Intrusion detection systems for wireless sensor networks using computational intelligence techniques

As the reliance on digital networks grows, the need for robust security measures becomes paramount. It offers a thorough analysis of creating and assessing a Network Intrusion Detection System (NIDS) incorporating various machine learning algorithms. The selected algorithms consist of K Nearest Neighbour, Naïve Bayes, Decision Tree, Support Vector Machine, and Logistic Regression. Moreover, widely recognised ensemble learning models like CatBoost, AdaBoost, Gradient Boost, Random Forest, and Extreme Gradient Boost. The core objective is to improve the efficiency of intrusion detection by evaluating various machine-learning models. Utilizes feature selection techniques based on chi-square to enhance the relevance and effectiveness of the selected features. The evaluation metrics, particularly Matthews’ Correlation Coefficient (MCC) and ROC Curves, are used in assessing the models’ efficacy. They offer valuable insights into the models’ capability to differentiate between normal and malicious network activities. This study aims to advance cybersecurity by comparing classical machine learning and ensemble models for network intrusion detection. Significantly, our results underscore XGBoost’s effectiveness with 0.996 accuracy and 0.993 MCC, in enhancing digital network security.

Nature-inspired intrusion detection system for protecting software-defined networks controller

Enhancing cyber security in WSN using optimized self-attention-based provisional variational auto-encoder generative adversarial network

Network intrusion detection system are significant features that contribute to the enterprises and organization network success. In the past decade, Intrusion Detection system (IDS) using several methods have been proposed and implemented to safeguard the networks within an organization to be reliable, secure, and readily available. Although, some of these methods are built utilizing machine learning concepts. IDS utilizing machine learning techniques are accurate, effective and efficient in spotting attacks. However, the machine learning model performance reduces with high dimensional intrusion dataset. Therefore, it is sacrosanct to implement an efficient feature selection technique that can generate a great impact on the classification stage. Also, the feature selection phase is the most serious phase in IDS based on machine learning. This phase is expensive both in time and efforts. Furthermore, most of the proposed machine learning intrusion detection system suffered from low detection accuracy and high false positive rate when the models are experimented on high dimensional dataset. The aim of this paper is to propose a binary firefly algorithm (BFFA) based feature selection for IDS. We first performed normalization in the first stage of the model. Subsequently, the BFFA algorithm was used for feature selection stage. We adopted random forest algorithm for the classification phase. The experiment was performed on high dimensional University of New South Wales-NB 2015 (UNSW-NB15) dataset with seventy five percent of the data used for training the model and twenty percent for testing. The findings showed an accuracy of 99.72%, detection rate of 99.84%, precision of 99.27%, recall of 99.84% and F-score of 99.56%. The results were gauge with the state-of-the-art results and our results were found outstanding.

Network Intrusion Detection Systems (NIDS) have played an important role in protecting computer networks against illegal or unauthorized access and many cyberattacks. Given the advancement of machine learning (ML) approaches, NIDS have become more effective in detecting various complex anomalies in computer networks. However, the increasing complexity in adversarial attacks (AAs) poses a significant challenge to such systems. Cyberattacks are estimated to cost approximately $10.5 trillion annually by 2025, and this encourages researchers to improve and develop ML-based NIDS in order to address adversarial vulnerabilities in these systems to remain the systems resilient to modern attacks. In this review, we reveal the weaknesses of ML-based IDS to AAs in which many different attack techniques, such as evasion, poisoning, and generative adversarial networks (GAN) have been included. Also, this study presents and evaluates current possible defensive approaches against AAs, including but not limited to anomaly detection approaches, adversarial training, and feature selection. We also provide a comparative analysis of different ML models used in NIDS in order to further evaluate the system’s susceptibility to sophisticated AAs. A discussion on future work to improve ML-based NIDS resilience against sophisticated attacks is also given.

