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.

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.

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.

Cloud computing (CC) is vulnerable to existing information technology attacks, since it extends and utilizes information technology infrastructure, applications and typical operating systems. In this manuscript, an Enhanced capsule generative adversarial network (ECGAN) with blockchain based Proof of authority consensus procedure fostered Intrusion detection (ID) system is proposed for enhancing cyber security in CC. The data are collected via NSL-KDD benchmark dataset. The input data is fed to proposed Z-Score Normalization process to eliminate the redundancy including missing values. The pre-processing output is fed to feature selection. During feature selection, extracting the optimum features on the basis of univariate ensemble feature selection (UEFS). Optimum features basis, the data are classified as normal and anomalous utilizing Enhanced capsule generative adversarial networks. Subsequently, blockchain based Proof of authority (POA) consensus process is proposed for improving the cyber security of the data in cloud computing environment. The proposed ECGAN-BC-POA-IDS method is executed in Python and the performance metrics are calculated. The proposed approach has attained 33.7\%, 25.7\%, 21.4\% improved accuracy, 24.6\%, 35.6\%, 38.9\% lower attack detection time, and 23.8\%, 18.9\%, 15.78\% lower delay than the existing methods, like Artificial Neural Network (ANN) with blockchain framework, Integrated Architecture with Byzantine Fault Tolerance consensus, and Blockchain Random Neural Network (RNN-BC) respectively.

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.

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.

Nowadays, companies, critical infrastructure and governments face cyber attacks every day ranging from simple denial-of-service and password guessing attacks to complex nationstate attack campaigns, so-called advanced persistent threats (APTs). Defenders employ intrusion detection systems (IDSs) among other tools to detect malicious activity and protect network assets. With the evolution of threats, detection techniques have followed with modern systems usually relying on some form of artificial intelligence (AI) or anomaly detection as part of their defense portfolio. While these systems are able to achieve higher accuracy in detecting APT activity, they cannot provide much context about the attack, as the underlying models are often too complex to interpret. This paper presents an approach to explain single predictions (i. e., detected attacks) of any graphbased anomaly detection systems. By systematically modifying the input graph of an anomaly and observing the output, we leverage a variation of permutation importance to identify parts of the graph that are likely responsible for the detected anomaly. Our approach treats the anomaly detection function as a black box and is thus applicable to any whole-graph explanation problems. Our results on two established datasets for APT detection (StreamSpot \& DARPA TC Engagement Three) indicate that our approach can identify nodes that are likely part of the anomaly. We quantify this through our area under baseline (AuB) metric and show how the AuB is higher for anomalous graphs. Further analysis via the Wilcoxon rank-sum test confirms that these results are statistically significant with a p-value of 0.0041\%.

As cyberattacks are rising, Moving Target Defense (MTD) can be a countermeasure to proactively protect a networked system against cyber-attacks. Despite the fact that MTD systems demonstrate security effectiveness against the reconnaissance of Cyber Kill Chain (CKC), a time-based MTD has a limitation when it comes to protecting a system against the next phases of CKC. In this work, we propose a novel hybrid MTD technique, its implementation and evaluation. Our hybrid MTD system is designed on a real SDN testbed and it uses an intrusion detection system (IDS) to provide an additional MTD triggering condition. This in itself presents an extra layer of system protection. Our hybrid MTD technique can enhance security in the response to multi-phased cyber-attacks. The use of the reactive MTD triggering from intrusion detection alert shows that it is effective to thwart the further phase of detected cyber-attacks. We also investigate the performance degradation due to more frequent MTD triggers.This work contributes to (1) proposing an ML-based rule classification model for predicting identified attacks which helps a decision-making process for security enhancement; (2) developing a hybrid-based MTD integrated with a Network Intrusion Detection System (NIDS) with the consideration of performance and security; and (3) assessment of the performance degradation and security effectiveness against potential real attacks (i.e., scanning, dictionary, and SQL injection attack) in a physical testbed.

Cyber attack scenario reconstruction plays a crucial role in understanding and mitigating security breaches. In this paper, we propose a novel framework that leverages Natural Language Processing (NLP), specifically Named Entity Recognition (NER), and semantic similarity techniques to reconstruct cyber attack scenarios. By analyzing Intrusion Detection alerts, our offline approach identifies relevant entities, detects relationships between them, and measures semantic similarity to uncover hidden patterns and connections. We demonstrate the effectiveness of our framework through experimental evaluations using a public dataset. The results highlight the potential of NLP-based approaches in cyber attack scenario reconstruction.

