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In recent years, blockchain (BC) technologies have been increasing for data secrecy, system reliability and safety. BC is vulnerable to cyberattacks despite its utility. According to the statistics, attacks are rare, which differs greatly from the average. The goal of BC attack detection is to discover insights, patterns and anomalies within massive data repositories, it may benefit from deep learning. In this paper, the Prevention of Insider Attacks using Blockchain with Hierarchical Auto-associative Polynomial Convolutional Neural Network in Cloud Platform (PIS-BCNN-CP) is proposed. Here, the node authentication is handled by the smart contract. The aim of authorizing a node is to confirm that only a particular node has the possibility to submit and recover the information. Then Hierarchical Auto-associative Polynomial Convolutional Neural Network (HAAPCNN) is proposed to detect the Insider Attacks as Malicious and Normal. Generally, HAAPCNN does not agree with any optimization strategies to determine the optimal parameters for guaranteeing the exact detection of insider attacks. Hence, the Bear Smell Search Algorithm (BSSA) is exploited to optimize the weight parameters of a HAAPCNN. The BC Enabled Secure Data Storage depends on Proof of Continuous Work (PoCW) consensus BC algorithm is used. The proposed system is implemented and evaluated using performance metrics. The results provide higher accuracy, and lower False Negative Rate when compared with existing state-of-the-art methods.

In Wireless Sensor Network (WSN), node localization is a crucial need for precise data gathering and effective communication. However, high energy requirements, long inter-node distances and unpredictable limitations create problems for traditional localization techniques. This study proposes an innovative two-stage approach to improve localization accuracy and maximize route selection in WSNs. In the first stage, the Self-Adaptive Binary Waterwheel Plant Optimization (SA-BWP) algorithm is used to evaluate a node’s trustworthiness to achieve accurate localization. In the second stage, the Gazelle-Enhanced Binary Waterwheel Plant Optimization (G-BWP) method is employed to determine the most effective data transfer path between sensor nodes and the sink. To create effective routes, the G-BWP algorithm takes into account variables like energy consumption, shortest distance, delay and trust. The goal of the proposed approach is to optimize WSN performance through precise localization and effective routing. MATLAB is used for both implementation and evaluation of the model, which shows improved performance over current methods in terms of throughput, delivery ratio, network lifetime, energy efficiency, delay reduction and localization accuracy in terms of various number of nodes and rounds. The proposed model achieves highest delivery ratio of 0.97, less delay of 5.39, less energy of 23.3 across various nodes and rounds.

New Simple Queue (NSQ) is a distributed messaging platform developed in Golang that can handle billions of daily messages. Its distributed architecture ensures high fault tolerance and availability. Given NSQ’s widespread application across various fields, it is crucial to focus on the system’s robustness and data transmission security. Therefore, a rigorous mathematical logic analysis of NSQ’s messaging mechanism is essential for verifying its reliability. This paper formalizes NSQ’s core components using Communicating Sequential Processes (CSP), resulting in a comprehensive formal model of the system. Furthermore, this paper utilizes the Process Analysis Toolkit (PAT) for practical implementation of the model, verifying five critical properties of the NSQ system. And the verification results demonstrate that the NSQ system successfully satisfies these essential properties, highlighting its flexibility, robustness and efficient messaging service capabilities. Moreover, this paper focuses on formalizing and verifying the security mechanisms in NSQ’s data transmission. By integrating the Transport Layer Security (TLS) protocol into the NSQ system and employing a man-in-the-middle model to simulate deception and interception attacks, it is demonstrated that the TLS protocol enhances the security of NSQ data transmission. This paper also proposes upgrading from one-way to two-way certificate authentication to further enhance TLS data security. Experimental results reveal that despite significant security improvements with the TLS protocol, producer and consumer processes remain vulnerable to spoofing attacks under specific insecure network conditions, leading to potential data leaks. Therefore, the TLS protocol can significantly improve the data security of the NSQ system.

Software has emerged as an indispensable part of everyday existence in the increasingly digital age, impacting everything from healthcare to finance to transportation to industries. Given the increasing reliance on software, ensuring it is reliable and safe is crucial. Discovering and fixing vulnerabilities is crucial for sustaining the security and dependability of software. Numerous issues, including illegal accessibility, data breaches, and service interruptions, may result from these vulnerabilities. Software updates are applied by patching to address vulnerabilities, increase functionality, or improve performance. Adopting best practices for patching and vulnerability management can significantly lower security risks and improve the overall resilience of software environments despite certain limitations. Very few attempts have been made to model vulnerability Patch models (VPMs), even though Vulnerability Discovery Modeling (VDM) has been modeled based on the impact of vulnerabilities discovered over time, which help software vendors identify security trends, forecast security investments, and plan patches. The proposed modeling framework, which includes vulnerability discovery modeling and vulnerability Patch modeling, is designed to provide a comprehensive understanding of the patch management workflow. This paper also introduces a new modeling framework in the context of Patch. A statistical analysis has been conducted utilizing patch datasets to demonstrate the proposed systematic layout, providing a solid foundation for the research findings.

