Biblio

The recent 5G networks aim to provide higher speed, lower latency, and greater capacity; therefore, compared to the previous mobile networks, more advanced and intelligent network security is essential for 5G networks. To detect unknown and evolving 5G network intrusions, this paper presents an artificial intelligence (AI)-based network threat detection system to perform data labeling, data filtering, data preprocessing, and data learning for 5G network flow and security event data. The performance evaluations are first conducted on two well-known datasets-NSL-KDD and CICIDS 2017; then, the practical testing of proposed system is performed in 5G industrial IoT environments. To demonstrate detection against network threats in real 5G environments, this study utilizes the 5G model factory, which is downscaled to a real smart factory that comprises a number of 5G industrial IoT-based devices.

Tactical Data Link (TDL) is one of the important elements in Network Centric Warfare (NCW). TDL provides the means for rapid exchange of tactical information between air, ground, sea units and command centers. In military operations, TDL has high demands for resilience, responsiveness, reliability, availability and security. MANET has characteristics that are suitable for the combat environment, namely the ability to self-form and self-healing so that this network may be applied to the TDL system. To produce high performance in MANET adapted for TDL system, an efficient MAC Protocol method is needed. This paper provides a survey of several MAC Protocol methods on a tactical MANET. In this paper also suggests some improvements to the MANET MAC protocol to improve TDL system performance.

Virtual worlds was becoming increasingly popular in a variety of fields, including education, business, space exploration, and video games. Establishing the security of virtual worlds was becoming more critical as they become more widely used. Virtual users were identified using a behavioral biometric system. Improve the system's ability to identify objects by fusing scores from multiple sources. Identification was based on a review of user interactions in virtual environments and a comparison with previous recordings in the database. For behavioral biometric systems like the one described, it appears that score-level biometric fusion was a promising tool for improving system performance. As virtual worlds become more immersive, more people will want to participate in them, and more people will want to be able to interact with each other. Each region of the Meta-verse was given a glimpse of the current state of affairs and the trends to come. As hardware performance and institutional and public interest continue to improve, the Meta-verse's development is hampered by limitations like computational method limits and a lack of realized collaboration between virtual world stakeholders and developers alike. A major goal of the proposed research was to verify the accuracy of the biometric system to enhance the security in virtual world. In this study, the precision of the proposed work was compared to that of previous work.

With the popularization of cloud computing and the deepening of its application, more and more cloud block storage systems have been put into use. The performance optimization of cloud block storage systems has become an important challenge facing today, which is manifested in the reduction of system performance caused by the unbalanced resource load of cloud block storage systems. Accurately predicting the I/O load status of the cloud block storage system can effectively avoid the load imbalance problem. However, the cloud block storage system has the characteristics of frequent random reads and writes, and a large amount of I/O requests, which makes prediction difficult. Therefore, we propose a novel I/O load prediction method for XB-IOPS feature engineering. The feature engineering is designed according to the I/O request pattern, I/O size and I/O interference, and realizes the prediction of the actual load value at a certain moment in the future and the average load value in the continuous time interval in the future. Validated on a real dataset of Alibaba Cloud block storage system, the results show that the XB-IOPS feature engineering prediction model in this paper has better performance in Alibaba Cloud block storage devices where random I/O and small I/O dominate. The prediction performance is better, and the prediction time is shorter than other prediction models.

In today's power market, it's vital to keep electrical energy affordable to the vast majority of people while maintaining the highest degree of dependability. Due to which, the transmission network must operate beyond transfer limitations, generating congestion on transmission lines. These transmission line difficulties can be alleviated with the use of reactive power adjustment based on FACTS devices. Using a fuzzy tuned Static Synchronous Series Compensator [SSSC], this research proposes a novel method for calculating the effective damping oscillation control signals. The performance of the SSSC is compared to that of fuzzy logic-based controllers using PI controllers. According to the simulation results, the SSSC with fuzzy logic control effectively improves power flow under disrupted conditions

