An Intelligent Tree-Based Intrusion Detection Model for Cyber Security

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.

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

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.

In recent years, intelligent intrusion detection techniques based on machine learning have been the research spots in the field of intrusion detection. Whereas, as network traffic and network scale increase continually, some current machine learning algorithms can't meet the requirement of the network intrusion detection models for efficiency and accuracy, which restricts the application of machine learning into intrusion detection. In order to enhance the availability and practicality of intelligent intrusion detection system based on machine learning in high-speed network, an improved fast inductive learning method for intrusion detection (FILMID) is designed and implemented. Accordingly, an efficient intrusion detection model based on FILMID algorithm is presented. The experiment results on the standard testing dataset validate the effectiveness of the FILMID based intrusion detection model.

In order to execute cyber-security, intrusion detection systems (IDS) are developed to identify threats and irregularities in computer networks. An efficient data-driven intrusion detection system has been developed as a result of the use of artificial intelligence, particularly machine learning techniques. The proposed security model utilizes BGOTSVM to develop IDS systems starting from a security feature ranking process followed by model development using crucial features. The increasing sophistication of cyber threats necessitates robust and intelligent threat detection systems. This study uses the UNSW-NB15 dataset to demonstrate a Convolutional Neural Network (CNN)-based approach for financial fraud detection. To improve model performance, the suggested methodology incorporates data preparation techniques such as feature selection, one-hot encoding, and managing missing values. The CNN model, optimized through hyperparameter tuning, is compared against traditional machine learning (ML) models, including AdaBoost and naïve biased. Experimental findings show that CNN performs better than any baseline model, reaching the maximum accuracy (93.40%), precision (94.63%), recall (93.40%), and F1-score (92.81%). Performance evaluation metrics, classification reports, and confusion matrices further validate the CNN model’s ability to identify fraudulent activity. Comparative analysis indicates that deep learning techniques, particularly CNN, offer superior threat detection capabilities by effectively identifying complex trends in communication over network information.

The tremendous growth of cyberspace has magnified the risk of advanced cyber-attacks, making the conventional security system deficient. An intrusion detection system (IDS) is a crucial aspect for securing the networks, and machine learning based approaches have attained significance for detecting growing threats, which is extremely essential to address the major concern of network security. This survey paper systematically reviews the machine learning-based IDS, focusing on detection models, the most used datasets, and evaluation metrics. A systematic review methodology, including defined selection criteria and a detailed analysis framework, enables clarity and reproducibility. Furthermore, the paper presents a comparative analysis of popular IDS datasets such as NSL-KDD, CICIS2017, and UNSW-NB15 and points out their limitations, including imbalance and antiquated attack methods. The key contributions are: (a) an organized taxonomy of ML-based IDS techniques, (b) a detailed comparison of datasets and related challenges, and (c) pointing out the research gaps to guide the future directions. This survey paper aims to help scholars in developing vigorous, intelligent, and convertible intrusion detection solutions.

Chapter 5 - Intrusion Prevention and Detection Systems

The growing development of IoT (Internet of Things) devices creates a large attack surface for cybercriminals to conduct potentially more destructive cyberattacks; as a result, the security industry has seen an exponential increase in cyber-attacks. Many of these attacks have effectively accomplished their malicious goals because intruders conduct cyber-attacks using novel and innovative techniques. An anomaly-based IDS (Intrusion Detection System) uses machine learning techniques to detect and classify attacks in IoT networks. In the presence of unpredictable network technologies and various intrusion methods, traditional machine learning techniques appear inefficient. In many research areas, deep learning methods have shown their ability to identify anomalies accurately. Convolutional neural networks are an excellent alternative for anomaly detection and classification due to their ability to automatically categorize main characteristics in input data and their effectiveness in performing faster computations. In this paper, we design and develop a novel anomaly-based intrusion detection model for IoT networks. First, a convolutional neural network model is used to create a multiclass classification model. The proposed model is then implemented using convolutional neural networks in 1D, 2D, and 3D. The proposed convolutional neural network model is validated using the BoT-IoT, IoT Network Intrusion, MQTT-IoT-IDS2020, and IoT-23 intrusion detection datasets. Transfer learning is used to implement binary and multiclass classification using a convolutional neural network multiclass pre-trained model. Our proposed binary and multiclass classification models have achieved high accuracy, precision, recall, and F1 score compared to existing deep learning implementations.