A three-party evolutionary game model is constructed by combining the cyber deception, the defender (intrusion detection system), and the attacker. The attackers choose attack strategies to gain greater benefits. The cyber deception can induce attackers to attack fake vulnerabilities, so as capture and analyze the attackers intentions. The defenders use the captured attacker information to adjust their defense strategies and improve detection of attacks. Using cyber deception to enhance the defender choice of strategy, reduce attacker s profit, enable defender to play their own superior strategy, reduce node resource overhead, and prolong network survival time. Through the capture and feature extraction of attacker s attack information, the attack feature database of intrusion detection system is improved, and the detection probability of the attack by the defender is increased. According to the simulation results, the cyber deception can provide the defender with the attacker s attack information in the process of attack and defense, increase the probability of the defender s successful defense, speed up the convergence speed of the optimal defense strategy, and reduce the convergence speed of the attacker s optimal strategy. It is proved that the cyber deception as a third-party participant can effectively help the defender to protect the security of the network.

Cybersecurity is an increasingly critical aspect of modern society, with cyber attacks becoming more sophisticated and frequent. Artificial intelligence (AI) and neural network models have emerged as promising tools for improving cyber defense. This paper explores the potential of AI and neural network models in cybersecurity, focusing on their applications in intrusion detection, malware detection, and vulnerability analysis. Intruder detection, or "intrusion detection," is the process of identifying Invasion of Privacy to a computer system. AI-based security systems that can spot intrusions (IDS) use AI-powered packet-level network traffic analysis and intrusion detection patterns to signify an assault. Neural network models can also be used to improve IDS accuracy by modeling the behavior of legitimate users and detecting anomalies. Malware detection involves identifying malicious software on a computer system. AI-based malware machine-learning algorithms are used by detecting systems to assess the behavior of software and recognize patterns that indicate malicious activity. Neural network models can also serve to hone the precision of malware identification by modeling the behavior of known malware and identifying new variants. Vulnerability analysis involves identifying weaknesses in a computer system that could be exploited by attackers. AI-based vulnerability analysis systems use machine learning algorithms to analyze system configurations and identify potential vulnerabilities. Neural network models can also be used to improve the accuracy of vulnerability analysis by modeling the behavior of known vulnerabilities and identifying new ones. Overall, AI and neural network models have significant potential in cybersecurity. By improving intrusion detection, malware detection, and vulnerability analysis, they can help organizations better defend against cyber attacks. However, these technologies also present challenges, including a lack of understanding of the importance of data in machine learning and the potential for attackers to use AI themselves. As such, careful consideration is necessary when implementing AI and neural network models in cybersecurity.

In this research, we evaluate the effectiveness of different MTD techniques on the transformer-based cyber anomaly detection models trained on the KDD Cup’99 Dataset, a publicly available dataset commonly used for evaluating intrusion detection systems. We explore the trade-offs between security and performance when using MTD techniques for cyber anomaly detection and investigate how MTD techniques can be combined with other cybersecurity techniques to improve the overall security of the system. We evaluate their performance using standard metrics such as accuracy and FI score, as well as measures of robustness against adversarial attacks. Our results show that MTD techniques can significantly improve the security of the anomaly detection model, with some techniques being more effective than others depending on the model architecture. We also find that there are trade-offs between security and performance, with some MTD techniques leading to a reduction in model accuracy or an increase in computation time. However, we demonstrate that these tradeoffs can be mitigated by optimizing the MTD parameters for the specific model architecture.

These days, safety measures can t be neglected. In a world where digital risks are becoming more prevalent, efficient security has become an essential aspect of any system or business. Protecting valuables now requires a defensive strategy with several layers. Security systems play an important role in today s modern, industrialised society. The security system is primarily intended to address the need for the protection of hard-earned treasures (jewels). Unlike the current method, which uses physical locks that are readily falsified, this system uses Bluetooth and RFID tags in conjunction with digital (electronic) code locks to unlock the door automatically once the series of authentications is validated and emits alarm noises when any discrepancy happens. The ability of subsequent layers of defense to prevent intrusion is unaffected by the failure of an earlier one to provide detection. In this effort, we use IoT to design and build a fully automated security system that will operate with no more human intervention when it is put into place. In addition, the system s overall cost of adoption is far lower than that of any other consumer security solution now on the market.