Face recognition technology is widely employed in a variety of applications, including public security, criminal identification, multimedia data management, and so on. Because of its importance for practical applications and theoretical issues, the facial recognition system has received a lot of attention. Furthermore, numerous strategies have been offered, each of which has shown to be a significant benefit in the field of facial and pattern recognition systems. Face recognition still faces substantial hurdles in unrestricted situations, despite these advancements. Deep learning techniques for facial recognition are presented in this paper for accurate detection and identification of facial images. The primary goal of facial recognition is to recognize and validate facial features. The database consists of 500 color images of people that have been pre-processed and features extracted using Linear Discriminant Analysis. These features are split into 70 percent for training and 30 percent for testing of decision tree classifiers for the computation of face recognition system performance.

Facial is the most dynamic part of the human body that conveys information about emotions. The level of diversity in facial geometry and facial look makes it possible to detect various human expressions. To be able to differentiate among numerous facial expressions of emotion, it is crucial to identify the classes of facial expressions. The methodology used in this article is based on convolutional neural networks (CNN). In this paper Deep Learning CNN is used to examine Alex net architectures. Improvements were achieved by applying the transfer learning approach and modifying the fully connected layer with the Support Vector Machine(SVM) classifier. The system succeeded by achieving satisfactory results on icv-the MEFED dataset. Improved models achieved around 64.29 %of recognition rates for the classification of the selected expressions. The results obtained are acceptable and comparable to the relevant systems in the literature provide ideas a background for further improvements.

Research on intelligent accounting system based on intelligent financial data sheet analysis system considering complex data mining is conducted in the paper. The expert audit system extracts business records from the business database according to the specified audit conditions, and the program automatically calculates the total amount of the amount data items, and then compares it with the standard or normal business, reflecting the necessary information such as differences and also possible audit trails. In order to find intrusion behaviors and traces, data collection is carried out from multiple points in the network system. The collection content includes system logs, network data packets, important files, and the status and the behavior of the user activities. Furthermore, complex data mining model is combined for the systematic analysis on the system performance. The simulation on the collected data is provided to the validate the performance.

Redactable blockchain allows modifiers or voting committees with modification privileges to edit the data on the chain. Among them, trapdoor holders in chameleon-based hash redactable blockchains can quickly compute hash collisions for arbitrary data without breaking the link of the hash-chain. However, chameleon-based hash redactable blockchain schemes have difficulty solving issues such as editing operations with different granularity or conflicts and auditing modifiers that abuse editing privileges. To address the above challenges, we propose a redactable blockchain with Cross-audit and Diversity Editing (CDEdit). The proposed scheme distributes subdivided transaction-level and block-level tokens to the matching modifier committee to weaken the influence of central power. A number of modifiers are unpredictably selected based on reputation value proportions and the mapping of the consistent hash ring to enable diversity editing operations, and resist Sybil attacks. Meanwhile, an adaptive cross-auditing protocol is proposed to adjust the roles of modifiers and auditors dynamically. This protocol imposes a reputation penalty on the modifiers of illegal edits and solves the problems of abuse of editing privileges and collusion attacks. In addition, We used ciphertext policy attribute-based encryption (CP-ABE) and chameleon hashes with ephemeral trapdoor (CHET) for data modification, and present a system steps and security analysis of CDEdit. Finally, the extensive comparisons and evaluations show that our scheme costs less time overhead than other schemes and is suitable for complex application scenarios, e.g. IoT data management.

Dual Connectivity is a key approach to achieving optimization of throughput and latency in heterogeneous networks. Originally a technique introduced by the 3rd Generation Partnership Project (3GPP) for terrestrial communications, it is not been widely explored in satellite systems. In this paper, Dual Connectivity is implemented in a multi-orbital satellite network, where a network model is developed by employing the diversity gains from Dual Connectivity and Carrier Aggregation for the enhancement of satellite uplink capacity. An introduction of software defined network controller is performed at the network layer coupled with a carefully designed hybrid resource allocation algorithm which is implemented strategically. The algorithm performs optimum dynamic flow control and traffic steering by considering the availability of resources and the channel propagation information of the orbital links to arrive at a resource allocation pattern suitable in enhancing uplink system performance. Simulation results are shown to evaluate the achievable gains in throughput and latency; in addition we provide useful insight in the design of multi-orbital satellite networks with implementable scheduler design.

