Network intrusion, such as denial of service, probing attacks, and phishing, comprises some of the complex threats that have put the online community at risk. The increase in the number of these attacks has given rise to a serious interest in the research community to curb the menace. One of the research efforts is to have an intrusion detection mechanism in place. Batch learning and data streaming are approaches used for processing the huge amount of data required for proper intrusion detection. Batch learning, despite its advantages, has been faulted for poor scalability due to the constant re-training of new training instances. Hence, this paper seeks to conduct a comparative study using selected batch learning and data streaming algorithms. The batch learning and data streaming algorithms considered are J48, projective adaptive resonance theory (PART), Hoeffding tree (HT) and OzaBagAdwin (OBA). Furthermore, binary and multiclass classification problems are considered for the tested algorithms. Experimental results show that data streaming algorithms achieved considerably higher performance in binary classification problems when compared with batch learning algorithms. Specifically, binary classification produced J48 (94.73), PART (92.83), HT (98.38), and OBA (99.67), and multiclass classification produced J48 (87.66), PART (87.05), HT (71.98), OBA (82.80) based on accuracy. Hence, the use of data streaming algorithms to solve the scalability issue and allow real-time detection of network intrusion is highly recommended. Full article

Effective access control techniques are in demand, as electronically assisted healthcare services require the patient’s sensitive health records. In emergency situations, where the patient’s well-being is jeopardized, different healthcare actors associated with emergency cases should be granted permission to access Electronic Health Records (EHRs) of patients. The research objective of our study is to develop machine learning techniques based on patients’ time sequential health metrics and integrate them with an Attribute Based Access Control (ABAC) mechanism. We propose an ABAC mechanism that can yield access to sensitive EHRs systems by applying prognostic context handlers where contextual information, is used to identify emergency conditions and permit access to medical records. Specifically, we use patients’ recent health history to predict the health metrics for the next two hours by leveraging Long Short Term Memory (LSTM) Neural Networks (NNs). These predicted health metrics values are evaluated by our personalized fuzzy context handlers, to predict the criticality of patients’ status. The developed access control method provides secure access for emergency clinicians to sensitive information and simultaneously safeguards the patient’s well-being. Integrating this predictive mechanism with personalized context handlers proved to be a robust tool to enhance the performance of the access control mechanism to modern EHRs System. Full article

The growth of the Internet of Things (IoT) in various mission-critical applications generates service heterogeneity with different priority labels. A set of virtual network function (VNF) orders represents service function chaining (SFC) for a particular service to robustly execute in a network function virtualization (NFV)-enabled environment. In IoT networks, the configuration of adaptive SFC has emerged to ensure optimality and elasticity of resource expenditure. In this paper, priority-aware resource management for adaptive SFC is provided by modeling the configuration of real-time IoT service requests. The problem models of the primary features that impact the optimization of configuration times and resource utilization are studied. The proposed approaches query the promising embedded deep reinforcement learning engine in the management layer (e.g., orchestrator) to observe the state features of VNFs, apply the action on instantiating and modifying new/created VNFs, and evaluate the average transmission delays for end-to-end IoT services. In the embedded SFC procedures, the agent formulates the function approximator for scoring the existing chain performance metrics. The testbed simulation was conducted in SDN/NFV topologies and captured the average of rewards, delays, delivery ratio, and throughput as −48.6666, 10.9766 ms, 99.9221%, and 615.8441 Mbps, which outperformed other reference approaches, following parameter configuration in this environment. Full article

To solve the problem of the sensorless control method of a permanent magnet synchronous motor, based on the study of a mathematical model for a permanent magnet synchronous motor and model adaptation theory, a reference model equation and adjustable model equation are derived according to the stator current equation. The correctness of the selected linear compensator matrix is strictly proved. Then, Popov’s super-stability theory is used to derive the speed adaptive law and prove its asymptotic stability. Based on the voltage closed-loop feedback MTPA weak magnetic control strategy, a simulation model of a MRAS control system based on stator current is built and combined with the principle of MRAS. Aiming to investigate the problem that the PI adaptive law in the traditional MRAS algorithm is not robust, super-twisting sliding mode control is introduced to replace the PI adaptive law. The observer based on STSM−MRAS is designed. The simulation model of the MRAS control system based on the super-twisting sliding mode is established. Under certain working conditions, the STSM−MRAS algorithm and the traditional MRAS algorithm are simulated and compared. The results show that the STSM−MRAS algorithm can improve the steady-state performance and robustness of a sensorless control system. Full article