With the growth of Internet, there has been a tremendous increases in the number of attacks and therefore Intrusion Detection Systems (IDS’s) has become a main stream of information security. The purpose of IDS is to help the computer systems to deal with attacks. This anomaly detection system creates a database of normal behaviour and deviations from the normal behaviour to trigger during the occurrence of intrusions. Based on the source of data, IDS is classified into Host based IDS and Network based IDS. In network based IDS, the individual packets flowing through the network are analyzed where as in host based IDS the activities on the single computer or host are analyzed. The feature selection used in IDS helps to reduce the classification time. In this paper, the IDS for detecting the attacks effectively has been proposed and implemented. For this purpose, a new feature selection algorithm called Optimal Feature Selection algorithm based on Information Gain Ratio has been proposed and implemented. This feature selection algorithm selects optimal number of features from KDD Cup dataset. In addition, two classification techniques namely Support Vector Machine and Rule Based Classification have been used for effective classification of the data set. This system is very efficient in detecting DoS attacks and effectively reduces the false alarm rate. The proposed feature selection and classification algorithms enhance the performance of the IDS in detecting the attacks.

Network intrusion detection systems (NIDS) are essential for protecting computer networks against cyberattacks. The selection of a nominal set of essential features that may adequately discriminate malicious traffic from normal traffic is indispensable while developing a NIDS. As such, a more reliable and accurate detection result may be realized when intrusion detection is carried out on a dataset based on an inclusive feature representation. This work presents the pre-processing and feature selection workflow as well as its results in the case of the CIC-IDS-2017 dataset, with a focus on two cyber-attacks, namely Denial-of-Service (DoS) and PortScan. The study applied an ensemble feature selection method based on information gain and Random Forest to filter out important features. Recursive Feature Elimination method was then applied to the reduced features to optimize the selected feature subset. The selected feature subset was experimented with using two classification algorithms, namely support vector machine and multi-layer perceptron. In the evaluation process, four widely used performance metrics were considered. The study results demonstrated the efficacy of the proposed ensemble approach to optimize the selected feature subset for detecting PortScan and DoS attacks in network traffic. Experimental results revealed that the support vector machine had a slight advantage in accuracy and could train more quickly. According to the study's evaluation, the NIDS may be able to shorten processing times without sacrificing the ability to detect PortScan and DoS attacks accurately by choosing a narrow subset of informative features. This suggests the approach might be applicable to real-world NIDS scenarios involving these attacks. The study also provides encouraging perspectives on how ensemble feature selection utilizing MLP and SVM can enhance the effectiveness of NIDS. Building on these findings, more research can create NIDS solutions that are even more reliable and efficient for the dynamic field of cybersecurity.

Untargeted white-box adversarial attack with heuristic defence methods in real-time deep learning based network intrusion detection system

Intrusion detection is a significant technique for detecting various attacks in Wireless Mesh Networks (WMNs). Intrusion Detection Systems (IDS) powered by machine learning algorithms play a vital role in enhancing security by actively monitoring data traffic in real-time. The performance of attack detection in IDS is reduced by the existence of noisy and unwanted features in the network data. Hence, extracting Informative Features (IF) is essential for enhancing the effectiveness of the IDS. The proposed approach optimally selects IF from the network dataset, which facilitates in accurately detecting intrusions. Existing methods lack in efficient feature selection, leading to higher computational complexity and lower detection performance. This research aims to enhance IDS accuracy by leveraging the eXtreme Gradient Boosting (XGBoost) algorithm to classify the intrusions in the mesh network of smart grid and the optimal feature selection carried out with the help of Golden Eagle Optimization Algorithm (GEOA). This novel hybrid IDS model enhances the attack detection in WMNs. The proposed IDS’s performance is improved by using a feature selection method based on the GEOA, and then obtained results are compared against evolutionary algorithms-based features selection method. The proposed model of IDS using XGBoost algorithm is tested on a smart grid dataset, and its performance is compared with existing literature works.

This paper describes the ESIDE-Depian intrusion detection and prevention system, which uses Bayesian structural and parametric learning and also evidence propagation and adaptation, in order to improve the accuracy and manageability of network intrusion detection systems (NIDS). Current NIDS do not consider the two main detection paradigms, i.e. misuse detection and anomaly detection, in an unified style, so the analysis is not inherently complete. Besides, historical data are not generally used, neither for analysis nor for sequential adaptation of the knowledge representation models used for detection; hence this wealthy information about the essence and the potential trends of the target system is not commonly considered. Thus, by the generalized use of Bayesian belief networks, ESIDE-Depian achieves the main goal of detecting and preventing both well-known and also zero-day attacks with excellent results, by means of unified real-time analysis of network traffic.