Resilience of urban infrastructure to sudden, system-wide, potentially catastrophic events is a critical need across domains. The growing connectivity of infrastructure, including its cyber-physical components which can be controlled in real time, offers an attractive path towards rapid adaptation to adverse events and adjustment of system objectives. However, existing work in the field often offers disjoint approaches that respond to particular scenarios. On the other hand, abstract work on control of complex systems focuses on attempting to adapt to the changes in the system dynamics or environment, but without understanding that the system may simply not be able to perform its original task after an adverse event. To address this challenge, this programmatic paper proposes a vision for a new paradigm of infrastructure resilience. Such a framework treats infrastructure across domains through a unified theory of controlled dynamical systems, but remains cognizant of the lack of knowledge about the system following a widespread adverse event and aims to identify the system's fundamental limits. As a result, it will enable the infrastructure operator to assess and assure system performance following an adverse event, even if the exact nature of the event is not yet known. Building off ongoing work on assured resilience of control systems, in this paper we identify promising early results, challenges that motivate the development of resilience theory for infrastructure system, and possible paths forward for the proposed effort.

Power system robustness against high-impact low probability events is becoming a major concern. To depict distinct phases of a system response during these disturbances, an irregular polygon model is derived from the conventional trapezoid model and the model is analytically investigated for transmission system performance, based on which resiliency metrics are developed for the same. Furthermore, the system resiliency to windstorms is evaluated on the IEEE reliability test system (RTS) by performing steady-state and dynamic security assessment incorporating protection modelling and corrective action schemes using the Power System Simulator for Engineering (PSS®E) software. Based on the results of steady-state and dynamic analysis, modified resiliency metrics are quantified. Finally, this paper quantifies the interdependency of operational and infrastructure resiliency as they cannot be considered discrete characteristics of the system.

Soft real-time applications, including multimedia, gaming, and smart appliances, rely on specific architectural characteristics to deliver output in a time-constrained fashion. Any violation of application deadlines can lower the Quality-of-Service (QoS). The data sets associated with these applications are distributed over cores that communicate via Network-on-Chip (NoC) in multi-core systems. Accordingly, the response time of such applications depends on the worst-case latency of request/reply packets. A malicious implant such as Hardware Trojan (HT) that initiates a delay-of-service attack can tamper with the system performance. We model an HT that mounts a time-delay attack in the system by violating the path selection strategy used by the adaptive NoC router. Our analysis shows that once activated, the proposed HT increases the packet latency by 17% and degrades the system performance (IPC) by 18% over the Baseline. Furthermore, we propose an HT detection framework that uses packet traffic analysis and path monitoring to localise the HT. Experiment results show that the proposed detection framework exhibits 4.8% less power consumption and 6.4% less area than the existing technique.

A unified cloud management platform is the key to efficient and secure management of cloud computing resources. To improve the operation effect of the power cloud service platform, power companies can use the micro-service architecture technology to carry out data processing, information integration, and innovative functional architecture of the power cloud service platform, realize the optimal design of the power cloud service platform and improve the power cloud service platform-security service quality. According to the technical requirements of the power cloud security management platform, this paper designs the technical architecture of the power unified cloud security management platform and expounds on the functional characteristics of the cloud security management platform to verify the feasibility and effectiveness of the cloud security management platform.

The study focused on assessing and testing Windows 10 to identify possible vulnerabilities and their ability to withstand cyber-attacks. CVE data, alongside other vulnerability reports, were instrumental in measuring the operating system's performance. Metasploit and Nmap were essential in penetration and intrusion experiments in a simulated environment. The study applied the following testing procedure: information gathering, scanning and results analysis, vulnerability selection, launch attacks, and gaining access to the operating system. Penetration testing involved eight attacks, two of which were effective against the different Windows 10 versions. Installing the latest version of Windows 10 did not guarantee complete protection against attacks. Further research is essential in assessing the system's vulnerabilities are recommending better solutions.

