An Efficient Intrusion Detection Model Based on Fast Inductive Learning

Today, intrusion detection system using the neural network is an interested and considerable area for the research community. The computational intelligence systems are defined on the basis of the following parameters: fault tolerance and adaptation; adaptable the requirements of make a better intrusion detection model. In this paper, provide an overview of the research progress using computational intelligence to the problem of intrusion detection. The goal of this paper summarized and compared research contributions of Intrusion detection system using computational intelligence and neural network, define existing research challenges and anticipated solution of machine learning. Research showed that application of machine learning techniques in intrusion detection could achieve high detection rate. Machine learning and classification algorithms help to design "Intrusion Detection Models" which can classify the network traffic into intrusive or normal traffic. This paper discusses some commonly used machine learning techniques in Intrusion Detection System and also reviews some of the existing machine learning IDS proposed by researchers at different times.

Blockchain and federated learning-based intrusion detection approaches for edge-enabled industrial IoT networks: a survey

The widespread use of the Internet of Things and distributed heterogeneous devices has shed light on the implementation of efficient and reliable intrusion detection systems. These systems should be able to efficiently protect data and physical devices from cyber-attacks. However, the huge amount of data with different dimensions and security features can affect the detection accuracy and increase the computation complexity of these systems. Lately, Artificial Intelligence has received significant interest and is now being integrated into these systems to intelligently detect and protect against cyber-attacks. This paper aims to propose an intelligent intrusion detection model to predict and detect attacks in cyberspace. The model is designed based on the concept of Decision Trees, taking into consideration the ranking of the security features. The model is applied to a real dataset for network intrusion detection systems. Moreover, it is validated based on predefined performance evaluation metrics, namely accuracy, precision, recall and Fscore. Meanwhile, the experimental results reveal that our tree-based intrusion detection model can detect and predict cyber-attacks efficiently and reduce the complexity of computation process compared to other traditional machine learning techniques.

Internet of Medical Things (IoMT) is network of interconnected medical devices (smart watches, pace makers, prosthetics, glucometer, etc.), software applications, and health systems and services. IoMT has successfully addressed many old healthcare problems. But it comes with its drawbacks essentially with patient’s information privacy and security related issues that comes from IoMT architecture. Using obsolete systems can bring security vulnerabilities and draw attacker’s attention emphasizing the need for effective solution to secure and protect the data traffic in IoMT network. Recently, intrusion detection system (IDS) is regarded as an essential security solution for protecting IoMT network. In the past decades, machines learning (ML) algorithms have demonstrated breakthrough results in the field of intrusion detection. Notwithstanding, to our knowledge, there is no work that investigates the power of machines learning algorithms for intrusion detection in IoMT network. This paper aims to fill this gap of knowledge investigating the application of different ML algorithms for intrusion detection in IoMT network. The investigation analysis includes ML algorithms such as K-nearest neighbor, Naïve Bayes, support vector machine, artificial neural network and decision tree. The benchmark dataset, Bot-IoT which is publicly available with comprehensive set of attacks was used to train and test the effectiveness of all ML models considered for investigation. Also, we used comprehensive set of evaluation metrics to compare the power of ML algorithms with regard to their detection accuracy for intrusion in IoMT networks. The outcome of the analysis provides a promising path to identify the best the machine learning approach can be used for building effective IDS that can safeguard IoMT network against malicious activities.

Today’s security attacks on computer networks are becoming more complex and severe, which has prompted security researchers to use a variety of machine learning techniques to safeguard the information and reputation of their clients. Detecting network infiltration has long been a difficult task. Machine learning advancements have raised the way for improving intrusion detection systems (IDS). This development has led to intrusion detection’s integration into network security. Using supervised machine learning techniques, intrusion detection has attained great detection accuracy. However, it is unlikely that a machine learning (ML) classifier will be able to correctly identify all attacks, particularly obscure ones.An approach based on deep learning is presented for more precise intrusion detection. This review article presents an extensive survey and classification of deep learning-based intrusion detection techniques with an emphasis on these approaches. The main background ideas about the IDS architecture and several machine and deep learning approaches are initially presented. Then, it categorizes these schemes based on the many types of methodologies each one employs. It explains how accurate intrusion detection is achieved through the use of machine and deep learning networks. The researched IDS frameworks are then fully analysed, with final thoughts and suggested directions for the future underlined.

