Wireless Sensor Networks (WSNs) play a pivotal role in critical applications, ranging from industrial control systems to healthcare monitoring. As these networks become increasingly integrated into our daily lives, understanding their energy consumption behavior is paramount for achieving sustainability and resilience. This paper delves into the intricate relationship between energy consumption patterns in WSNs and their security implications within critical contexts. We commence by conducting a comprehensive analysis of energy consumption behavior in WSNs, considering factors such as data transmission, node mobility, and sensing activities. Through empirical studies and simulations, we identify key parameters influencing energy utilization and establish a foundation for further investigation. Building upon this understanding, we explore the security impacts associated with the energy profile of WSNs operating in critical environments. We address potential vulnerabilities arising from compromised nodes due to energy depletion, communication constraints, and malicious attacks. By examining these security challenges, we highlight the urgency of developing robust solutions to ensure the reliability and integrity of WSNs in critical applications. In response to these challenges, we propose mitigation strategies that synergistically address both energy consumption and security concerns. Our approach based on security information and event management with deep learning security use case algorithms for impact mitigation. These strategies aim to enhance the overall sustainability and security of WSNs, ensuring their continued functionality in demanding and sensitive environments. In conclusion, this paper provides a comprehensive overview of the intricate interplay between energy consumption behavior and security impacts in WSNs within critical contexts. Our findings underscore the need for holistic approaches that integrate energy-awareness and security measures to fortify the resilience of WSNs, fostering their sustainable deployment in critical applications.

Cloud computing allows us to access available systems and pay for what we require whenever needed. When there is access to the internet, it uses some techniques like Service-Oriented Architecture (SOA), virtualization, distributed computing, etc. Cloud computing has transformed the way people utilize and handle computer services. It enables sharing, pooling, and accessing resources on the Internet. It offers tremendous advantages that enhance the cost-effectiveness and efficiency of organizations, which is marked by security challenges or threats that can compromise data, service safety and privacy. This paper gives an overview of cloud computing and explores the threats and vulnerabilities related to cloud computing with its countermeasures. It also explores the recent advancement in cloud computing threats and countermeasures. Further, this paper highlights the case studies on recent attacks and vulnerabilities which are compromised. Finally, this paper concludes that cloud computing is efficiently used to mitigate the threats and vulnerabilities with its countermeasures.

In the rapidly evolving technological landscape, securing cloud computing environments while optimizing resource allocation is of paramount importance. This research study introduces a novel approach that seamlessly integrates deep learning with a nature-inspired optimization algorithm for achieving joint security and resource allocation. The proposed methodology harnesses the power of ResNet, a proven deep learning architecture, to bolster cloud security by identifying and mitigating threats effectively. Complementing this, the Flower Pollination Algorithm (FPA), inspired by natural pollination processes, is employed to strike an optimal balance between resource utilization and cost efficiency. This amalgamation creates a robust framework for managing cloud resources, ensuring the confidentiality, integrity, and availability of data and services, all while maintaining efficient resource allocation. The approach is flexible, adaptive, and capable of addressing the dynamic nature of cloud environments, making it a valuable asset for organizations seeking to enhance their cloud security posture without compromising on resource efficiency.

Developing network intrusion detection systems (IDS) presents significant challenges due to the evolving nature of threats and the diverse range of network applications. Existing IDSs often struggle to detect dynamic attack patterns and covert attacks, leading to misidentified network vulnerabilities and degraded system performance. These requirements must be met via dependable, scalable, effective, and adaptable IDS designs. Our IDS can recognise and classify complex network threats by combining the Deep Q-Network (DQN) algorithm with distributed agents and attention techniques.. Our proposed distributed multi-agent IDS architecture has many advantages for guiding an all-encompassing security approach, including scalability, fault tolerance, and multi-view analysis. We conducted experiments using industry-standard datasets including NSL-KDD and CICIDS2017 to determine how well our model performed. The results show that our IDS outperforms others in terms of accuracy, precision, recall, F1-score, and false-positive rate. Additionally, we evaluated our model s resistance to black-box adversarial attacks, which are commonly used to take advantage of flaws in machine learning. Under these difficult circumstances, our model performed quite well.We used a denoising autoencoder (DAE) for further model strengthening to improve the IDS s robustness. Lastly, we evaluated the effectiveness of our zero-day defenses, which are designed to mitigate attacks exploiting unknown vulnerabilities. Through our research, we have developed an advanced IDS solution that addresses the limitations of traditional approaches. Our model demonstrates superior performance, robustness against adversarial attacks, and effective zero-day defenses. By combining deep reinforcement learning, distributed agents, attention techniques, and other enhancements, we provide a reliable and comprehensive solution for network security.

