic insulation of the inner-most transmission line. We find that at the present-day applied field values, the effects of the applied field on current coupling are negligible. Estimates of the potential impact on current coupling of the larger applied field values planned for future experiments are also given. Shunted current is measured with B-dot probes and flyer velocimetry techniques. Analytical calculations, 2D particle-in-cell simulations, and experimental measurements will be presented.

Graphs are the dominant formalism for modeling multi-agent systems. The algebraic connectivity of a graph is particularly important because it provides the convergence rates of consensus algorithms that underlie many multi-agent control and optimization techniques. However, sharing the value of algebraic connectivity can inadvertently reveal sensitive information about the topology of a graph, such as connections in social networks. Therefore, in this work we present a method to release a graph’s algebraic connectivity under a graph-theoretic form of differential privacy, called edge differential privacy. Edge differential privacy obfuscates differences among graphs’ edge sets and thus conceals the absence or presence of sensitive connections therein. We provide privacy with bounded Laplace noise, which improves accuracy relative to conventional unbounded noise. The private algebraic connectivity values are analytically shown to provide accurate estimates of consensus convergence rates, as well as accurate bounds on the diameter of a graph and the mean distance between its nodes. Simulation results confirm the utility of private algebraic connectivity in these contexts.

Modern embedded systems need to cater for several needs depending upon the application domain in which they are deployed. For example, mixed-critically needs to be considered for real-time and safety-critical systems and energy for battery-operated systems. At the same time, many of these systems demand for their reliability and security as well. With electronic systems being used for increasingly varying type of applications, novel challenges have emerged. For example, with the use of embedded systems in increasingly complex applications that execute tasks with varying priorities, mixed-criticality systems present unique challenges to designing reliable systems. The large design space involved in implementing cross-layer reliability in heterogeneous systems, particularly for mixed-critical systems, poses new research problems. Further, malicious security attacks on these systems pose additional extraordinary challenges in the system design. In this paper, we cover both the industry and academia perspectives of the challenges posed by these emergent aspects of system design towards designing highperformance, energy-efficient, reliable and/or secure embedded systems. We also provide our views on paths forward.

Conversational agents are the most popular AI technology in IT trends. Domain specific chatbots are now used by almost every industry in order to upgrade their customer service. The Proposed paper shows the modelling and performance of one such conversational agent created using deep learning. The proposed model utilizes NMT (Neural Machine Translation) from the TensorFlow software libraries. A BiRNN (Bidirectional Recurrent Neural Network) is used in order to process input sentences that contain large number of tokens (20-40 words). In order to understand the context of the input sentence attention model is used along with BiRNN. The conversational models usually have one drawback, that is, they sometimes provide irrelevant answer to the input. This happens quite often in conversational chatbots as the chatbot doesn't realize that it is answering without context. This drawback is solved in the proposed system using Deep Reinforcement Learning technique. Deep reinforcement Learning follows a reward system that enables the bot to differentiate between right and wrong answers. Deep Reinforcement Learning techniques allows the chatbot to understand the sentiment of the query and reply accordingly. The Deep Reinforcement Learning algorithms used in the proposed system is Q-Learning, Deep Q Neural Network (DQN) and Distributional Reinforcement Learning with Quantile Regression (QR-DQN). The performance of each algorithm is evaluated and compared in this paper in order to find the best DRL algorithm. The dataset used in the proposed system is Cornell Movie-dialogs corpus and CoQA (A Conversational Question Answering Challenge). CoQA is a large dataset that contains data collected from 8000+ conversations in the form of questions and answers. The main goal of the proposed work is to increase the relevancy of the chatbot responses and to increase the perplexity of the conversational chatbot.

The number of people suffering from mental health issues like depression and anxiety have spiked enormously in recent times. Conversational agents like chatbots have emerged as an effective way for users to express their feelings and anxious thoughts and in turn obtain some empathetic reply that would relieve their anxiety. In our work, we construct two types of empathetic conversational agent models based on sequence-to-sequence modeling with and without attention mechanism. We implement the attention mechanism proposed by Bahdanau et al. for neural machine translation models. We train our model on the benchmark Facebook Empathetic Dialogue dataset and the BLEU scores are computed. Our empathetic conversational agent model incorporating attention mechanism generates better quality empathetic responses and is better in capturing human feelings and emotions in the conversation.

Smart Contracts based cryptocurrencies such as Ethereum are becoming increasingly popular in various domains: but with this increase in popularity comes a significant decrease in throughput and efficiency. Smart Contracts are executed by every miner in the system serially without any parallelism, both inter and intra-Smart Contracts. Such a serial execution inhibits the scalability required to obtain extremely high throughput pertaining to computationally intensive tasks deployed with such Smart Contracts. While significant advancements have been made in the field of concurrency, from GPU architectures that enable massively parallel computation to tools such as MapRe-duce that distributed computing to several nodes connected in the system to achieve higher performance in distributed systems, none are incorporated in blockchain-based distributed computing. The team proposes a novel blockchain that allows public nodes in a permission-independent blockchain to deploy and run Smart Contracts that provide concurrency-related functionalities within the Smart Contract framework. In this paper, the researchers present “ConCurrency,” a blockchain network capable of handling big data-based computations. The technique is based on currently used distributed system paradigms, such as MapReduce, while also allowing for fundamental parallelly computable problems. Concurrency is achieved using a sharding protocol incorporated with consensus mechanisms to ensure high scalability, high reliability, and better efficiency. A detailed methodology and a comprehensive analysis of the proposed blockchain further indicate a significant increase in throughput for parallelly computable tasks, as detailed in this paper.