An intrusion detection system (IDS) is a crucial software or hardware application that employs security mechanisms to identify suspicious activity in a system or network. According to the detection technique, IDS is divided into two, namely signature-based and anomaly-based. Signature-based is said to be incapable of handling zero-day attacks, while anomaly-based is able to handle it. Machine learning techniques play a vital role in the development of IDS. There are differences of opinion regarding the most optimal algorithm for IDS classification in several previous studies, such as Random Forest, J48, and AdaBoost. Therefore, this study aims to evaluate the performance of the three algorithm models, using the NSL-KDD and UNSW-NB15 datasets used in previous studies. Empirical results demonstrate that utilizing AdaBoost+J48 with NSL-KDD achieves an accuracy of 99.86\%, along with precision, recall, and f1-score rates of 99.9\%. These results surpass previous studies using AdaBoost+Random Tree, with an accuracy of 98.45\%. Furthermore, this research explores the effectiveness of anomaly-based systems in dealing with zero-day attacks. Remarkably, the results show that anomaly-based systems perform admirably in such scenarios. For instance, employing Random Forest with the UNSW-NB15 dataset yielded the highest performance, with an accuracy rating of 99.81\%.

An Intrusion detection system (IDS) plays a role in network intrusion detection through network data analysis, and high detection accuracy, precision, and recall are required to detect intrusions. Also, various techniques such as expert systems, data mining, and state transition analysis are used for network data analysis. The paper compares the detection effects of the two IDS methods using data mining. The first technique is a support vector machine (SVM), a machine learning algorithm; the second is a deep neural network (DNN), one of the artificial neural network models. The accuracy, precision, and recall were calculated and compared using NSL-KDD training and validation data, which is widely used in intrusion detection to compare the detection effects of the two techniques. DNN shows slightly higher accuracy than the SVM model. The risk of recognizing an actual intrusion as normal data is much greater than the risk of considering normal data as an intrusion, so DNN proves to be much more effective in intrusion detection than SVM.

The number of Internet of Things (IoT) devices being deployed into networks is growing at a phenomenal pace, which makes IoT networks more vulnerable in the wireless medium. Advanced Persistent Threat (APT) is malicious to most of the network facilities and the available attack data for training the machine learning-based Intrusion Detection System (IDS) is limited when compared to the normal traffic. Therefore, it is quite challenging to enhance the detection performance in order to mitigate the influence of APT. Therefore, Prior Knowledge Input (PKI) models are proposed and tested using the SCVIC-APT2021 dataset. To obtain prior knowledge, the proposed PKI model pre-classifies the original dataset with unsupervised clustering method. Then, the obtained prior knowledge is incorporated into the supervised model to decrease training complexity and assist the supervised model in determining the optimal mapping between the raw data and true labels. The experimental findings indicate that the PKI model outperforms the supervised baseline, with the best macro average F1-score of 81.37\%, which is 10.47\% higher than the baseline.

With the proliferation of Low Earth Orbit (LEO) spacecraft constellations, comes the rise of space-based wireless cognitive communications systems (CCS) and the need to safeguard and protect data against potential hostiles to maintain widespread communications for enabling science, military and commercial services. For example, known adversaries are using advanced persistent threats (APT) or highly progressive intrusion mechanisms to target high priority wireless space communication systems. Specialized threats continue to evolve with the advent of machine learning and artificial intelligence, where computer systems inherently can identify system vulnerabilities expeditiously over naive human threat actors due to increased processing resources and unbiased pattern recognition. This paper presents a disruptive abuse case for an APT-attack on such a CCS and describes a trade-off analysis that was performed to evaluate a variety of machine learning techniques that could aid in the rapid detection and mitigation of an APT-attack. The trade results indicate that with the employment of neural networks, the CCS s resiliency would increase its operational functionality, and therefore, on-demand communication services reliability would increase. Further, modelling, simulation, and analysis (MS\&A) was achieved using the Knowledge Discovery and Data Mining (KDD) Cup 1999 data set as a means to validate a subset of the trade study results against Training Time and Number of Parameters selection criteria. Training and cross-validation learning curves were computed to model the learning performance over time to yield a reasonable conclusion about the application of neural networks.