As the ongoing energy transition requires more communication infrastructure in the electricity grid, this intro-duces new possible attack vectors. Current intrusion detection approaches for cyber attacks often neglect the underlying phys-ical environment, which makes it especially hard to detect data injection attacks. We follow a process-aware approach to eval-uate the communicated measurement data within the electricity system in a context-sensitive way and to detect manipulations in the communication layer of the SCADA architecture. This paper proposes a sophisticated tool for intrusion detection, which integrates power flow analysis in real-time and can be applied locally at field stations mainly at the intersection between the medium and low voltage grid. Applicability is illustrated using a simulation testbed with a typical three-node architecture and six different (attack) scenarios. Results show that the sensitivity parameter of the proposed tool can be tuned in advance such that attacks can be detected reliably.

Network intrusion detection technology has developed for more than ten years, but due to the network intrusion is complex and variable, it is impossible to determine the function of network intrusion behaviour. Combined with the research on the intrusion detection technology of the cluster system, the network security intrusion detection and mass alarms are realized. Method: This article starts with an intrusion detection system, which introduces the classification and workflow. The structure and working principle of intrusion detection system based on protocol analysis technology are analysed in detail. Results: With the help of the existing network intrusion detection in the network laboratory, the Synflood attack has successfully detected, which verified the flexibility, accuracy, and high reliability of the protocol analysis technology. Conclusion: The high-performance cluster-computing platform designed in this paper is already available. The focus of future work will strengthen the functions of the cluster-computing platform, enhancing stability, and improving and optimizing the fault tolerance mechanism.

Computer networks are increasingly vulnerable to security disruptions such as congestion, malicious access, and attacks. Intrusion Detection Systems (IDS) play a crucial role in identifying and mitigating these threats. However, many IDSs have limitations, including reduced performance in terms of scalability, configurability, and fault tolerance. In this context, we aim to enhance intrusion detection through a cooperative approach. To achieve this, we propose the implementation of ICIDS-BB (Intelligent Cooperative Intrusion Detection System based on Blockchain). This system leverages Blockchain technology to secure data exchange among collaborative components. Internally, we employ two machine learning algorithms, the decision tree and random forest, to improve attack detection.

Envisioned to be the next-generation Internet, the metaverse faces far more security challenges due to its large scale, distributed, and decentralized nature. While traditional third-party security solutions remain certain limitations such as scalability and Single Point of Failure (SPoF), numerous wearable Augmented/Virtual Reality (AR/VR) devices with increasingly computational capacity can contribute underused resource to protect the metaverse. Realizing the potential of Collaborative Intrusion Detection System (CIDS) in the metaverse context, we propose MetaCIDS, a blockchain-based Federated Learning (FL) framework that allows metaverse users to: (i) collaboratively train an adaptable CIDS model based on their collected local data with privacy protection; (ii) utilize such the FL model to detect metaverse intrusion using the locally observed network traffic; (iii) submit verifiable intrusion alerts through blockchain transactions to obtain token-based reward. Security analysis shows that MetaCIDS can tolerate up to 33\% malicious trainers during the training of FL models, while the verifiability of alerts offer resistance to Distributed Denial of Service (DDoS) attacks. Besides, experimental results illustrate the efficiency and feasibility of MetaCIDS.

This paper presents FBA-SDN, a novel Stellar Consensus Protocol (SCP)-based Federated Byzantine Agreement System (FBAS) approach to trustworthy Collaborative Intrusion Detection (CIDS) in Software-Defined Network (SDN) environments. The proposed approach employs the robustness of Byzantine Fault Tolerance (BFT) consensus mechanisms and the decentralized nature of blockchain ledgers to coordinate the Intrusion Detection System (IDS) operation securely. The federated architecture adopted in FBA-SDN facilitates collaborative analysis of low-confidence alert data, reaching system-wide consensus on potential intrusions. Additionally, the Quorum-based nature of the approach reduces the risk of a single point of failure (SPoF) while simultaneously improving upon the scalability offered by existing blockchain-based approaches. Through simulation, we demonstrate promising results concerning the efficacy of reaching rapid and reliable consensus on both binary and multi-class simulated intrusion data compared with the existing approaches.

Even with the rise of cyberattacks on high-value systems, we still do not see widespread adoption of intrusion-tolerant replication protocols, despite their long history in the research community and potential to support the needed resiliency. A key barrier is that deploying and managing intrusion-tolerant systems in practice requires substantial investment in additional physical infrastructure, as well as specialized technical expertise. In this work, we address this gap by designing a hybrid management model: while the system operator manages their application, a service provider hosts and manages the intrusion-tolerant replication service using cloud infrastructure. We develop the protocols to support this system architecture, without revealing application state, algorithms, or client information to the cloud provider, even when application servers are compromised. We implement and evaluate our approach in the context of an industrial control system and show that it meets the system s performance and resilience requirements.