WOGRU-IDS — An intelligent intrusion detection system for IoT assisted Wireless Sensor Networks

Attacks on networks are currently the most pressing issue confronting modern society. Network risks affect all networks, from small to large. An intrusion detection system must be present for detecting and mitigating hostile attacks inside networks. Machine Learning and Deep Learning are currently used in several sectors, particularly the security of information, to design efficient intrusion detection systems. These systems can quickly and accurately identify threats. However, because malicious threats emerge and evolve regularly, networks need an advanced security solution. Hence, building an intrusion detection system that is both effective and intelligent is one of the most cognizant research issues. There are several public datasets available for research on intrusion detection. Because of the complexity of attacks and the continually evolving detection of an attack method, publicly available intrusion databases must be updated frequently. A convolutional recurrent neural network is employed in this study to construct a deep-learning-based hybrid intrusion detection system that detects attacks over a network. To boost the efficiency of the intrusion detection system and predictability, the convolutional neural network performs the convolution to collect local features, while a deep-layered recurrent neural network extracts the features in the proposed Hybrid Deep-Learning-Based Network Intrusion Detection System (HDLNIDS). Experiments are conducted using publicly accessible benchmark CICIDS-2018 data, to determine the effectiveness of the proposed system. The findings of the research demonstrate that the proposed HDLNIDS outperforms current intrusion detection approaches with an average accuracy of 98.90% in detecting malicious attacks.

It is seen how intrusion detection systems in networks have gained notoriety and grown to be a significant factor in the area of analysis as a result of the digital revolution and the growth of its operation. The Intrusion Detection System classifies the attack as hostile or conventional based on the users' behavior. A Network Intrusion Detection System (NIDS) handles congested network data. Despite being unorganized, it has the ability to handle challenging situations. The previous study documented many intrusion detection systems, all of which had varying degrees of accuracy. Although no model can accurately detect or foresee a cyberattack. Therefore, creating an effective and reliable intrusion detection model is crucial. In this study, the accuracy of four different machine learning models has been used to the NSL-KDD dataset to provide us enhanced accuracy and outperform some other conventional classifiers for attack classifications. This study and evaluation of the model’s performance used the NSL-KDD dataset. The final results indicate that this suggested approach can provide a reduced false alarm rate while keeping a high diagnosis rate.

Network Intrusion Detection Systems (NIDS) which should perform time-consuming evaluation of every packet received from network have faced throughput challenge as a result of the increase in the speed of network communications and the high volume of Internet threats. In an NIDS, the most important and time-consuming processes are pattern matching and deep inspection of the header and the body of packets. Several analyses show that this process can take up to 75% of the time of processing packets. In this paper, relying on the processing power of general purpose graphics cards - which seem to be a better option compared to other hardware technologies like FPGAs with regard to speed, scalability, flexibility, ease of programming and price - and with the idea of having the signature-based detection engine of NIDS systems run on GPU rather than CPU, it is tried to present an efficient method to increase the speed of intrusion detection systems such as Snort. The proposed method provides a means to perform payload matching and non-payload matching of packets in a parallel platform on GPU, which can speed up the signature-based detection engine of Snort 3.6.

Many network security applications such as Intrusion Detection System (IDS), Firewall and Data Loss Prevention System (DLPS) are based on deep packet inspection, in this packets header as well as payload of the packets are checked with predefined attack signature to identify whether it contains malicious traffic or not. To perform this checking different pattern matching methods are used by NIDS. The most popular method to implement pattern matching is to use of Finite Automata (FA). Generally, regular expressions are used to represent most of the attack signatures defined by NIDS. They are implemented using finite automata, which takes the payload of packet as input string. However, existing approaches of Finite Automata (FA), both deterministic finite automata (DFA) and non-deterministic finite automata (NFA) for pattern matching are having their own advantages and some drawbacks. The DFA based pattern matching methods are fast enough but require more memory. However, NFA based pattern matching methods are comparatively takes less memory but the speed of matching is very slow, to overcome these drawbacks of finite automata there are many approaches have been proposed. This paper discuses comparative study of some Finite Automata (FA) based techniques for pattern matching in network intrusion detection system (NIDS).