22 research outputs found

    Development and implementation of a method to detect an abnormal behavior of nodes in a group of robots

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    The present paper examines the issues of security in a group of mobile robots in the implementation of malicious attacks aimed at the availability of information.  The main methods and approaches for detecting attacks and mobile robots anomalies were analyzed. The major advantages and disadvantages of existing approaches were identified. The aim is to develop an attack detection method that allows avoiding a creation of either a reference distribution, or a signature database, or rules for a group of mobile robots. The method should detect anomalies within the current conditions with a dynamically changing network structure. The paper presents a method for detecting abnormal behavior of a network node based on analysis of parameters: the residual energy and network load. The behavior of individual robots of the group is analyzed with respect to the deviation from the general behavior using probabilistic methods, which avoids creating a reference distribution for describing the behavior of the node, as well as the creating of a signature database for detecting anomalies. The developed method of detecting abnormal behavior based on the probabilistic evaluation of events. Three types of a network node state were defined, a graph of node transitions to each state was constructed, and parameters that affect these transitions were determined. The developed method demonstrates a high detection rate of denial of service attacks and distributed denial of service attacks when the number of malicious nodes is not greater than or slightly greater than the amount trusted nodes. It also provides detection of the Sybil attack. An experimental study was carried out. It includes the development of a model to simulate a group of mobile robots, in particular a robot network. Scenarios of attacks were developed, implemented for a group of mobile robots. It allows evaluating the effectiveness of this method of anomalous behavior detection. To determine the effectiveness of the developed method, the following indicators were used: time of detection of attackers and the number of nodes of the attacker that can be detected

    Issues and Challenges for Network Virtualisation

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    In recent years, network virtualisation has been of great interest to researchers, being a relatively new and major paradigm in networking. This has been reflected in the IT industry where many virtualisation solutions are being marketed as revolutionary and purchased by enterprises to exploit these promised performances. Adversely, there are certain drawbacks like security, isolation and others that have conceded the network virtualisation. In this study, an investigation of the different state-of-the-art virtualisation technologies, their issues and challenges are addressed and besides, a guideline for a quintessential Network Virtualisation Environment (NVE) is been proposed. A systematic review was effectuated on selectively picked research papers and technical reports. Moreover a comparative study is performed on different Network Virtualisation technologies which include features like security, isolation, stability, convergence, outlay, scalability, robustness, manageability, resource management, programmability, flexibility, heterogeneity, legacy Support, and ease of deployment. The virtualisation technologies comprise Virtual Private Network (VPN), Virtual Local Area Network (VLAN), Virtual Extensible Local Area Network (VXLAN), Software Defined Networking (SDN) and Network Function Virtualisation (NFV). Conclusively the results exhibited the disparity as to the gaps of creating an ideal network virtualisation model which can be circumvented using these as a benchmark

    Deep Learning Based Secure MIMO Communications with Imperfect CSI for Heterogeneous Networks

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    Perfect channel state information (CSI) is required in most of the classical physical layer security techniques, while it is difficult to obtain the ideal CSI due to the time varying wireless fading channel. Although imperfect CSI has a greatly impact on the security of MIMO communications, deep learning is becoming a promising solution to handle the negative effect of imperfect CSI. In this work, we propose two types of deep learning based secure MIMO detectors for heterogeneous networks, where the macro base station (BS) chooses the null-space eigenvectors to prevent information leakage to the femto BS. Thus, the bit error rate of the associated user is adopted as the metric to valuate the system performance. With the help of deep convolutional neural networks (CNNs), the macro BS obtains the refined version from the imperfect CSI. Simulation results are provided to validate the proposed algorithms. The impacts of system parameters, such as the correlation factor of imperfect CSI, the normalized doppler frequency, the number of antennas are investigated in different setup scenarios. The results show that considerable performance gains can be obtained from the deep learning based detectors compared with the classical maximum likelihood algorithm

    A Low Energy Consumption Storage Method for Cloud Video Surveillance Data Based on SLA Classification

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    Nonlinear Dynamic Chaos Theory Framework for Passenger Demand Forecasting in Smart City

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    Recently chaos theory has emerged as a powerful tool to address forecasting problems of nonlinear time series, since it is able to meet the dynamical and geometrical structures of very complex systems, reaching higher accuracy on the prediction values than the classical approaches. This paper aims at applying the chaos theory principles to different problems, in order to pursue high levels of accuracy on the predicted results. After the verification of the chaotic behavior of the datasets taken into analysis through the largest Lyapunov exponent research, the detection of the suitable embedding dimension and time delay has been carried out, in order to reconstruct the phase space of the underlying dynamical systems. Three different predictive methods have been proposed for different datasets. Finally, the performance comparison with the moving average model, a deep neural network based strategy, and a chaos theory based algorithm recently proposed in literature has been provided

    Survey: An overview of lightweight RFID authentication protocols suitable for the maritime internet of things

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    The maritime sector employs the Internet of Things (IoT) to exploit many of its benefits to maintain a competitive advantage and keep up with the growing demands of the global economy. The maritime IoT (MIoT) not only inherits similar security threats as the general IoT, it also faces cyber threats that do not exist in the traditional IoT due to factors such as the support for long-distance communication and low-bandwidth connectivity. Therefore, the MIoT presents a significant concern for the sustainability and security of the maritime industry, as a successful cyber attack can be detrimental to national security and have a flow-on effect on the global economy. A common component of maritime IoT systems is Radio Frequency Identification (RFID) technology. It has been revealed in previous studies that current RFID authentication protocols are insecure against a number of attacks. This paper provides an overview of vulnerabilities relating to maritime RFID systems and systematically reviews lightweight RFID authentication protocols and their impacts if they were to be used in the maritime sector. Specifically, this paper investigates the capabilities of lightweight RFID authentication protocols that could be used in a maritime environment by evaluating those authentication protocols in terms of the encryption system, authentication method, and resistance to various wireless attacks