Container-based virtualization has gained momentum over the past few years thanks to its lightweight nature and support for agility. However, its appealing features come at the price of a reduced isolation level compared to the traditional host-based virtualization techniques, exposing workloads to various faults, such as co-residency attacks like container escape. In this work, we propose to leverage the automated management capabilities of containerized environments to derive a Fault and Intrusion Tolerance (FIT) framework based on error detection-recovery and fault treatment. Namely, we aim at deriving a specification-based error detection mechanism at the host level to systematically and formally capture security state errors indicating breaches potentially caused by malicious containers. Although the paper focuses on security side use cases, results are logically extendable to accidental faults. Our aim is to immunize the target environments against accidental and malicious faults and preserve their core dependability and security properties.

In the face of a large number of network attacks, intrusion detection system can issue early warning, indicating the emergence of network attacks. In order to improve the traditional machine learning network intrusion detection model to identify the behavior of network attacks, improve the detection accuracy and accuracy. Convolutional neural network is used to construct intrusion detection model, which has better ability to solve complex problems and better adaptability of algorithm. In order to solve the problems such as dimension explosion caused by input data, the albino PCA algorithm is used to extract data features and reduce data dimensions. For the common problem of convolutional neural networks in intrusion detection such as overfitting, Dropout layers are added before and after the fully connected layer of CNN, and Sigmoid is selected as the intrusion classification prediction function. This reduces the overfitting, improves the robustness of the intrusion detection model, and enhances the fault tolerance and generalization ability of the model to improve the accuracy of the intrusion detection model. The effectiveness of the proposed method in intrusion detection is verified by comparison and analysis of numerical examples.

The open and shared environment makes it unavoidable to face data attacks in the context of the energy internet. Tolerance to data intrusion is of utmost importance for the security and stability of the energy internet. Existing methods for data intrusion tolerance suffer from insufficient dynamic adaptability and challenges in determining tolerance levels. To address these issues, this paper introduces a data intrusion tolerance model based on game theory. A Nash equilibrium is established by analyzing the gains and losses of both attackers and defenders through game theory. Finally, the simulation results conducted on the IEEE 14-bus node system illustrate that the model we propose offers guidance for decision-making within the energy internet, enabling the utilization of game theory to determine optimal intrusion tolerance strategies.

The enhancement of big data security in cloud computing has become inevitable dues to factors such as the volume, velocity, veracity, Value, and velocity of the big data. These enhancements of big data and cloud technologies have computing enabled a wide range of vulnerabilities in applications in organizational business environments leading to various attacks such as denial-of-service attacks, injection attacks, and Phishing among others. Deploying big data in cloud computing environments is a rapidly growing technology that significantly impacts organizations and provides benefits such as demand-driven access to computational services, a distorted version of infinite computing capacity, and assistance with demand-driven scaling up, scaling down, and scaling out. To secure cloud computing for big data processing, a variety of encryption techniques such as RSA, and AES can be applied. However, there are several vulnerabilities during processing. The paper aims to explore the enhancement of big data security in cloud computing using the RSA algorithm to improve the deployment and processing of the variety, volume, veracity, velocity and value of the data utilizing RSA encryptions. The novelty contribution of the paper is threefold: First, explore the current challenges and vulnerabilities in securing big data in cloud computing and how the RSA algorithm can be used to address them. Secondly, we implement the RSA algorithm in a cloud computing environment using the AWS cloud platform to secure big data to improve the performance and scalability of the RSA algorithm for big data security in cloud computing. We compare the RSA algorithm to other cryptographic algorithms in terms of its ability to enhance big data security in cloud computing. Finally, we recommend control mechanisms to improve security in the cloud platform. The results show that the RSA algorithm can be used to improve Cloud Security in a network environment.

The surveillance factor impacting the Internet-of-Things (IoT) conceptual framework has recently received significant attention from the research community. To do this, a number of surveys covering a variety of IoT-centric topics, such as intrusion detection systems, threat modeling, as well as emerging technologies, were suggested. Stability is not a problem that can be handled separately. Each layer of the IoT solutions must be designed and built with security in mind. IoT security goes beyond safeguarding the network as well as data to include attacks that could be directed at human health or even life. We discuss the IoT s security challenges in this study. We start by going over some fundamental security ideas and IoT security requirements. Following that, we look at IoT market statistics and IoT security statistics to see where it is all headed and how to make your situation better by implementing appropriate security measures.