Due to the presence of reliability, security, or performance-related issues, software systems will become nondependable during the early development phase. Currently, there is a lack of research addressing these dependability issues in software quality analysis. To bridge this gap, this study proposes a neutrosophic inference system (NIS)-based model to predict reliability, security and performance attributes during the early phase. The NIS model accommodates uncertainties, imprecisions, indeterminacies and incompleteness of metric values utilizing its truth, indeterminate and false components. To enhance the prediction accuracy of the NIS model, a rule-base formation algorithm is proposed for NIS considering domain expert knowledge. Finally, an artificial neural network (ANN) model is designed based on estimated values of reliability, security and performance attributes to predict the total number of faults in software projects. Comparative analysis demonstrates that the proposed model outperforms other existing models. This proposed methodology helps software developers in assessing software dependability from the beginning stage of software development.

Since the advent of networked systems, fuzzy graph theory has surfaced as a fertile paradigm for handling uncertainties and ambiguities. Among the different modes of handling challenges created by the uncertainties and ambiguities of current networked systems, integrating fuzzy graph theory with cryptography has emerged as the most promising approach. In this regard, this review paper elaborates on potentially studying fuzzy graph-based cryptographic techniques, application perspectives, and future research directions. Since the expressive power of fuzzy graphs allows the cryptographic schemes to handle imprecise information and to enhance security in many domains, several domains have benefited, such as image encryption, key management, and attribute-based encryption. The paper analyzes in depth the research landscape, mainly by focusing on the varied techniques used, such as fuzzy logic for key generation and fuzzy attribute representation for access control policies. A comparison with performance metrics unveils the trade-offs and advantages of different fuzzy graph-based approaches in efficiency, security strength, and computational overhead. Additionally, the survey explores the security applications of fuzzy graph-based cryptography and underpins potential development for secure communication in wireless sensor networks, privacy-preserving data mining, fine-grained access control in cloud computing, and blockchain security. Some challenges and research directions, such as the standardization of fuzzy logic operators, algorithmic optimization, integration with emerging technologies, and exploitation of post-quantum cryptography applications, are also brought out. This review will thus bring insight into this interdisciplinary domain and stimulate further research for the design of more robust, adaptive, and secure cryptographic systems in the wake of rising complexities and uncertainties.

Distributed Denial of Service (DDoS) attacks remain a persistent and formidable threat in the ever-changing world of cyber security. These attacks have the potential to disrupt internet services and cause substantial financial and reputational concerns. The major challenge is developing an adaptable and real-time Intrusion Detection System (IDS) that can detect and neutralize DDoS attacks effectively and quickly, even when attackers use increasingly advanced ways to avoid detection. The problem concerns the development of a dynamic and real-time intrusion detection technique that combines the benefits of logistic regression for anomaly detection with GoogLeNet for deep learning-based network traffic analysis. This paper proposes a unique framework for intrusion detection that blends logistic regression-based anomaly detection with GoogLeNet deep learning capabilities. The combination of these technologies makes it easier to identify and mitigate DDoS attacks, hence improving the security of internet-based systems. The proposed IDS framework utility is proved through experimental evaluations, which highlight its capacity to effectively identify DDoS attacks while minimizing false positives. The use of this technology in real-time during security games demonstrates its potential to improve online service security infrastructure and reduce the impact of DDoS attacks on critical networks and data resources.

The cloud-based gaming services have given rise to an abundance of fresh worries around data protection and safety. The present cloud gaming security solutions suffer from a key issue in that they do not include a comprehensive framework. This framework would take into consideration the dynamic and interactive nature of the data center environment. When trying to mitigate risks in today’s continuously moving cyber threat scenario, it is imperative to consider all relevant factors, including the health of the system, individual costs, and potential advantages. The existing works address about how game theory might be used to improve the safety of cloud-based gaming. The purpose of this research is to overcome the research gap by developing a novel model that captures the multiple linkages between security measures and potential attacks, supplying a more holistic perspective on cloud gaming security in the process. In this line of investigation, a Noncooperative Game-Theoretic Model is used to investigate the dynamics of predators and prey. In this research, a Noncooperative Game-Theoretic Model is presented with the purpose of analysing and perfecting security measures inside the dynamic data-center environment of cloud gaming. Precautionary safety measures take on the role of a predator, while potential threats take on the role of prey. The results prove that the Noncooperative Game-Theoretic Model is an effective tool for enhancing safeguards in cloud-based online gaming environments.