Intrusion detection is important in the defense in depth network security framework and a hot topic in computer network security in recent years. In this paper, an effective method for anomaly intrusion detection with low overhead and high efficiency is presented and applied to monitor the abnormal behavior of processes. The method is based on rough set theory and capable of extracting a set of detection rules with the minimum size to form a normal behavior model from the record of system call sequences generated during the normal execution of a process. Based on the network security knowledge base system, this paper proposes an intrusion detection model based on the network security knowledge base system, including data filtering, attack attempt analysis and situation assessment engine. In this model, evolutionary self organizing mapping is used to discover multi - target attacks of the same origin; The association rules obtained by time series analysis method are used to correlate online alarm events to identify complex attacks scattered in time; Finally, the corresponding evaluation indexes and corresponding quantitative evaluation methods are given for host level and LAN system level threats respectively. Compared with the existing IDS, this model has a more complete structure, richer knowledge available, and can more easily find cooperative attacks and effectively reduce the false positive rate.

IBMD(Intelligent Behavior-Based Malware Detection) aims to detect and mitigate malicious activities in cloud computing environments by analyzing the behavior of cloud resources, such as virtual machines, containers, and applications.The system uses different machine learning methods like deep learning and artificial neural networks, to analyze the behavior of cloud resources and detect anomalies that may indicate malicious activity. The IBMD system can also monitor and accumulate the data from various resources, such as network traffic and system logs, to provide a comprehensive view of the behavior of cloud resources. IBMD is designed to operate in a cloud computing environment, taking advantage of the scalability and flexibility of the cloud to detect malware and respond to security incidents. The system can also be integrated with existing security tools and services, such as firewalls and intrusion detection systems, to provide a comprehensive security solution for cloud computing environments.

With the rapid development of science and technology, information security issues have been attracting more attention. According to statistics, tens of millions of computers around the world are infected by malicious software (Malware) every year, causing losses of up to several USD billion. Malware uses various methods to invade computer systems, including viruses, worms, Trojan horses, and others and exploit network vulnerabilities for intrusion. Most intrusion detection approaches employ behavioral analysis techniques to analyze malware threats with packet collection and filtering, feature engineering, and attribute comparison. These approaches are difficult to differentiate malicious traffic from legitimate traffic. Malware detection and classification are conducted with deep learning and graph neural networks (GNNs) to learn the characteristics of malware. In this study, a GNN-based model is proposed for malware detection and classification on a renewable energy management platform. It uses GNN to analyze malware with Cuckoo Sandbox malware records for malware detection and classification. To evaluate the effectiveness of the GNN-based model, the CIC-AndMal2017 dataset is used to examine its accuracy, precision, recall, and ROC curve. Experimental results show that the GNN-based model can reach better results.

With the development of network technologies, network intrusion has become increasing complex which makes the intrusion detection challenging. Traditional intrusion detection algorithms detect intrusion traffic through intrusion traffic characteristics or machine learning. These methods are inefficient due to the dependence of manual work. Therefore, in order to improve the efficiency and the accuracy, we propose an intrusion detection method based on deep learning. We integrate the Transformer and LSTM module with intrusion detection model to automatically detect network intrusion. The Transformer and LSTM can capture the temporal information of the traffic data which benefits to distinguish the abnormal data from normal data. We conduct experiments on the publicly available NSL-KDD dataset to evaluate the performance of our proposed model. The experimental results show that the proposed model outperforms other deep learning based models.

In the ever-evolving landscape of cybersecurity threats, Intrusion detection systems are critical in protecting network and server infrastructure in the ever-changing spectrum ofcybersecurity threats. This research introduces a hybrid detection approach that uses deep learning techniques to improve intrusion detection accuracy and efficiency. The proposed prototype combines the strength of the XGBoost and MaxPooling1D algorithms within an ensemble model, resulting in a stable and effective solution. Through the fusion of these methodologies, the hybrid detection system achieves superior performance in identifying and mitigating various types of intrusions. This paper provides an overview of the prototype s architecture, discusses the benefits of using deep learning in intrusion detection, and presents experimental results showcasing the system s efficacy.

Network intrusion detection is a crucial task in ensuring the security and reliability of computer networks. In recent years, machine learning algorithms have shown promising results in identifying anomalous activities indicative of network intrusions. In the context of intrusion detection systems, novelty detection often receives limited attention within machine learning communities. This oversight can be attributed to the historical emphasis on optimizing performance metrics using established datasets, which may not adequately represent the evolving landscape of cyber threats. This research aims to compare four widely used novelty detection algorithms for network intrusion detection, namely SGDOneClassSVM, LocalOutlierDetection, EllipticalEnvelope Covariance, and Isolation Forest. Our experiments with the UNSW-NB15 dataset show that Isolation Forest was the best-performing algorithm with an F1-score of 0.723. The result shows that network-based intrusion detection systems are still challenging for novelty detection algorithms.

