Biblio

The success of the web and the consequent rise in data sharing have made network security a challenge. Attackers from all around the world target PC installations. When an attack is successful, an electronic device's security is jeopardised. The intrusion implicitly includes any sort of behaviours that purport to think twice about the respectability, secrecy, or accessibility of an asset. Information is shielded from unauthorised clients' scrutiny by the integrity of a certain foundation. Accessibility refers to the framework that gives users of the framework true access to information. The word "classification" implies that data within a given frame is shielded from unauthorised access and public display. Consequently, a PC network is considered to be fully completed if the primary objectives of these three standards have been satisfactorily met. To assist in achieving these objectives, Intrusion Detection Systems have been developed with the fundamental purpose of scanning incoming traffic on computer networks for malicious intrusions.

The SCADA (Supervisory Control And Data Acquisition) has become ubiquitous in industrial control systems. However, it may be exposed to cyber attack threats when it accesses the Internet. We propose a three-layer IDS (Intrusion Detection System) model, which integrates three main functions: access control, flow detection and password authentication. We use the reliability test system IEEE RTS-79 to evaluate the reliability. The experimental results provide insights into the establishment of the power SCADA system reliability enhancement strategies.

Real-time situational awareness (SA) plays an essential role in accurate and timely incident response. Maintaining SA is, however, extremely costly due to excessive false alerts generated by intrusion detection systems, which require prioritization and manual investigation by security analysts. In this paper, we propose a novel approach to prioritizing alerts so as to maximize SA, by formulating the problem as that of active learning in a hidden Markov model (HMM). We propose to use the entropy of the belief of the security state as a proxy for the mean squared error (MSE) of the belief, and we develop two computationally tractable policies for choosing alerts to investigate that minimize the entropy, taking into account the potential uncertainty of the investigations' results. We use simulations to compare our policies to a variety of baseline policies. We find that our policies reduce the MSE of the belief of the security state by up to 50% compared to static baseline policies, and they are robust to high false alert rates and to the investigation errors.

The main intention of edge computing is to improve network performance by storing and computing data at the edge of the network near the end user. However, its rapid development largely ignores security threats in large-scale computing platforms and their capable applications. Therefore, Security and privacy are crucial need for edge computing and edge computing based environment. Security vulnerabilities in edge computing systems lead to security threats affecting edge computing networks. Therefore, there is a basic need for an intrusion detection system (IDS) designed for edge computing to mitigate security attacks. Due to recent attacks, traditional algorithms may not be possibility for edge computing. This article outlines the latest IDS designed for edge computing and focuses on the corresponding methods, functions and mechanisms. This review also provides deep understanding of emerging security attacks in edge computing. This article proves that although the design and implementation of edge computing IDS have been studied previously, the development of efficient, reliable and powerful IDS for edge computing systems is still a crucial task. At the end of the review, the IDS developed will be introduced as a future prospect.

Intrusion detection systems (IDSs) are widely deployed in the industrial control systems to protect network security. IDSs typically generate a huge number of alerts, which are time-consuming for system operators to process. Most of the alerts are individually insignificant false alarms. However, it is not the best solution to discard these alerts, as they can still provide useful information about network situation. Based on the study of characteristics of alerts in the industrial control systems, we adopt an enhanced method of exponentially weighted moving average (EWMA) control charts to help operators in processing alerts. We classify all detection signatures as regular and irregular according to their frequencies, set multiple control limits to detect anomalies, and monitor regular signatures for network security situational awareness. Extensive experiments have been performed using real-world alert data. Simulation results demonstrate that the proposed enhanced EWMA method can greatly reduce the volume of alerts to be processed while reserving significant abnormal information.