In this paper a novel low-power approximate floating-point multiplier is presented. Since the mantissa computation is responsible for the largest part of the power consumption, we apply a novel approximation technique to mantissa multiplication, based on static segmentation. In our approach, the inputs of the mantissa multiplier are properly segmented so that a small inner multiplier can be used to calculate the output, with beneficial impact on power and area. To further improve performance, we introduce a novel segmentation-and-truncation approach which allows us to eliminate the shifter normally present at the output of the segmented multiplier. In addition, a simple compensation term for reducing approximation error is employed. The accuracy of the circuit can be tailored at the design time, by acting on a single parameter. The proposed approximate floating-point multiplier is compared with the state-of-the-art, showing good performance in terms of both precision and hardware saving. For single-precision floating-point format, the obtained NMED is in the range 10⁻⁵–7 × 10⁻⁷, while MRED is in the range 3 × 10⁻³–1.7 × 10⁻⁴. Synthesis results in 28 nm CMOS show area and power saving of up to 82% and 85%, respectively, compared to the exact floating-point multiplier. Image processing applications confirm the expectations, with results very close to the exact case. Full article

The comprehensive and safe application of unmanned surface vessels is certainly one of the biggest challenges currently facing maritime science. Such vessels can be implemented within a wide range of autonomy levels that goes from remote-controlled vessels to fully autonomous vessels in which intelligent vessel systems completely perform all necessary operations. One of the ways to achieve autonomous vessel systems is to implement multi-agent systems that take over all functions performed by the crew in classical manned crew vessels. A vessel is a complex system that conceptually can be considered as a set of interconnected subsystems. Theoretically, the functions of these subsystems could be performed using appropriate multi-agent systems. In this paper we analyzed 24 relevant papers. A review of the current state of implementation of multi-agent systems for performing the functions of unmanned surface vessels is presented. Full article

Bearings are an important component in mechanical equipment, and their health detection and fault diagnosis are of great significance. In order to meet the speed and recognition accuracy requirements of bearing fault diagnosis, this paper uses the lightweight MobileNetV2 network combined with fast spectral kurtosis to diagnose bearing faults. On the basis of the original MobileNetV2 network, a progressive classifier is used to compress the feature information layer by layer with the network structure to achieve high-precision and rapid identification and classification. A cross-local connection structure is added to the network to increase the extracted feature information to improve accuracy. At the same time, the original fault signal of the bearing is a one-dimensional vibration signal, and the signal contains a large number of non-Gaussian noise and accidental shock defects. In order to extract fault features more efficiently, this paper uses the fast spectral kurtosis algorithm to process the signal, extract the center frequency of the original signal, and calculate the spectral kurtosis value. The kurtosis map generated by signal preprocessing is used as the input of the MobileNetV2 network for fault classification. In order to verify the effectiveness and generality of the proposed method, this paper uses the XJTU-SY bearing fault dataset and the CWRU bearing dataset to conduct experiments. Through data preprocessing methods, such as data expansion for different fault types in the original dataset, input data that meet the experimental requirements are generated and fault diagnosis experiments are carried out. At the same time, through the comparison with other typical classification networks, the paper proves that the proposed method has significant advantages in terms of accuracy, model size, training speed, etc., and, finally, proves the effectiveness and generality of the proposed network model in the field of fault diagnosis. Full article

Companies aim to offer customized treatments, intelligent care, and a seamless experience to their customers. Interactions between a company and its customers largely depend on the company’s ability to learn, understand, and predict customer behaviors. Customer behavior prediction is a pivotal factor in improving a company’s quality of services and thus its growth. Different machine learning techniques have been applied to gather customer data to predict behavioral patterns. Traditional methods are unable to discover hidden patterns in ideal situations and need to be improved to produce more accurate predictions. This work proposes a novel hybrid model comprised of two modules: a novel clustering module on the basis of an optimized fuzzy deep belief network and a customer behavior prediction module on the basis of a deep recurrent neural network. Customers’ previous purchasing characteristics and portfolio details were analyzed by applying learning parameters. In this paper, the deep learning techniques were optimized by applying the butterfly optimization method, which minimizes the maximum error classification problem. The performance of the system was evaluated using experimental analysis. The proposed approach was compared to other single and hybrid-model-based approaches and attained the highest performance in the respective metrics. Full article