In this fast growing technology and tight integration of physical devices in conventional networks, the resource management and adaptive scalability is a problematic undertaking particularly when it comes to network security measures. Current work focuses on software defined network (SDN) and network function virtualization (NFV) based security solution to address problems in network and security management. However, deployment, configuration and implementation of SDN/NFVbased security solution remains a real challenge. To overcome this research challenge, this paper presents the implementation of SDN-NFVs based network security solution. The proposed methodology is based on using open network operating system (ONOS) SDN Controller with Zodiac FX Openflow switches and virtual network functions (VNF). VNF comprises of virtual security functions (VSF) which includes firewall, intrusion prevention system (IPS) and intrusion detection system (IDS). One of the main contributions of this research is the implementation of security solution of an enterprise, utilizing SDN-NFV platform and commodity hardware. We demonstrate the successful implementation, configuration and deployment of the proposed NFVbased network security solution for an enterprise.

A smart university is supposed to be a safe university. At this moment we observe multiple cameras in different locations in the Hall University and rooms to detect suspicious behavior such as violation, larceny or persons in a state of alcohol or drug intoxication. Samples of the video footage is monitored 24/7 by operators in control rooms. Currently the recorded videos are visual assessed after a suspicious event has occurred. There is a requirement for realtime surveillance with smart cameras which can detect, track and analyze suspicious behavior over place and time. The expanding number of cameras requires an enormous measure of observing operators. This paper proposes a distributed intelligent surveillance system based on smart cameras. We seek to improve the Quality of Experience QoE operator side or QoEvideo surveillance expressed in function of i- resource availability constraints, ii- false detection of suspicious behavior, iii- define an optimal perimeter for intrusion detection (subset of cameras, network parameters required . . . ).

Neural Network Security - With the continuous development of network technology and the continuous expansion of network scale, the security of the network has suffered more threats, and the attacks faced are becoming more and more extensive. The frequent occurrence of network security incidents has caused huge losses, facing more and more severe situation, it is necessary to adopt various network security technologies to solve the problem. In network security, the most commonly used technology is firewall. The firewall has a certain blocking effect on attacks from outside the network, but it has a weak defense effect on the attacks in the internal network, and it is easy to be bypassed. Intrusion detection technology can detect both internal and external network attacks. Responses are generated before the intrusion behavior occurs, and alarm information is issued for timely and effective processing. In recent years, China s campus security incidents are still happening, seriously threatening the lives of students and disrupting the normal teaching order of schools. At present, there are still many loopholes in campus security operations. Campus security management system has become an important task in campus security construction. On this basis, relevant personnel are required to analyze the existing problems of campus safety and the needs of the safety management system, and find the main technology of a more advanced intelligent safety management system.

Neural Network Resiliency - With the proliferation of Low Earth Orbit (LEO) spacecraft constellations, comes the rise of space-based wireless cognitive communications systems (CCS) and the need to safeguard and protect data against potential hostiles to maintain widespread communications for enabling science, military and commercial services. For example, known adversaries are using advanced persistent threats (APT) or highly progressive intrusion mechanisms to target high priority wireless space communication systems. Specialized threats continue to evolve with the advent of machine learning and artificial intelligence, where computer systems inherently can identify system vulnerabilities expeditiously over naive human threat actors due to increased processing resources and unbiased pattern recognition. This paper presents a disruptive abuse case for an APT-attack on such a CCS and describes a trade-off analysis that was performed to evaluate a variety of machine learning techniques that could aid in the rapid detection and mitigation of an APT-attack. The trade results indicate that with the employment of neural networks, the CCS s resiliency would increase its operational functionality, and therefore, on-demand communication services reliability would increase. Further, modelling, simulation, and analysis (MS\&A) was achieved using the Knowledge Discovery and Data Mining (KDD) Cup 1999 data set as a means to validate a subset of the trade study results against Training Time and Number of Parameters selection criteria. Training and cross-validation learning curves were computed to model the learning performance over time to yield a reasonable conclusion about the application of neural networks.