The spread of Internet of Things (IoT) devices in our homes, healthcare, industries etc. are more easily infiltrated than desktop computers have resulted in a surge in botnet attacks based on IoT devices, which may jeopardize the IoT security. Hence, there is a need to detect these attacks and mitigate the damage. Existing systems rely on supervised learning-based intrusion detection methods, which require a large labelled data set to achieve high accuracy. Botnets are onerous to detect because of stealthy command & control protocols and large amount of network traffic and hence obtaining a large labelled data set is also difficult. Due to unlabeled Network traffic, the supervised classification techniques may not be used directly to sort out the botnet that is responsible for the attack. To overcome this limitation, a semi-supervised Deep Learning (DL) approach is proposed which uses Semi-supervised GAN (SGAN) for IoT botnet detection on N-BaIoT dataset which contains "Bashlite" and "Mirai" attacks along with their sub attacks. The results have been compared with the state-of-the-art supervised solutions and found efficient in terms of better accuracy which is 99.89% in binary classification and 59% in multi classification on larger dataset, faster and reliable model for IoT Botnet detection.

With the continues growth of our technology, majority in our sectors are becoming smart and one of its great applications is in agriculture, which we call it as smart farming. The application of sensors, IoT, artificial intelligence, networking in the agricultural setting with the main purpose of increasing crop production and security level. With this advancement in farming, this provides a lot of privileges like remote monitoring, optimization of produce and too many to mention. In light of the thorough systematic analysis performed in this study, it was discovered that Edge-of-things is a potential computing scheme that could boost an artificial intelligence for intelligent remote farm security and intrusion detection pre-alarm system over other computing schemes. Again, the purpose of this study is not to replace existing cloud computing, but rather to highlight the potential of the Edge. The Edge architecture improves end-user experience by improving the time-related response of the system. response time of the system. One of the strengths of this system is to provide time-critical response service to make a decision with almost no delay, making it ideal for a farm security setting. Moreover, this study discussed the comparative analysis of Cloud, Fog and Edge in relation to farm security, the demand for a farm security system and the tools needed to materialize an Edge computing in a farm environment.

Due to Bitcoin's innovative block structure, it is both immutable and decentralized, making it a valuable tool or instrument for changing current financial systems. However, the appealing features of Bitcoin have also drawn the attention of cybercriminals. The Bitcoin scripting system allows users to include up to 80 bytes of arbitrary data in Bitcoin transactions, making it possible to store illegal information in the blockchain. This makes Bitcoin a powerful tool for obfuscating information and using it as the command-and-control infrastructure for blockchain-based botnets. On the other hand, Blockchain offers an intriguing solution for IoT security. Blockchain provides strong protection against data tampering, locks Internet of Things devices, and enables the shutdown of compromised devices within an IoT network. Thus, blockchain could be used both to attack and defend IoT networks and communications.

Industrial control systems (ICSs) have become more complex due to their increasing connectivity, heterogeneity and, autonomy. As a result, cyber-threats against such systems have been significantly increased as well. Since a compromised industrial system can easily lead to hazardous safety and security consequences, it is crucial to develop security countermeasures to protect coexisting IT systems and industrial physical processes being involved in modern ICSs. Accordingly, in this study, we propose a deep learning-based semantic anomaly detection framework to model the complex behavior of ICSs. In contrast to the related work assuming only simpler security threats targeting individual controllers in an ICS, we address multi-PLC attacks that are harder to detect as requiring to observe the overall system state alongside single-PLC attacks. Using industrial simulation and emulation frameworks, we create a realistic setup representing both the production and networking aspects of industrial systems and conduct some potential attacks. Our experimental results indicate that our model can detect single-PLC attacks with 95% accuracy and multi-PLC attacks with 80% accuracy and nearly 1% false positive rate.