The most widely used protocol for routing across the 6LoWPAN stack is the Routing Protocol for Low Power and Lossy (RPL) Network. However, the RPL lacks adequate security solutions, resulting in numerous internal and external security vulnerabilities. There is still much research work left to uncover RPL's shortcomings. As a result, we first implement the worst parent selection (WPS) attack in this paper. Second, we offer an intrusion detection system (IDS) to identify the WPS attack. The WPS attack modifies the victim node's objective function, causing it to choose the worst node as its preferred parent. Consequently, the network does not achieve optimal convergence, and nodes form the loop; a lower rank node selects a higher rank node as a parent, effectively isolating many nodes from the network. In addition, we propose DWA-IDS as an IDS for detecting WPS attacks. We use the Contiki-cooja simulator for simulation purposes. According to the simulation results, the WPS attack reduces system performance by increasing packet transmission time. The DWA-IDS simulation results show that our IDS detects all malicious nodes that launch the WPS attack. The true positive rate of the proposed DWA-IDS is more than 95%, and the detection rate is 100%. We also deliberate the theoretical proof for the false-positive case as our DWA-IDS do not have any false-positive case. The overhead of DWA-IDS is modest enough to be set up with low-power and memory-constrained devices.

Cyber-Physical Power Systems (CPPSs) currently face an increasing number of security attacks and lack methods for optimal proactive security decisions to defend the attacks. This paper proposed an optimal defensive method based on game theory to minimize the system performance deterioration of CPPSs under cyberspace attacks. The reinforcement learning algorithmic solution is used to obtain the Nash equilibrium and a set of metrics of system vulnerabilities are adopted to quantify the cost of defense against cyber-attacks. The minimax-Q algorithm is utilized to obtain the optimal defense strategy without the availability of the attacker's information. The proposed solution is assessed through experiments based on a realistic power generation microsystem testbed and the numerical results confirmed its effectiveness.

Visible light communications (VLC) have been the focus of many recent investigations due to its potential for transmitting data at a higher bitrate than conventional communication systems. Alongside the advantages of being energy efficient through the use of LEDs (Light Emitting Diodes), it is imperative that these systems also take in consideration privacy and security measures available. This work highlights the technical aspects of a typical 16-QAM (Quadrature Amplitude Modulation) VLC system incorporating an enhanced privacy feature using an hyperchaotic map to scramble the symbols. The results obtained in this study showed a low dispersion symbol constellation while communicating at 100 Baud and with a 1 m link. Using the measured EVM (Error Vector Magnitude) of the constellation, the BER (Bit Error Rate) of this system was estimated to be bellow 10−12 which is lower than the threshold limit of 3.8.10−3 that corresponds to the 7% hard-decision forward error correction (HD- FEC) for optimal transmission, showing that this technique can be implemented with higher bitrates and with a higher modulation index.

This paper studies the differential privacy-preserving problem of discrete-time multi-agent systems (MASs) with antagonistic information, where the connected signed graph is structurally balanced. First, we introduce the bipartite consensus definitions in the sense of mean square and almost sure, respectively. Second, some criteria for mean square and almost sure bipartite consensus are derived, where the eventualy value is related to the gauge matrix and agents’ initial states. Third, we design the ε-differential privacy algorithm and characterize the tradeoff between differential privacy and system performance. Finally, simulations validate the effectiveness of the proposed algorithm.

In Cloud Computing, a wide range of virtual platforms are integrated and offer users a flexible pay-as-you-need service. Compared to conventional computing systems, the provision of an acceptable degree of resilience to cloud services is a daunting challenge due to the complexities of the cloud environment and the need for efficient technology that could sustain cloud advantages over other technologies. For a cloud guest resilience service solution, we provide architectural design, installation specifics, and performance outcomes throughout this article. Virtual Machine Manager (VMM) enables execution statistical test of the virtual machine states to be monitored and avoids to reach faulty states.