Network Intrusion Detection - This paper proposes a CNN-BiLS TM intrusion detection model for complex system networks. The model performs data over-sampling on the unbalanced data set, which reduces the gap in the amount of category data. It is based on the integration, cooperation, and selectivity of methods and mechanisms in the intrusion detection system, so as to achieve the idea of optimization. In the intrusion detection system, an intrusion detection system based on a variety of detection methods and technologies is proposed, and an integrated, cooperative, and selective overall structure is established. It will be based on distributed intrusion detection and feature engine analysis of intrusion detection, efficiency an increase of 6.7\%.

Network Intrusion Detection - Aiming at the problems of low detection accuracy, high false detection rate and high missed detection rate of traditional Intelligent Substation (I-S) secondary system network Intrusion Detection (I-D) methods, a semantic enhanced network I-D method for I-S secondary system is proposed. First of all, through the analysis of the secondary system network of I-S and the existing security risks, the information network security protection architecture is built based on network I-D. Then, the overall structure of I-S secondary network I-D is constructed by integrating CNN and BiLSTM. Finally, the semantic analysis of Latent Dirichlet Allocation (LDA) is introduced to enhance the network I-D model, which greatly improves the detection accuracy. The proposed method is compared with the other two methods under the same conditions through simulation experiments. The results show that the detection accuracy of the proposed method is the highest (95.02\%) in the face of 10 different types of attack traffic, and the false detection rate and missed detection rate are the lowest (1.3\% and 3.8\% respectively). The algorithm performance is better than the other three comparison algorithms.

Network Intrusion Detection - Network intrusion detection technology has been a popular application technology for current network security, but the existing network intrusion detection technology in the application process, there are problems such as low detection efficiency, low detection accuracy and other poor detection performance. To solve the above problems, a new treatment combining artificial intelligence with network intrusion detection is proposed. Artificial intelligence-based network intrusion detection technology refers to the application of artificial intelligence techniques, such as: neural networks, neural algorithms, etc., to network intrusion detection, and the application of these artificial intelligence techniques makes the automatic detection of network intrusion detection models possible.

Network Intrusion Detection - 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.

Nearest Neighbor Search - Network security is one of the main challenges faced by network administrators and owners, especially with the increasing numbers and types of attacks. This rapid increase results in a need to develop different protection techniques and methods. Network Intrusion Detection Systems (NIDS) are a method to detect and analyze network traffic to identify attacks and notify network administrators. Recently, machine learning (ML) techniques have been extensively applied in developing detection systems. Due to the high complexity of data exchanged over the networks, applying ML techniques will negatively impact system performance as many features need to be analyzed. To select the most relevant features subset from the input data, a feature selection technique is used, which results in enhancing the overall performance of the NIDS. In this paper, we propose a wrapper approach as a feature selection based on a Chaotic Crow Search Algorithm (CCSA) for anomaly network intrusion detection systems. Experiments were conducted on the LITNET2020 dataset. To the best of our knowledge, our proposed method can be considered the first selection algorithm applied on this dataset based on swarm intelligence optimization to find a special subset of features for binary and multiclass classifications that optimizes the performance for all classes at the same time.The model was evaluated using several ML classifiers namely, Knearest neighbors (KNN), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Multi-layer perceptron (MLP), and Long Short-Term Memory (LSTM). The results proved that the proposed algorithm is more efficient in improving the performance of NIDS in terms of accuracy, detection rate, precision, F-score, specificity, and false alarm rate, outperforming state-of-the-art feature selection techniques recently proposed in the literature.