In recent years, as an important part of the Internet, web applications have gradually penetrated into life. Now enterprises, units and institutions are using web applications regardless of size. Intrusion detection to effectively identify malicious traffic has become an inevitable requirement for the development of network security technology. In addition, the proportion of deserialization vulnerabilities is increasing. Traditional intrusion detection mostly focuses on the identification of SQL injection, XSS, and command execution, and there are few studies on the identification of deserialization attack traffic. This paper use a method to extracts relevant features from the deserialized traffic or even the obfuscated deserialized traffic by reorganizing the traffic and running the relevant content through simulation, and combines deep learning technology to make judgments to efficiently identify deserialization attacks. Finally, a prototype system was designed to capture related attacks in real-world. The technology can be used in the field of malicious traffic detection and help combat Internet crimes in the future.

Classifying and predicting the accuracy of intrusion detection on cybercrime by comparing machine learning methods such as Innovative Decision Tree (DT) with Support Vector Machine (SVM). By comparing the Decision Tree (N=20) and the Support Vector Machine algorithm (N=20) two classes of machine learning classifiers were used to determine the accuracy. The decision Tree (99.19%) has the highest accuracy than the SVM (98.5615%) and the independent T-test was carried out (=.507) and shows that it is statistically insignificant (p\textgreater0.05) with a confidence value of 95%. by comparing Innovative Decision Tree and Support Vector Machine. The Decision Tree is more productive than the Support Vector Machine for recognizing intruders with substantially checked, according to the significant analysis.

Improving the accuracy of intruders in innovative Intrusion detection by comparing Machine Learning classifiers such as Random Forest (RF) with Support Vector Machine (SVM). Two groups of supervised Machine Learning algorithms acquire perfection by looking at the Random Forest calculation (N=20) with the Support Vector Machine calculation (N=20)G power value is 0.8. Random Forest (99.3198%) has the highest accuracy than the SVM (9S.56l5%) and the independent T-test was carried out (=0.507) and shows that it is statistically insignificant (p \textgreater0.05) with a confidence value of 95% by comparing RF and SVM. Conclusion: The comparative examination displays that the Random Forest is more productive than the Support Vector Machine for identifying the intruders are significantly tested.

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.

This paper discusses research-based findings of applying metaheuristic optimization techniques and nature-inspired algorithms to detect and mitigate phishing attacks. The focus will be on the Firefly nature-inspired metaheuristic algorithm optimized with Random Forest and Support Vector Machine (SVM) classification. Existing research recommends the development and use of nature-inspired detection techniques to solve complex real-world problems. Existing research using nature-inspired heuristics appears to be promising in solving NP-hard problems such as the traveling salesperson problem. In the same classification of NP-hard, is that of cyber security existing research indicates that the security threats are complex, and that providing security is an NP-hard problem. This study is expanding the existing research with a hybrid optimization of nature-inspired metaheuristic with existing classifiers (random forest and SVM) for an improvement in results to include increased true positives and decreased false positives. The proposed study will present the importance of nature and natural processes in developing algorithms and systems with high precision and accuracy.

The Internet of Things (IoT) is a technology that has evolved to make day-to-day life faster and easier. But with the increase in the number of users, the IoT network is prone to various security and privacy issues. And most of these issues/attacks occur during the routing of the data in the IoT network. Therefore, for secure routing among resource-constrained nodes of IoT, the RPL protocol has been standardized by IETF. But the RPL protocol is also vulnerable to attacks based on resources, topology formation and traffic flow between nodes. The attacks like DoS, Blackhole, eavesdropping, flood attacks and so on cannot be efficiently defended using RPL protocol for routing data in IoT networks. So, defense mechanisms are used to protect networks from routing attacks. And are classified into Secure Routing Protocols (SRPs) and Intrusion Detection systems (IDs). This paper gives an overview of the RPL attacks and the defense mechanisms used to detect or mitigate the RPL routing attacks in IoT networks.