This paper proposes a reinforcement learning (RL) algorithm for the security problem of state estimation of cyber-physical system (CPS) under denial-of-service (DoS) attacks. The security of CPS will inevitably decline when faced with malicious cyber attacks. In order to analyze the impact of cyber attacks on CPS performance, a Kalman filter, as an adaptive state estimation technology, is combined with an RL method to evaluate the issue of system security, where estimation performance is adopted as an evaluation criterion. Then, the transition of estimation error covariance under a DoS attack is described as a Markov decision process, and the RL algorithm could be applied to resolve the optimal countermeasures. Meanwhile, the interactive combat between defender and attacker could be regarded as a two-player zero-sum game, where the Nash equilibrium policy exists but needs to be solved. Considering the energy constraints, the action selection of both sides will be restricted by setting certain cost functions. The proposed RL approach is designed from three different perspectives, including the defender, the attacker and the interactive game of two opposite sides. In addition, the framework of Q-learning and state–action–reward–state–action (SARSA) methods are investigated separately in this paper to analyze the influence of different RL algorithms. The results show that both algorithms obtain the corresponding optimal policy and the Nash equilibrium policy of the zero-sum interactive game. Through comparative analysis of two algorithms, it is verified that the differences between Q-Learning and SARSA could be applied effectively into the secure state estimation in CPS. Full article

This paper is concerned with the stability and stabilization problem of a Takagi-Sugeno fuzzy (TSF) system. Using a non-quadratic function (well-known integral Lyapunov fuzzy candidate (ILF)) and some lemmas, new sufficient conditions are established as linear matrix inequalities (LMIs), which are solved with a stochastic fractal search (SFS). The main advantage of the technique used is its small conservatives. Motivated by the mean value theorem, a state feedback controller based on a non-quadratic Lyapunov function is designed. Unlike other approaches based on poly-quadratic Lyapunov candidates, stability conditions of the closed loop are obtained in LMI regions. It is important to highlight that the time derivatives of membership functions do not appear in the used line integral Lyapunov function, which is the well-known problem of poly-quadratic Lyapunov functions. A numerical example is given to show the advantages and the utility of the integral Lyapunov fuzzy candidate, which provides a wider feasibility region than other Lyapunov functions. Full article

Prior study suggests that VANET has two types of communications: Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communications. V2V is very important and ensures cooperative communications between vehicles and safety measures. It is also defined as Inter-Vehicle Communication (IVC).The communication is based on clustering the nodes to transmit the data from vehicle to vehicle. The overhead and stability are considered as main challenges that need to be addressed during vehicle intersections. In this paper, a novel self-adaptable Angular based k-medoid Clustering Scheme (SAACS) is proposed to form flexible clusters. The clusters are formed by estimating the road length and transmission ranges to minimize the network delay. And the Cluster Head (CH) is elected from a novel performance metric, ‘cosine-based node uncoupling frequency,’ that finds the best nodes irrespective of their current network statistics. The parametric analysis varies according to the number of vehicular nodes with the transmission range. The experimental results have proven that the proposed technique serves better in comparison to existing approaches such as Cluster Head Lifetime (CHL), Cluster Member Lifetime (CML), Cluster Number (CL), Cluster Overhead (CO), Packet Loss Ratio (PLR) and Average Packet Delay (APD). CHL is enhanced 40% as compare to Real-Time Vehicular Communication (RTVC), Efficient Cluster Head Selection (ECHS) whereas CML is 50% better than RTVC and ECHS. Packet loss ratio and overhead is 45% better in our proposed algorithm than RTVC and ECHS. It is observed from the results that the incorporation of cosine-based node uncoupling frequency has minimized the incongruity between vehicular nodes placed in dense and sparse zones of highways. Full article