Cloud computing has turned into an important technology of our time. It has drawn attention due to its, availability, dynamicity, elasticity and pay as per use pricing mechanism this made multiple organizations to shift onto the cloud platform. It leverages the cloud to reduce administrative and backup overhead. Cloud computing offers a lot of versatility. Quantum technology, on the other hand, advances at a breakneck pace. Experts anticipate a positive outcome and predict that within the next decade, powerful quantum computers will be available. This has and will have a substantial impact on various sciences streams such as cryptography, medical research, and much more. Sourcing applications for business and informational data to the cloud, presents privacy and security concerns, which have become crucial in cloud installation and services adoption. To address the current security weaknesses, researchers and impacted organizations have offered several security techniques in the literature. The literature also gives a thorough examination of cloud computing security and privacy concerns.

IoT shares data with other things, such as applications, networked devices, or industrial equipment. With a large-scale complex architecture de-sign composed of numerous ‘things’, the scalability and reliability of various models stand out. When these advantages are vulnerable to security, constant problems occur continuously. Since IoT devices are provided with services closely to users, it can be seen that there are many users with various hacking methods and environments vulnerable to hacking.

Internet of Things (IoT) is encroaching in every aspect of our lives. The exponential increase in connected devices has massively increased the attack surface in IoT. The unprotected IoT devices are not only the target for attackers but also used as attack generating elements. The Distributed Denial of Service (DDoS) attacks generated using the geographically distributed unprotected IoT devices as botnet pose a serious threat to IoT. The large-scale DDoS attacks may arise through multiple low-rate DDoS attacks from geographically distributed, compromised IoT devices. This kind of DDoS attacks are difficult to detect with the existing security mechanisms because of the large-scale distributed nature of IoT. The proposed method provides solution to this problem using Fog computing containing fog nodes which are closer to edge IoT devices. The distributed fog nodes detects the low-rate DDoS attacks from IoT devices before it leads to largescale DDoS attack. The effectiveness analysis of the proposed method proves that the real time detection is practical. The experimental results depicts that the lowrate DDoS attacks are detected at faster rate in fog nodes, hence the large-scale DDoS attacks are detected at early stage to protect from massive attack.

Different contemporary organisations are using cloud computing application in business operation activities to gain competitive advantages over other competitors. It also helps in promoting flexibility of the business operation activities. Cloud computing involves delivery of different computer resources to data centres over the internet services. Different kinds of delivered computer resources include data storage, servers, database, analytics, software, networking, and other types of data applications etc. In this present era of data breaches, cloud computing ensures security protocols to protect different kinds of sensitive transaction data and confidential information. Use of cloud computing ensures that a third party individual does not tamper the data. Use of cloud computing also provides different kinds of competitive advantages to the organisations. Cloud computing also helps in providing efficiency and a platform for innovation for the contemporary organisations. Theoretical frameworks are usedin the literature review section to determine the important roles of cloud computing in effective data and security management in the organisations. It is also justified in the research work that qualitative methodology is suitable for the researcher to meet the developed research objectives. A secondary data analysis approach has been considered by the researcher in this study to carry out the investigation and meet the developed objectives. From the findings, few challenges associated with the cloud computing system have been identified. Proper recommendations are suggested at the end of the study to help future researchers in overcoming the identified associated challenges.

With billions of devices already connected to the network s edge, the Internet of Things (IoT) is shaping the future of pervasive computing. Nonetheless, IoT applications still cannot escape the need for the computing resources available at the fog layer. This becomes challenging since the fog nodes are not necessarily secure nor reliable, which widens even further the IoT threat surface. Moreover, the security risk appetite of heterogeneous IoT applications in different domains or deploy-ment contexts should not be assessed similarly. To respond to this challenge, this paper proposes a new approach to optimize the allocation of secure and reliable fog computing resources among IoT applications with varying security risk level. First, the security and reliability levels of fog nodes are quantitatively evaluated, and a security risk assessment methodology is defined for IoT services. Then, an online, incentive-compatible mechanism is designed to allocate secure fog resources to high-risk IoT offloading requests. Compared to the offline Vickrey auction, the proposed mechanism is computationally efficient and yields an acceptable approximation of the social welfare of IoT devices, allowing to attenuate security risk within the edge network.