    Quantum Key Distribution: Modeling and Simulation through BB84 Protocol Using Python3

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    Autonomous “Things” is becoming the future trend as the role, and responsibility of IoT keep diversifying. Its applicability and deployment need to re-stand technological advancement. The versatile security interaction between IoTs in human-to-machine and machine-to-machine must also endure mathematical and computational cryptographic attack intricacies. Quantum cryptography uses the laws of quantum mechanics to generate a secure key by manipulating light properties for secure end-to-end communication. We present a proof-of-principle via a communication architecture model and implementation to simulate these laws of nature. The model relies on the BB84 quantum key distribution (QKD) protocol with two scenarios, without and with the presence of an eavesdropper via the interception-resend attack model from a theoretical, methodological, and practical perspective. The proposed simulation initiates communication over a quantum channel for polarized photon transmission after a pre-agreed configuration over a Classic Channel with parameters. Simulation implementation results confirm that the presence of an eavesdropper is detectable during key generation due to Heisenberg’s uncertainty and no-cloning principles. An eavesdropper has a 0.5 probability of guessing transmission qubit and 0.25 for the polarization state. During simulation re-iterations, a base-mismatch process discarded about 50 percent of the total initial key bits with an Error threshold of 0.11 percent.</p

    A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency

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    In this paper, we address the problem of asset performance monitoring, with the intention of both detecting any potential reliability problem and predicting any loss of energy consumption e ciency. This is an important concern for many industries and utilities with very intensive capitalization in very long-lasting assets. To overcome this problem, in this paper we propose an approach to combine an Artificial Neural Network (ANN) with Data Mining (DM) tools, specifically with Association Rule (AR) Mining. The combination of these two techniques can now be done using software which can handle large volumes of data (big data), but the process still needs to ensure that the required amount of data will be available during the assets’ life cycle and that its quality is acceptable. The combination of these two techniques in the proposed sequence di ers from previous works found in the literature, giving researchers new options to face the problem. Practical implementation of the proposed approach may lead to novel predictive maintenance models (emerging predictive analytics) that may detect with unprecedented precision any asset’s lack of performance and help manage assets’ O&M accordingly. The approach is illustrated using specific examples where asset performance monitoring is rather complex under normal operational conditions.Ministerio de Economía y Competitividad DPI2015-70842-

    Security Engineering of Patient-Centered Health Care Information Systems in Peer-to-Peer Environments: Systematic Review

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    Background: Patient-centered health care information systems (PHSs) enable patients to take control and become knowledgeable about their own health, preferably in a secure environment. Current and emerging PHSs use either a centralized database, peer-to-peer (P2P) technology, or distributed ledger technology for PHS deployment. The evolving COVID-19 decentralized Bluetooth-based tracing systems are examples of disease-centric P2P PHSs. Although using P2P technology for the provision of PHSs can be flexible, scalable, resilient to a single point of failure, and inexpensive for patients, the use of health information on P2P networks poses major security issues as users must manage information security largely by themselves. Objective: This study aims to identify the inherent security issues for PHS deployment in P2P networks and how they can be overcome. In addition, this study reviews different P2P architectures and proposes a suitable architecture for P2P PHS deployment. Methods: A systematic literature review was conducted following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) reporting guidelines. Thematic analysis was used for data analysis. We searched the following databases: IEEE Digital Library, PubMed, Science Direct, ACM Digital Library, Scopus, and Semantic Scholar. The search was conducted on articles published between 2008 and 2020. The Common Vulnerability Scoring System was used as a guide for rating security issues. Results: Our findings are consolidated into 8 key security issues associated with PHS implementation and deployment on P2P networks and 7 factors promoting them. Moreover, we propose a suitable architecture for P2P PHSs and guidelines for the provision of PHSs while maintaining information security. Conclusions: Despite the clear advantages of P2P PHSs, the absence of centralized controls and inconsistent views of the network on some P2P systems have profound adverse impacts in terms of security. The security issues identified in this study need to be addressed to increase patients\u27 intention to use PHSs on P2P networks by making them safe to use

    FEHCA: A Fault-Tolerant Energy-Efficient Hierarchical Clustering Algorithm for Wireless Sensor Networks

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    Technological advancements have led to increased confidence in the design of large-scale wireless networks that comprise small energy constraint devices. Despite the boost in technological advancements, energy dissipation and fault tolerance are amongst the key deciding factors while designing and deploying wireless sensor networks. This paper proposes a Fault-tolerant Energy-efficient Hierarchical Clustering Algorithm (FEHCA) for wireless sensor networks (WSNs), which demonstrates energy-efficient clustering and fault-tolerant operation of cluster heads (CHs). It treats CHs as no special node but equally prone to faults as normal sensing nodes of the cluster. The proposed scheme addresses some of the limitations of prominent hierarchical clustering algorithms, such as the randomized election of the cluster heads after each round, which results in significant energy dissipation; non-consideration of the residual energy of the sensing nodes while selecting cluster heads, etc. It utilizes the capability of vector quantization to partition the deployed sensors into an optimal number of clusters and ensures that almost the entire area to be monitored is alive for most of the network’s lifetime. This supports better decision-making compared to decisions made on the basis of limited area sensing data after a few rounds of communication. The scheme is implemented for both friendly as well as hostile deployments. The simulation results are encouraging and validate the proposed algorithm.articl
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