Moving Target Defense - The use of traditional defense mechanisms or intrusion detection systems presents a disadvantage for defenders against attackers since these mechanisms are essentially reactive. Moving target defense (MTD) has emerged as a proactive defense mechanism to reduce this disadvantage by randomly and continuously changing the attack surface of a system to confuse attackers. Although significant progress has been made recently in analyzing the security effectiveness of MTD mechanisms, critical gaps still exist, especially in maximizing security levels and estimating network reconfiguration speed for given attack power. In this paper, we propose a set of Petri Net models and use them to perform a comprehensive evaluation regarding key security metrics of Software-Defined Network (SDNs) based systems adopting a time-based MTD mechanism. We evaluate two use-case scenarios considering two different types of attacks to demonstrate the feasibility and applicability of our models. Our analyses showed that a time-based MTD mechanism could reduce the attackers’ speed by at least 78\% compared to a system without MTD. Also, in the best-case scenario, it can reduce the attack success probability by about ten times.

MANET Security - The current stady is confined in proposing a reputation based approach for detecting malicious activity where past activities of each node is recorded for future reference. It has been regarded that the Mobile ad-hoc network commonly called as (MANET) is stated as the critical wireless network on the mobile devices using self related assets. Security considered as the main challenge in MANET. Many existing work has done on the basis of detecting attacks by using various approaches like Intrusion Detection, Bait detection, Cooperative malicious detection and so on. In this paper some approaches for identifying malicious nodes has been discussed. But this Reputation based approach mainly focuses on sleuthing the critcal nodes on the trusted path than the shortest path. Each node will record the activity of its own like data received from and Transferred to information. As soon as a node update its activity it is verified and a trust factor is assigned. By comparing the assigned trust factor a list of suspicious or malicious node is created..

MANET Attack Detection - The current stady is confined in proposing a reputation based approach for detecting malicious activity where past activities of each node is recorded for future reference. It has been regarded that the Mobile ad-hoc network commonly called as (MANET) is stated as the critical wireless network on the mobile devices using self related assets. Security considered as the main challenge in MANET. Many existing work has done on the basis of detecting attacks by using various approaches like Intrusion Detection, Bait detection, Cooperative malicious detection and so on. In this paper some approaches for identifying malicious nodes has been discussed. But this Reputation based approach mainly focuses on sleuthing the critcal nodes on the trusted path than the shortest path. Each node will record the activity of its own like data received from and Transferred to information. As soon as a node update its activity it is verified and a trust factor is assigned. By comparing the assigned trust factor a list of suspicious or malicious node is created.

MANET Attack Prevention - Wireless ad hoc networks are characterized by dynamic topology and high node mobility. Network attacks on wireless ad hoc networks can significantly reduce performance metrics, such as the packet delivery ratio from the source to the destination node, overhead, throughput, etc. The article presents an experimental study of an intrusion detection system prototype in mobile ad hoc networks based on machine learning. The experiment is carried out in a MANET segment of 50 nodes, the detection and prevention of DDoS and cooperative blackhole attacks are investigated. The dependencies of features on the type of network traffic and the dependence of performance metrics on the speed of mobile nodes in the network are investigated. The conducted experimental studies show the effectiveness of an intrusion detection system prototype on simulated data.

MANET Attack Prevention - Since the mid-1990s, the growth of laptops and Wi-Fi networks has led to a great increase in the use of MANET (Mobile ad hoc network) in wireless communication. MANET is a group of mobile devices for example mobile phones, computers, laptops, radios, sensors, etc., that communicate with each other wirelessly without any support from existing internet infrastructure or any other kind of fixed stations. As MANET is an infrastructure-less network it is prone to various attacks, which can lead to loss of information during communication, security breaches or other unauthentic malpractices. Various types of attacks to which MANET can be vulnerable are denial of service (DOS) and packet dropping attacks such as Gray hole, Blackhole, Wormhole, etc. In this research, we are particularly focusing on the detection and prevention of Gray hole attack. Gray hole node drops selective data packets, while participating in the routing process like other nodes, and advertises itself as a genuine node. The Intrusion Detection System (IDS) technique is used for identification and aversion of the Gray hole attack. Use of AODV routing protocol is made in the network. The network is incorporated and simulation parameters such as PDR (Packet Delivery Ratio), Energy Consumption, End-to-end delay, and Throughput are analyzed using simulation software.