Secure and private user data are more important than ever with the explosion of online gaming platforms and the resulting deluge of user information. Intending to protect gaming ecosystems and maintain user confidence, Heuristic Predictive Modeling provides a proactive security strategy by allowing early detection and mitigation of potential risks. The ever-changing nature of the game, the wide variety of user interactions, and the always-evolving strategies of cybercriminals all contribute to the singular problems that data management and security encounter in modern gaming settings. This research proposes Heuristic Predictive Modeling for Gaming Security (HPM-GS). This system can analyze gaming data in real time and detect trends and abnormalities that could indicate security breaches. It uses advanced algorithms and machine learning approaches. With HPM-GS, gaming platforms can keep their users safe and secure by anticipating and proactively addressing security threats. Several areas of gaming security can benefit from HPM-GS, such as user authentication, detection of cheats, prevention of fraud, and incident response. Enhanced user experience and platform reliability can be achieved by incorporating HPM-GS into pre-existing security frameworks, which allows gaming platforms to strengthen their defenses and efficiently reduce risks. Extensive simulation studies assess the effectiveness of HPM-GS in gaming security. The performance metrics of HPM-GS, such as detection accuracy, false positive rates, and response time, are evaluated using real-world datasets and simulated attack scenarios. The simulation findings show that HPM-GS is a good solution for protecting gaming environments from cyber-attacks. The HPM-GS is a proactive, elastic gaming application data management and security method. The purpose of this research is to emphasize the potential of HPM-GS to improve the security posture of online gaming platforms and to ensure that players have a gaming experience that is both safer and more pleasant. This is accomplished by addressing the significance of HPM-GS, potential difficulties, proposed techniques, implementations, and simulation analysis.

Securing a network at its edge, that is, the location where a user creates and stores data within a larger network is defined as edge security. It is essential to have this type of perimeter security in edge computing (EC) settings. One of the biggest challenges in EC is establishing and sustaining dependable network connectivity at the edge. This study presents an innovative strategy to improve network security by combining EC and artificial intelligence (AI). With the explosion of Internet of Things (IoT) devices and the developing complexity of cyber threats, conventional security assessments are becoming insufficient. In this study, we suggested a novel water wave-optimized flexible recurrent neural network (WWO-FRNN) for security attack detection on EC. The HTTP DATASET CSIC 2010 was gathered for this study and used for intrusion detection. The data were preprocessed utilizing min–max normalization to transmogrify numeric data into a communal scale. Next, the feature is extracted for dimensional reduction using principle component analysis (PLA). The proposed method is implemented using Python software. WWO-FRNN is compared to the other traditional algorithms. The result shows the proposed methods achieved high accuracy, precision, recall, and F1-score. The study demonstrates its effectiveness against different types of attacks, improving edge network security for IoT applications.

E-banking requires a high level of trust because it involves disclosure of vital and confidential information which customers are often reluctant to share because of the risks involved. With the growth of technology, there has been an increase in cyber threats. While transacting online customers fear the security of their personal information to safeguard against financial losses and unwanted disturbance. Thus, customers’ perceived security and credibility have a critical role in influencing their trust in E-banking. Whereas the identified literature has not paid much attention to studying the impact of identified E-service quality dimensions (credibility and security) on E-trust, particularly in the developing nations context. Therefore, to fill the existing gap, the study seeks to measure the impact of credibility and security dimensions of E-service quality on customers’ E-trust in the context of Indian banks. The sample for the study is taken from customers of public and private sector banks of Delhi/NCR. A total of 420 complete and suitable responses were received for final analysis. During data collection, it was ensured that the respondent is over the age of 18, has an active bank account, and has been using banking E-services for at least six months. Data for the study is analyzed using FEA, CFA, and SEM via using SPSS 24 and AMOS 21 software. The findings of the study reveal that both the credibility and security dimensions of E-service quality have a strong impact in influencing customer trust in E-banking. Banks’ focus on enhancing credibility and security measures creates value in customers’ eyes that as a result enhances their faith in the service provider.

Serendipity has been a major player in most dye discoveries, and phthalocyanines are no exception. The true account of their discovery in 1928 is given using information provided by one of the individuals involved (Ron Greig). One of the inventors, Drescher, after making a key observation as to the nature of the insoluble blue impurity formed in the routine manufacture of phthalimide from phthalic anhydride, was killed weeks later when, on his beloved Sunbeam motorcycle, he had a head-on collision with a steamroller on his way to work. The unique properties of phthalocyanine dyes and pigments make them the colorant of choice for most blue and green colours. Thus most blue and green cars, including sports cars, are coloured by phthalocyanine pigments. In addition to these traditional uses, phthalocyanines are also finding extensive use in modern hi-tech areas. They are used for their colour as, for example, cyan dyes in ink jet printing, and in colourless applications such as infrared absorbers in security. The discovery, traditional and hitech applications of phthalocyanines are described in detail in this paper.