As cyberspace continues to expand and diversify, protecting networked systems from malicious activity remains a fundamental challenge across science and engineering. Intrusion detection systems (IDSs) play a critical role in maintaining the security and reliability of modern digital infrastructure. Many existing anomaly-based intrusion detection systems rely on traditional machine learning techniques, such as clustering and decision trees, to identify deviations from normal traffic behavior. While effective in static settings, these approaches often struggle to adapt to evolving threats and complex, nonlinear patterns in network traffic, and they frequently require significant manual model design. The problem addressed in this paper is how to design an intrusion detection approach that can autonomously adapt its internal structure to better capture evolving patterns in network traffic. Here we show that a neuroevolutionary approach based on Hypercube-based NeuroEvolution of Augmenting Topologies (HyperNEAT) can evolve neural network architectures that improve anomaly detection without manual architectural tuning. Building on prior work using NEAT for intrusion detection, this study investigates HyperNEAT’s ability to exploit geometric relationships among network features through Compositional Pattern Producing Networks (CPPNs). Using the NSL-KDD dataset and three distinct fitness formulations, we evaluate HyperNEAT’s effectiveness in detecting anomalous traffic and compare its performance to traditional machine learning–based IDS methods. The results quantify the extent to which geometry-aware neuroevolution improves detection capability. More broadly, this work demonstrates the potential of neuroevolutionary algorithms to serve as adaptive, self-organizing alternatives to manually designed IDS models. By reducing reliance on predefined architectures, HyperNEAT offers a promising direction for building intrusion detection systems that can respond more effectively to emerging and unknown cyber threats.

Intrusion detection (ID) is an important component of infrastructure protection mechanisms. Intrusion detection systems (IDSs) need to be accurate, adaptive, and extensible. Given these requirements and the complexities of today's network environments, we need a more systematic and automated IDS development process rather that the pure knowledge encoding and engineering approaches. This article describes a novel framework, MADAM ID, for Mining Audit Data for Automated Models for Instrusion Detection. This framework uses data mining algorithms to compute activity patterns from system audit data and extracts predictive features from the patterns. It then applies machine learning algorithms to the audit records taht are processed according to the feature definitions to generate intrusion detection rules. Results from the 1998 DARPA Intrusion Detection Evaluation showed that our ID model was one of the best performing of all the participating systems. We also briefly discuss our experience in converting the detection models produced by off-line data mining programs to real-time modules of existing IDSs.

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.

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.

The increasing counterfeit of the internet usage has raised concerns of the security agencies to work very hard in order to diminish the presence of the abnormal users from the web. The motive of these illicit users (called intruders) is to harm the system or the network either by gaining access to the system or prohibiting genuine users to access the resources. Hence in order to tackle the abnormalities Intrusion Detection System (IDS) with Data Mining has evolved as the most demanding approach. On the one end IDS aims to detect the intrusions by monitoring a given environment while on the other end Data Mining allows mining of these intrusions hidden among genuine users. In this regard, IDS with Data Mining has been through several revisions in consideration to meet the current requirements with efficient detection of intrusions. Also several models have been proposed for enhancing the system performance. In context to improved performance, the paper presents a new model for intrusion detection. This improved model, named as REP (Reduced Error Pruning) based Intrusion Detection Model results in higher accuracy along with the increased number of correctly classified instances.

With the widespread use of Internet, the possibilities of exposing confidential data to invaders or attackers increases. Intrusion Detection System (IDS) is used for detecting various intrusions in network environment and to prevent data from malicious attackers. In this paper, a combined algorithm based on Principal Component Analysis (PCA) and Core Vector Machine (CVM), which is an extremely fast classifier, is proposed for intrusion detection. PCA is used as feature extraction technique to select principal features from the intrusion detection KDDCup'99 dataset and an intrusion detection model is constructed by CVM algorithm. The effectiveness of the features selected is also tested on ensemble based classifiers and the results are compared with the standard classifiers.

The increasing popularity of internet of things (IoT), dissimilar networks, distributed devices, and applications has turned out to be a major call for the identification of novel security threats and tracing malicious network behaviours. An intrusion detection system (IDS) is a self-defense tool for preventing several types of cyberattacks. Latest machine learning (ML) methods are becoming the backbone for constructing intelligent IDS that are highly data driven. This chapter proposes decision tree based IDS for the dataset NSL-KDD. A novel approach has been developed for the ranking of security features. The proposed system has been validated against performance evaluation metrics consisting of recall, precision, accuracy, and F score. The results produced by the proposed system are compared with well-known ML methods including logistic regression, support vector machines and K-nearest neighbor in order to analyse the efficiency.