As a result of this new computer design, edge computing can process data rapidly and effectively near to the source, avoiding network resource and latency constraints. By shifting computing power to the network edge, edge computing decreases the load on cloud services centers while also reducing the time required for users to input data. Edge computing advantages for data-intensive services, in particular, could be obscured if access latency becomes a bottleneck. Edge computing raises a number of challenges, such as security concerns, data incompleteness, and a hefty up-front and ongoing expense. There is now a shift in the worldwide mobile communications sector toward 5G technology. This unprecedented attention to edge computing has come about because 5G is one of the primary entry technologies for large-scale deployment. Edge computing privacy has been a major concern since the technology’s inception, limiting its adoption and advancement. As the capabilities of edge computing have evolved, so have the security issues that have arisen as a result of these developments, as well as the increasing public demand for privacy protection. The lack of trust amongst IoT devices is exacerbated by the inherent security concerns and assaults that plague IoT edge devices. A cognitive trust management system is proposed to reduce this malicious activity by maintaining the confidence of an appliance \& managing the service level belief \& Quality of Service (QoS). Improved packet delivery ratio and jitter in cognitive trust management systems based on QoS parameters show promise for spotting potentially harmful edge nodes in computing networks at the edge.

The big data platform based on cloud computing realizes the storage, analysis and processing of massive data, and provides users with more efficient, accurate and intelligent Internet services. Combined with the characteristics of college teaching resource sharing platform based on cloud computing mode, the multi-faceted security defense strategy of the platform is studied from security management, security inspection and technical means. In the detection module, the optimization of the support vector machine is realized, the detection period is determined, the DDoS data traffic characteristics are extracted, and the source ID blacklist is established; the triggering of the defense mechanism in the defense module, the construction of the forwarder forwarding queue and the forwarder forwarding capability are realized. Reallocation.

The innovation introduced by connectivity brings about significant changes in the industrial environment leading to the fourth industrial revolution, known as Industry 4.0. However, the integration and connectivity between industrial systems have significantly increased the risks and cyberattack surfaces. Nowadays, Virtualization is added to the security field to provide maximum protection against toxic attacks at minimum costs. Combining paradigms such as Software Defined Networking (SDN), and Network Function Virtualization (NFV) can improve virtualization performance through Openness (unified control of heterogeneous hardware and software resources), Flexibility (remote management and rapid response to changing demands), and Scalability (a faster cycle of innovative services deployment). The present paper proposes a Virtualized Security for Industry 4.0 (ViSI4.0), based on both SDN and Network Security Function Virtualisation (NSFV), to prevent attacks on Cyber-Physical System (CPS). Since industrial devices are limited in memory and processing, vNSFs are deployed as Docker containers. We conducted experiments to evaluate the performances of IIoT applications when using virtualized security services. Results showed that many real-time IIoT applications are still within their latency tolerance range. However, the additional delays introduced by virtualization have an impact on IIoT applications with very strict delays.

This paper addresses the issues of fault tolerance (FT) and intrusion detection (ID) in the Software-defined networking (SDN) environment. We design an integrated model that combines the FT-Manager as an FT mechanism and an ID-Manager, as an ID technique to collaboratively detect and mitigate threats in the SDN. The ID-Manager employs a machine learning (ML) technique to identify anomalous traffic accurately and effectively. Both techniques in the integrated model leverage the controller-switches communication for real-time network statistics collection. While the full implementation of the framework is yet to be realized, experimental evaluations have been conducted to identify the most suitable ML algorithm for ID-Manager to classify network traffic using a benchmarking dataset and various performance metrics. The principal component analysis method was utilized for feature engineering optimization, and the results indicate that the Random Forest (RF) classifier outperforms other algorithms with 99.9\% accuracy, precision, and recall. Based on these findings, the paper recommended RF as the ideal choice for ID design in the integrated model. We also stress the significance and potential benefits of the integrated model to sustain SDN network security and dependability.

With the proliferation of data in Internet-related applications, incidences of cyber security have increased manyfold. Energy management, which is one of the smart city layers, has also been experiencing cyberattacks. Furthermore, the Distributed Energy Resources (DER), which depend on different controllers to provide energy to the main physical smart grid of a smart city, is prone to cyberattacks. The increased cyber-attacks on DER systems are mainly because of its dependency on digital communication and controls as there is an increase in the number of devices owned and controlled by consumers and third parties. This paper analyzes the major cyber security and privacy challenges that might inflict, damage or compromise the DER and related controllers in smart cities. These challenges highlight that the security and privacy on the Internet of Things (IoT), big data, artificial intelligence, and smart grid, which are the building blocks of a smart city, must be addressed in the DER sector. It is observed that the security and privacy challenges in smart cities can be solved through the distributed framework, by identifying and classifying stakeholders, using appropriate model, and by incorporating fault-tolerance techniques.