This paper emphasizes the implementation of a conditioning circuit specific for an inductive or capacitive sensor. There are some inductive sensors, such as the proximity sensor, for which the inductance is dependent with the distance, or capacitive sensors strongly dependent with the humidity, distance, etc. This category of sensors is suitable for AC domain excitation from the measurement procedure point of view. Taking into consideration the fabrication technology, the measured physical quantity is being encoded as frequency or amplitude. To generate a sinusoidal signal with constant frequency and amplitude, the Colpitts or Hartley oscillators can be used [1], [2]. But the novelty of this paper is a different approach which reveals a microcontroller-based technology where the LC circuit works in an oscillating regime even though there is an underdamped oscillation behavior. For the oscillations’ occurrence, there will be a periodical energy injection using a driving source. One of the main advantages of the mentioned circuit is the small component number. The central unit of the embedded system will fulfil two functions: maintains the oscillating regime and measures the amplitude or frequency of the output signal. In this way, the built embedded system will be robust and easy to use due to its software configuration capabilities. As a plus, such a system can measure additional sensors used in environment parameters’ compensating procedure.

In the last few years, cluster ensembles have emerged as powerful techniques that integrate multiple clustering methods into recommender systems. Such integration leads to improving the performance, quality and the accuracy of the generated recommendations. This paper proposes a novel recommender system based on a cluster ensemble technique for big data. The proposed system incorporates the collaborative filtering recommendation technique and the cluster ensemble to improve the system performance. Besides, it integrates the Expectation-Maximization method and the HyperGraph Partitioning Algorithm to generate new recommendations and enhance the overall accuracy. We use two real-world datasets to evaluate our system: TED Talks and MovieLens. The experimental results show that the proposed system outperforms the traditional methods that utilize single clustering techniques in terms of recommendation quality and predictive accuracy. Most importantly, the results indicate that the proposed system provides the highest precision, recall, accuracy, F1, and the lowest Root Mean Square Error regardless of the used similarity strategy.

Aimming at the severe security threat faced by information transmission in wireless communication, the artificial interference in physical layer security technology was considered, and the influence of artificial interference signal style on system information transmission security was analyzed by simulation, which provided technical accumulation for the design of wireless security transmission system based on artificial interference.

The emerging vehicular edge computing (VEC) technology has the potential to bring revolutionary development to vehicular ad hoc network (VANET). However, the edge computing servers (ECSs) are subjected to a variety of security threats. One of the most dangerous types of security attacks is the Sybil attack, which can create fabricated virtual vehicles (called Sybil vehicles) to significantly overload ECSs' limited computation resources and thus disrupt legitimate vehicles' edge computing applications. In this paper, we present a novel Sybil attack detection system on ECSs that is based on the design of a credibility enhanced temporal graph convolutional network. Our approach can identify the malicious vehicles in a dynamic traffic environment while preserving the legitimate vehicles' privacy, particularly their local position information. We evaluate our proposed approach in the SUMO simulator. The results demonstrate that our proposed detection system can accurately identify most Sybil vehicles while maintaining a low error rate.

The integration of IDS and Internet of Things (IoT) with deep learning plays a significant role in safety. Security has a strong role to play. Application of the IoT network decreases the time complexity and resources. In the traditional intrusion detection systems (IDS), this research work implements the cutting-edge methodologies in the IoT environment. This research is based on analysis, conception, testing and execution. Detection of intrusions can be performed by using the advanced deep learning system and multiagent. The NSL-KDD dataset is used to test the IoT system. The IoT system is used to test the IoT system. In order to detect attacks from intruders of transport layer, efficiency result rely on advanced deep learning idea. In order to increase the system performance, multi -agent algorithms could be employed to train communications agencies and to optimize the feedback training process. Advanced deep learning techniques such as CNN will be researched to boost system performance. The testing part an IoT includes data simulator which will be used to generate in continuous of research work finding with deep learning algorithms of suitable IDS in IoT network environment of current scenario without time complexity.