The Internet of Things (IoT) is advancing technology by creating smart surroundings that make it easier for humans to do their work. This technological advancement not only improves human life and expands economic opportunities, but also allows intruders or attackers to discover and exploit numerous methods in order to circumvent the security of IoT networks. Hence, security and privacy are the key concerns to the IoT networks. It is vital to protect computer and IoT networks from many sorts of anomalies and attacks. Traditional intrusion detection systems (IDS) collect and employ large amounts of data with irrelevant and inappropriate attributes to train machine learning models, resulting in long detection times and a high rate of misclassification. This research presents an advance approach for the design of IDS for IoT networks based on the Particle Swarm Optimization Algorithm (PSO) for feature selection and the Extreme Gradient Boosting (XGB) model for PSO fitness function. The classifier utilized in the intrusion detection process is Random Forest (RF). The IoTID20 is being utilized to evaluate the efficacy and robustness of our suggested strategy. The proposed system attains the following level of accuracy on the IoTID20 dataset for different levels of classification: Binary classification 98 %, multiclass classification 83 %. The results indicate that the proposed framework effectively detects cyber threats and improves the security of IoT networks.

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 Activity and Event Network (AEN) graph is a new framework that allows modeling and detecting intrusions by capturing ongoing security-relevant activity and events occurring at a given organization using a large time-varying graph model. The graph is generated by processing various network security logs, such as network packets, system logs, and intrusion detection alerts. In this paper, we show how known attack methods can be captured generically using attack fingerprints based on the AEN graph. The fingerprints are constructed by identifying attack idiosyncrasies under the form of subgraphs that represent indicators of compromise (IOes), and then encoded using Property Graph Query Language (PGQL) queries. Among the many attack types, three main categories are implemented as a proof of concept in this paper: scanning, denial of service (DoS), and authentication breaches; each category contains its common variations. The experimental evaluation of the fingerprints was carried using a combination of intrusion detection datasets and yielded very encouraging results.

Mobile small cells that are enabled with Network Coding (NC) are seen as a potentially useful technique for Fifth Generation (5G) networks, since they can cover an entire city and can be put up on demand anywhere, any time, and on any device. Despite numerous advantages, significant security issues arise as a result of the fact that the NC-enabled mobile small cells are vulnerable to attacks. Intrusions are a severe security threat that exploits the inherent vulnerabilities of NC. In order to make NC-enabled mobile small cells to realize their full potential, it is essential to implement intrusion detection systems. When compared to homomorphic signature or hashing systems, homomorphic message authentication codes (MACs) provide safe network coding techniques with relatively smaller overheads. A number of research studies have been conducted with the goal of developing mobile small cells that are enabled with secure network coding and coming up with integrity protocols that are appropriate for such crowded situations. However, the intermediate nodes alter packets while they are in transit and hence the integrity of the data cannot be confirmed by using MACs and checksums. This research study has analyzed numerous intrusion detection models for NC enabled small cells. This research helps the scholars to get a brief idea about various intrusion detection models.

Recently, research on AI-based network intrusion detection has been actively conducted. In previous studies, the machine learning models such as SVM (Support Vector Machine) and RF (Random Forest) showed consistently high performance, whereas the NB (Naïve Bayes) showed various performances with large deviations. In the paper, after analyzing the cause of the NB models showing various performances addressed in the several studies, we measured the performance of the Gaussian NB model according to the smoothing factor that is closely related to these causes. Furthermore, we compared the performance of the Gaussian NB model with that of the other models as a zero-day attack detection system. As a result of the experiment, the accuracy was 38.80% and 87.99% in case that the smoothing factor is 0 and default respectively, and the highest accuracy was 94.53% in case that the smoothing factor is 1e-01. In the experiment, we used only some types of the attack data in the NSL-KDD dataset. The experiments showed the applicability of the Gaussian NB model as a zero-day attack detection system in the future. In addition, it is clarified that the smoothing factor of the Gaussian NB model determines the shape of gaussian distribution that is related to the likelihood.