Advancements in machine learning and artificial intelligence have been widely utilised in the security domain, including but not limited to intrusion detection techniques. With the large training datasets of modern traffic, intelligent algorithms and powerful machine learning tools, security researchers have been able to greatly improve on the intrusion detection models and enhance their ability to detect malicious traffic more accurately. Nonetheless, the problem of detecting completely unknown security attacks is still an open area of research. The enormous number of newly developed attacks constitutes an eccentric challenge for all types of intrusion detection systems. Additionally, the lack of a standard definition of what constitutes an unknown security attack in the literature and the industry alike adds to the problem. In this paper, the researchers reviewed the studies on detecting unknown attacks over the past 10 years and found that they tended to use inconsistent definitions. This formulates the need for a standard consistent definition to have comparable results. The researchers proposed a new categorisation of two types of unknown attacks, namely Type-A, which represents a completely new category of unknown attacks, and Type-B, which represents unknown attacks within already known categories of attacks. The researchers conducted several experiments and evaluated modern intrusion detection systems based on shallow and deep artificial neural network models and their ability to detect Type-A and Type-B attacks using two well-known benchmark datasets for network intrusion detection. The research problem was studied as both a binary and multi-class classification problem. The results showed that the evaluated models had poor overall generalisation error measures, where the classification error rate in detecting several types of unknown attacks from 92 experiments was 50.09%, which highlights the need for new approaches and techniques to address this problem.

The rapid growth of cloud computing has led to an increased demand for security mechanisms to safeguard sensitive data and resources from cyber threats. Intrusion detection systems (IDS) play a crucial role in identifying unauthorized access or malicious activities within cloud environments. This paper presents a comparative study of machine learning (ML) techniques used in intrusion detection for cloud computing platforms. Various ML algorithms, including decision trees, support vector machines, k-nearest neighbors, and neural networks, are evaluated based on their performance in detecting different types of attacks. The study assesses the accuracy, efficiency, and scalability of these techniques in cloud environments, highlighting their strengths and limitations. The findings provide valuable insights into the selection of appropriate machine learning models for effective intrusion detection in dynamic and scalable cloud systems.

Uses of Artificial Intelligent Techniques to Build Accurate Models for Intrusion Detection System

To study the characteristics and performance of the deep learning in intelligent intrusion detection, two hybrid algorithms, which combine restricted Boltzmann machine (RBM) with support vector machine (SVM) and deep belief network (DBN) respectively, are used to analyze the accuracy, false positive rate, false negative rate and testing time with the data set used for The Third International Knowledge Discovery and Data Mining Tools Competition (KDDCup99). Compared with each other and traditional hybrid intrusion detection algorithm, DBN performs better than the other both in the accuracy and speed, which is attributed to the unsupervised learning of RBM networks and the combination of the neural networks at the bottom.

Smart grid significantly improves the functions of conventional power networks, but it also makes them more susceptible to attacks of different types. Using smart grid’s vulnerabilities, attackers may compromise the integrity and confidentiality of networks, and obtain access to them as a result. Intrusion Detection System (IDS) constitutes an important means to provide safe and reliable services in a smart grid environment. This paper proposes an intrusion detection model under smart grid environment that is widely distributed in the three-tier architecture of power grid system. The model introduces Random Forest (RF) into machine learning and Particle Swarm Optimization (PSO) into evolutionary computation. To improve the accuracy of the model, this paper taps into the adaptive strategy for continued adjustment concerning the flight attitude of particles, and uses multiple mutation methods to prevent the occurrence of local optimum. The AMPSO algorithm proposed in this paper performs well in benchmarking function tests, thereby providing effective guarantee for the improvement of RF classifier. According to simulation experiments based on test sets of NPL-KDD, the intrusion detection model proposed in this paper is highly effective and practical.