In cybersecurity, Intrusion Detection Systems (IDS) protect against emerging cyber threats. Combining signature-based and anomaly-based detection methods may improve IDS accuracy and reduce false positives. This research analyzes hybrid intrusion detection systems signature-based components performance and limitations. The paper begins with a detailed history of signature-based detection methods responding to changing threat situations. This research analyzes signature databases to determine their capacity to identify and guard against current threats and cover known vulnerabilities. The paper also examines the intricate relationship between signature-based detection and anomalybased techniques in hybrid IDS systems. This investigation examines how these two methodologies work together to uncover old and new attack strategies, focusing on zero-day vulnerabilities and polymorphic malware. A diverse dataset of network traffic and attack scenarios is used to test. Detection, false positives, and response times assess signature-based components. Comparative examinations investigate how signature-based detection affects system accuracy and efficiency. This research illuminates the role of signature-based aspects in hybrid intrusion detection systems. This study recommends integrating signature-based detection techniques with anomaly-based methods to improve hybrid intrusion detection systems (IDS) at recognizing and mitigating various cyber threats.

The network intrusion detection system capably safeguards our network environment from attacks. Yet, the relentless surge in bandwidth and inherent constraints within these systems often hinder detection, particularly in confrontations with substantial traffic volume. Hence, this paper introduces the IP-filtered multi-channel convolutional neural networks (IP-MCCLSTM), which filters traffic by IP, curtails system loading, and notably enhances detection efficiency. IP-MCCLSTM outperforms comparison methods in tests using the 2017CICIDS data set. The result shows IPMCCLSTM obtains 98.9\% accuracy and 99.7\% Macro-Recall rate, showcasing its potential as an avant-garde solution in intrusion detection.

Due to its adaptability and pay-per-use services, cloud computing has grown in popularity among businesses, but security and privacy issues are still very much present. Intruders can exploit vulnerabilities in the open and dispersed nature of cloud environments, leading to attacks that can damage entire projects within a short period of time. To address this issue, organizations need to implement effective intrusion detection systems (IDS) that can detect and alert administrators of any suspicious activities. There are three widely used methods for IDS: signature-based detection, anomaly-based detection, and hybrid detection. Hybrid detection, which combines the strengths of signature-based and anomaly-based detection, has been shown to produce superior results. IDS can be categorized into host- based IDS (HIDS), network-based IDS (NIDS), hypervisor-based IDS, and distributed IDS (DIDS), each with their own unique characteristics and benefits. The CICIDS2017 dataset provides a diverse set of attacks and benign traffic for researchers and practitioners to develop and evaluate IDS systems. Overall, putting in place a strong intrusion detection system is critical for maintaining the security and privacy of cloud-based projects, as well as ensuring their availability.

The advancement of information technology is closely associated with various aspects of daily life, providing people with services for a comfortable life. As the network infrastructure expands to accommodate these services, it inevitably creates several vulnerable points susceptible to cyberattacks. Researchers have gained significant momentum by focusing on deep learning-based network intrusion detection. The development of a robust network intrusion detection system based on deep learning necessitates a substantial volume of data. Traditionally, collected data for centralized learning were transmitted to a central server for training the model. However, this approach causes concern regarding the potential compromise of the personal information contained within the raw data, thereby precipitating legal implications for vendors. Therefore, this paper proposes an ImprovedFedAvg, which enhances the existing FedAvg algorithm for network intrusion detection model. This method uses the full advantages of federated learning for data privacy preservation and significantly reduces the transmission of model weights while improving the performance of the model.

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 use of computers and the internet has spread rapidly over the course of the past few decades. Every day, more and more peopleare coming to rely heavily on the internet. When it comes to the field of information security, the subject of security is one that is becoming an increasingly important focus. It is vital to design a powerful intrusion detection system in order to prevent computer hackers and other intruders from effectively getting into computer networks or systems. This can be accomplished by: (IDS). The danger and attack detection capabilities of the computer system are built into the intrusion detection system. Abuse has occurred and can be used to identify invasions when there is a deviation between a preset pattern of intrusion and an observedpattern of intrusion. An intrusion detection system (IDS) is a piece of hardware (or software) that is used to generate reports for a Management Station as well as monitor network and/or system activities for unethical behaviour or policy violations. In the current study, an approach known as machine learning is suggested as a possible paradigm for the development of a network intrusion detection system. The results of the experiment show that the strategy that was suggested improves the capability of intrusion detection.