Synaptic crosstalk is an important biological phenomenon that widely exists in neural networks. The crosstalk can influence the ability of neurons to control the synaptic weights, thereby causing rich dynamics of neural networks. Based on the crosstalk between synapses, this paper presents a novel two-neuron based memristive Hopfield neural network with a hyperbolic memristor emulating synaptic crosstalk. The dynamics of the neural networks with varying memristive parameters and crosstalk weights are analyzed via the phase portraits, time-domain waveforms, bifurcation diagrams, and basin of attraction. Complex phenomena, especially coexisting dynamics, chaos and transient chaos emerge in the neural network. Finally, the circuit simulation results verify the effectiveness of theoretical analyses and mathematical simulation and further illustrate the feasibility of the two-neuron based memristive Hopfield neural network hardware. Full article

Software-defined networking (SDN) is a network approach achieved by decoupling of the control and data planes. The control plane is logically centralized and the data plane is distributed across the network elements. The real-time network is in need of the incorporation of distributed controllers to maintain distributed state information of the traffic flows. Software-based solutions aid distributed SDN controllers to handle fluctuating network traffic and the controller’s configurations are dynamically programmed in real time. In this study, SDN controllers were programmed with a stateful firewall application to provide firewall functionalities without the support of committed hardware. A stateful firewall filtered traffic based on the complete context of incoming packets; it continuously evaluated the entire context of traffic flows, looking for network entry rather than specific traffic flows. In addition, a flow-based scheduling module was implemented in the distributed controllers to improve network scalability. A network cluster was configured with three distributed controllers and we experimented with three independent network topologies. The performance of the proposed network model was evaluated by measuring and analyzing metrics such as network throughput (kbps), delay (ms) and network overhead (pkt/ms) for various combinations of controllers and topologies. The results of the analysis were determined using the mininet emulator. The findings of the performance evaluation indicate that the distributed SDN controllers performs better than a centralized controller. When comparing distributed SDN with two controllers and distributed SDN with three controllers the overall network throughput is increased by 64%, the delay is decreased by 43% and network overhead is reduced by 39%. Full article

Current quantum computers are still error-prone, with measurement errors being one of the factors limiting the scalability of quantum devices. To reduce their impact, a variety of readout error mitigation methods, mostly relying on classical post-processing, have been developed. However, the application of these methods is complicated by their heterogeneity and a lack of information regarding their functionality, configuration, and integration. To facilitate their use, we provide an overview of existing methods, and evaluate general and method-specific configuration options. Quantum applications comprise many classical pre- and post-processing tasks, including readout error mitigation. Automation can facilitate the execution of these often complex tasks, as their manual execution is time-consuming and error-prone. Workflow technology is a promising candidate for the orchestration of heterogeneous tasks, offering advantages such as reliability, robustness, and monitoring capabilities. In this paper, we present an approach to abstractly model quantum workflows comprising configurable readout error mitigation tasks. Based on the method configuration, these workflows can then be automatically refined into executable workflow models. To validate the feasibility of our approach, we provide a prototypical implementation and demonstrate it in a case study from the quantum humanities domain. Full article

With the growing use of quantum-dot cellular automata (QCA) nanotechnology, digital circuits designed at the Nanoscale have a number of advantages over CMOS devices, including the lower utilization of power, increased processing speed of the circuit, and higher density. There are several flip flop designs proposed in the literature with their realization in the QCA technology. However, the majority of these designs suffer from large cell counts, large area utilization, and latency, which leads to the high cost of the circuits. To address this, this work performed a literature survey of the D flip flop (DFF) designs and complex sequential circuits that can be designed from it. A new design of D flip flop was proposed in this work and to assess the performance of the proposed QCA design, an in-depth comparison with existing designs was performed. Further, sequential circuits such as parallel-in-parallel-out (PIPO) and serial-in-parallel-out (SIPO) shift registers were designed using the flip flop design that was put forward. A comprehensive evaluation of the energy dissipation of all presented fundamental flip-flop circuits and other sequential circuits was also performed using the QCAPro tool, and their energy dissipation maps were also obtained. The suggested designs showed lower power dissipation and were cost-efficient, making them suitable for designing higher-power circuits. Full article