In recent years, cyber-attacks on modern industrial control systems (ICS) have become more common and it acts as a victim to various kind of attackers. The percentage of attacked ICS computers in the world in 2021 is 39.6%. To identify the anomaly in a large database system is a challenging task. Deep-learning model provides better solutions for handling the huge dataset with good accuracy. On the other hand, real time datasets are highly imbalanced with their sample proportions. In this research, GAN based model, a supervised learning method which generates new fake samples that is similar to real samples has been proposed. GAN based adversarial training would address the class imbalance problem in real time datasets. Adversarial samples are combined with legitimate samples and shuffled via proper proportion and given as input to the classifiers. The generated data samples along with the original ones are classified using various machine learning classifiers and their performances have been evaluated. Gradient boosting was found to classify with 98% accuracy when compared to other

While digitization of distribution grids through information and communications technology brings numerous benefits, it also increases the grid's vulnerability to serious cyber attacks. Unlike conventional systems, attacks on many industrial control systems such as power grids often occur in multiple stages, with the attacker taking several steps at once to achieve its goal. Detection mechanisms with situational awareness are needed to detect orchestrated attack steps as part of a coherent attack campaign. To provide a foundation for detection and prevention of such attacks, this paper addresses the detection of multi-stage cyber attacks with the aid of a graph-based cyber intelligence database and alert correlation approach. Specifically, we propose an approach to detect multi-stage attacks by lever-aging heterogeneous data to form a knowledge base and employ a model-based correlation approach on the generated alerts to identify multi-stage cyber attack sequences taking place in the network. We investigate the detection quality of the proposed approach by using a case study of a multi-stage cyber attack campaign in a future-orientated power grid pilot.

With the rapid development of the Internet of Things (IoT), a large amount of data is exchanged between various communicating devices. Since the data should be communicated securely between the communicating devices, the network security is one of the dominant research areas for the 6LoWPAN IoT applications. Meanwhile, 6LoWPAN devices are vulnerable to attacks inherited from both the wireless sensor networks and the Internet protocols. Thus intrusion detection systems have become more and more critical and play a noteworthy role in improving the 6LoWPAN IoT networks. However, most intrusion detection systems focus on the attacked areas in the IoT networks instead of precisely on certain IoT nodes. This may lead more resources to further detect the compromised nodes or waste resources when detaching the whole attacked area. In this paper, we therefore proposed a new precisional detection strategy for 6LoWPAN Networks, named as PDS-6LoWPAN. In order to validate the strategy, we evaluate the performance and applicability of our solution with a thorough simulation by taking into account the detection accuracy and the detection response time.

Today billions of people are accessing the internet around the world. There is a need for new technology to provide security against malicious activities that can take preventive/ defensive actions against constantly evolving attacks. A new generation of technology that keeps an eye on such activities and responds intelligently to them is the intrusion detection system employing machine learning. It is difficult for traditional techniques to analyze network generated data due to nature, amount, and speed with which the data is generated. The evolution of advanced cyber threats makes it difficult for existing IDS to perform up to the mark. In addition, managing large volumes of data is beyond the capabilities of computer hardware and software. This data is not only vast in scope, but it is also moving quickly. The system architecture suggested in this study uses SVM to train the model and feature selection based on the information gain ratio measure ranking approach to boost the overall system's efficiency and increase the attack detection rate. This work also addresses the issue of false alarms and trying to reduce them. In the proposed framework, the UNSW-NB15 dataset is used. For analysis, the UNSW-NB15 and NSL-KDD datasets are used. Along with SVM, we have also trained various models using Naive Bayes, ANN, RF, etc. We have compared the result of various models. Also, we can extend these trained models to create an ensemble approach to improve the performance of IDS.

The intrusion detection systems are vital for the sustainability of Cooperative Intelligent Transportation Systems (C-ITS) and the detection of sybil attacks are particularly challenging. In this work, we propose a novel approach for the detection of sybil attacks in C-ITS environments. We provide an evaluation of our approach using extensive simulations that rely on real traces, showing our detection approach's effectiveness.