Malware authors are continuously developing crime toolkits. This has led to the situation of zero-day attacks, where malware harm computer systems despite the protection from existing Intrusion Detection Systems (IDSs). We propose an Intelligent Intrusion Detection and Prevention System (IIDPS) approach that combines the Signature based Intrusion Detection system (SIDS), Anomaly based Intrusion Detection System (AIDS) and Response Intrusion Detection System (RIDS). We used a risk assessment approach to determine an appropriate response action against each attack event. We also demonstrated the IIDPS make the detection and prevention of malware more effective.

Driven by recent technological advances in wireless communications, wireless sensors, and low power networked systems, wireless sensor networks (WSNs) are emerging as a promising technology in healthcare applications. Since information transmitted in these wireless body area networks (WBAN) often consists of critical and sensitive patient health and personal information, securing these networks is of central importance to their practical deployment in healthcare applications. The objective of this research is to design and develop intelligent intrusion detection techniques to improve security in WBAN. In this work, we propose iDetect, a multi-objective genetic algorithm based intrusion detection system (IDS) to provide optimal attack detection in these networks. The proposed algorithm guarantees that only those features necessary for detecting a specific attack is used in the intrusion detection process, thereby decreasing the computational complexity.

The cyber attack has been growing rapidly on IoT(Internet of things). Existing security mechanisms are no longer sufficient to detect and mitigate these attacks. Intrusion Detection Systems have come up as the second line of defense; however, classic techniques have always had problems with novel and complex attacks. This paper presents a proposed hybrid deep learning-based intrusion detection system that combines Autoencoder, LSTM, and CNN architectures in a complementary fashion to achieve accurate and robust intrusion detection. The evaluated system has been tested with the NSL-KDD dataset, where a stratified cross-validation technique is applied for the generalizability of the result. The approach demonstrates very high accuracy, precision, and recall and is markedly superior to traditional and single-model deep learning approaches. Comprehensive literature reviews are provided, motivation and objectives are discussed, research gaps are identified, and the workflow, model architecture, dataset characteristics, and result analysis have been detailed.

Intrusion Detection system becomes the one of the favorite research topic due to its capability of detecting various network attacks. There are many works done in the field of intrusion detection up till now. In this paper we are proposing the pattern based Intrusion Detection model. This system is based on CIDF architecture. The Pattern based Intrusion Detection model consist of five modules: Capture Module, Decode Module, Detection Module, Known Attack Pattern Module and Action Module. A pattern based IDS monitor packets on the network and compares them against a database of known attack pattern of malicious threats. Our paper focuses on the packet sniffer and its working, various attacks on network as an intrusion and their detection using pattern based IDS.

Traditional firewalls and data encryption techniques can no longer match the demands of current IoT network security due to the rising amount and variety of network threats. In order to manage IoT network risks, intrusion detection solutions have been advised. Even though machine learning (ML) helps the widely used intrusion detection techniques currently in use, these algorithms struggle with low detection rates and the requirement for extensive feature engineering. The deep learning model for IoT network intrusion detection is a method for traffic anomaly detection that is suggested by this study. To extract the sequence properties of data flow through a CNN, it combines an attention mechanism with a Long Short Term Memory (LSTM) network. This method uses adaptive synthetic sampling (ADASYN) to increase the size of minority-class samples. The proposed models demonstrated acceptable precision and recall for each class when used for binary-class classification, proving their stability and capacity to identify all classes correctly. The MLP classifier’s accuracy, precision, recall, and F1 value were 87%, 89%, 87%, and 89%, respectively, with an AUC score of 0.88. Overall, the proposed models performed well. The attack and all-class models exhibited good AUCs and macro metrics, the same as the proposed MLP classifier, which had an F1 score of 83% and an AUC score of 0.94. Additionally, it trained the MLP classifier and integrated the ADAM optimizer and category cross-entropy loss function for all-class classification. With an AUC value of 94%, it possessed 84% accuracy, 87% precision, 84% recall, and an 83% F1 score. A further indication of the hybrid model’s ability to combine the benefits of both models to improve overall performance was that it regularly outperformed the MLP model. This model’s accuracy and F1 score are better than those of earlier comparable algorithms, according to experimental results using the publicly accessible benchmark dataset for network intrusion detection (NSL–KDD).