Intrusion Intolerance - Redundant execution technology is one of the effective ways to improve the safety and reliability of computer systems. By rationally configuring redundant resources, adding components with the same function, using the determined redundant execution logic to coordinate and efficiently execute synchronously can effectively ensure high availability of the machine and system. Fault-tolerant is based on redundant execution, which is the primary method of dealing with system hardware failures. Recently, multi-threading redundancy has realized the continuous development of fault-tolerant technology, which makes the processing granularity of the system tolerate random failure factors gradually reduced. At the same time, intrusion tolerant technology has also been continuously developed with the emergence of multi-variant execution technology. It mainly uses the idea of dynamic heterogeneous redundancy to construct a set of variants with equivalent functions and different structures to complete the detection and processing of threats outside the system. We summarize the critical technologies of redundant execution to achieve fault tolerance and intrusion tolerance in recent years, sorts out the role of redundant execution in the development process from fault tolerance technology to intrusion tolerance technology, classify redundant execution technologies at different levels, finally point out the development prospects of redundant execution technology in multiple application fields and future technical research directions.

Intrusion Intolerance - 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.

Machine Learning - An IDS is a system that helps in detecting any kind of doubtful activity on a computer network. It is capable of identifying suspicious activities at both the levels i.e. locally at the system level and in transit at the network level. Since, the system does not have its own dataset as a result it is inefficient in identifying unknown attacks. In order to overcome this inefficiency, we make use of ML. ML assists in analysing and categorizing attacks on diverse datasets. In this study, the efficacy of eight machine learning algorithms based on KDD CUP99 is assessed. Based on our implementation and analysis, amongst the eight Algorithms considered here, Support Vector Machine (SVM), Random Forest (RF) and Decision Tree (DT) have the highest testing accuracy of which got SVM does have the highest accuracy

Intelligent Data and Security - 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.

Industrial Control Systems - With the wide application of Internet technology in the industrial control field, industrial control networks are getting larger and larger, and the industrial data generated by industrial control systems are increasing dramatically, and the performance requirements of the acquisition and storage systems are getting higher and higher. The collection and analysis of industrial equipment work logs and industrial timing data can realize comprehensive management and continuous monitoring of industrial control system work status, as well as intrusion detection and energy efficiency analysis in terms of traffic and data. In the face of increasingly large realtime industrial data, existing log collection systems and timing data gateways, such as packet loss and other phenomena [1], can not be more complete preservation of industrial control network thermal data. The emergence of software-defined networking provides a new solution to realize massive thermal data collection in industrial control networks. This paper proposes a 10-gigabit industrial thermal data acquisition and storage scheme based on software-defined networking, which uses software-defined networking technology to solve the problem of insufficient performance of existing gateways.

An IDS is a system that helps in detecting any kind of doubtful activity on a computer network. It is capable of identifying suspicious activities at both the levels i.e. locally at the system level and in transit at the network level. Since, the system does not have its own dataset as a result it is inefficient in identifying unknown attacks. In order to overcome this inefficiency, we make use of ML. ML assists in analysing and categorizing attacks on diverse datasets. In this study, the efficacy of eight machine learning algorithms based on KDD CUP99 is assessed. Based on our implementation and analysis, amongst the eight Algorithms considered here, Support Vector Machine (SVM), Random Forest (RF) and Decision Tree (DT) have the highest testing accuracy of which got SVM does have the highest accuracy

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.