A quantum authentication scheme is presented in this paper. Two parties share Einstein-Podolsky-Rosen(EPR) pairs previously as the identification token. They create auxiliary EPR pairs to interact with the identification token. Then the authentication is accomplished by a complete Bell state measurement. This scheme is proved to be secure. If no errors and eavesdroppers exist in the transmission, the identification token is unchanged after the authentication. So it can be reused.

This paper addresses the following general problem of tree regular model-checking: decide whether where is the reflexive and transitive closure of a successor relation induced by a term rewriting system , and and are both regular tree languages. We develop an automatic approximation-based technique to handle this – undecidable in general – problem in the case when term rewriting system rules are non left-linear.

Verification of string manipulation operations is a crucial problem in computer security. In this paper, we present a new relational string verification technique based on multi-track automata. Our approach is capable of verifying properties that depend on relations among string variables. This enables us to prove that vulnerabilities that result from improper string manipulation do not exist in a given program. Our main contributions in this paper can be summarized as follows: (1) We formally characterize the string verification problem as the reachability analysis of string systems and show decidability/undecidability results for several string analysis problems. (2) We develop a sound symbolic analysis technique for string verification that over-approximates the reachable states of a given string system using multi-track automata and summarization. (3) We evaluate the presented techniques with respect to several string analysis benchmarks extracted from real web applications.

In a wireless sensor network, we often require the deployment of new nodes to extend the lifetime of the network because some sensor nodes may be lost due to power exhaustion problem or they may be also malicious nodes. In order to protect malicious nodes from joining the sensor network, access control mechanism becomes a major challenging problem in the design of sensor network protocols. Existing access control protocols designed for wireless sensor networks require either high communication overheads or they are not scalable due to involvement of the base station during authentication and key establishment processes. In this paper, we propose a new access control scheme for large-scale distributed wireless sensor networks, which not only identifies the identity of each node but it has also ability to differentiate between old nodes and new nodes. The proposed scheme does not require involvement of the base station during authentication and key establishment processes, and it can be easily implemented as a dynamic access control protocol. In addition, our scheme significantly reduces communication costs in order to authenticate neighbor nodes among each other and establish symmetric keys between neighbor nodes as compared with existing approaches. Further, our scheme is secure against different attacks and unconditionally secure against node capture attacks. The simulation results of our scheme using the AVISPA (Automated Validation of Internet Security Protocols and Applications) tool ensure that our scheme is safe.

We demonstrate a large area time domain terahertz (THz) imaging system capable of scanning 1 meter square area in less than 20-100 minutes for several security applications. The detection of concealed explosives; metallic and non-metallic weapons (such as ceramic, plastic or composite guns and knives); and flammables in luggage, packages and personnel has been demonstrated. Transmission mode images of luggage containing threat items are discussed. Reflection mode images of luggage and personnel are discussed. Time domain THz images can be analyzed for 3 dimensional and volumetric information. Time domain THz images have advantages over coherent narrow band imaging methods, with freedom from interference artifacts and with greater ability to discard irrelevant or intervening reflections through time discrimination.

Field Programmable Gate Arrays (FPGA), as one of the popular circuit implementation platforms, provide the flexible and powerful way for different applications. IC designs are configured to FPGA through bitstream files. However, the configuration process can be hacked by side channel attacks (SCA) to acquire the critical design information, even under the protection of encryptions. Reports have shown many successful attacks against the FPGA cryptographic systems during the bitstream loading process to acquire the entire design. Current countermeasures, mostly random masking methods, are effective but also introduce large hardware complexity. They are not suitable for resource-constrained scenarios such as Internet of Things (IoT) applications. In this paper, we propose a new secure FPGA masking scheme to counter the SCA. By utilizing the FPGA partial reconfiguration feature, the proposed technique provides a light-weight and flexible solution for the FPGA decryption masking.

In this paper we provide a quantum key distribution (QKD) scheme based on the correlations of Einstein–Podolsky–Rosen (EPR) pairs. The scheme uses an auxiliary qubit to interact with the EPR pair and does the Bell state measurement to get the key. It is proved to be secure. All EPR pairs are used in distributing the key except some error-checking bits. So it is efficient. On the other hand there are less classical communications needed in the scheme.

In this paper we propose a high performance searching-based chaotic cipher. Experiments shows that its efficiency is comparable to the efficiencies of some widely used and known ciphers, namely, AES, RC4 and Sosemanuk. Also, its performance is better than some recently proposed chaotic ciphers of the same kind. The proposed cryptosystem shows independence with respect to the statistical characteristics of the plain texts, which prevents statistical attacks. The results of the tests suggest that this chaotic cipher can be competitive for practical usage.