An adaptive distributed intrusion detection system in local network: Hybrid classification methods

The complexity of handling modern apps is causing older network architectures to struggle. As a result, Software Defined Networking (SDN) has developed into a paradigm-shifting network framework that enables administrators to exercise programmable control over whole network infrastructures. This development makes it easier to configure and operate large networks, but it also adds vulnerabilities, including the possibility of Distributed Denial of Service (DDoS) assaults that could take down the SDN controller. Integrating security tools like firewalls, intrusion detection and prevention systems, and anti-DDoS solutions is crucial to reducing this danger. In the context of SDN, leveraging the analytical power of Machine Learning (ML) algorithms to analyse network traffic patterns is a key tactic for fending off DDoS attacks. Effective defence systems are supported by these ML models, which can distinguish between normal network behaviour and DDoS assault traffic with ease. Decision trees (DT), support vector machines (SVM), and artificial neural networks (ANN) are examples of supervised ML techniques that have evolved into crucial tools in this field. These algorithms are developed utilising datasets with annotations that include examples of both legitimate and malicious network traffic. Once trained, the models may be used to instantly categorise incoming traffic, making it easier to identify and contain any DDoS attacks in real-time.

As organizations increasingly rely on network services, the prevalence and severity of Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks have emerged as significant threats. The cornerstone of effectively addressing these challenges lies in the timely and precise detection capabilities offered by advanced intrusion detection systems (IDS). Hence, an innovative IDS framework is introduced that seamlessly integrates the extended Berkeley Packet Filter (eBPF) with powerful machine learning algorithms—specifically Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and TwinSVM—enabling unparalleled real‐time detection of DDoS attacks. This cutting‐edge solution provides a robust and scalable IDS framework to combat DoS and DDoS threats with high efficiency, leveraging eBPF's capabilities within the Linux kernel to bypass typical user space constraints. The methodology encompasses several key steps: (a) Collection of data from the renowned CIC‐IDS‐2017 repository; (b) Processing the raw data through a meticulous series of steps, including transmission, cleaning, reduction, and discretization; (c) Utilizing an ANOVA F‐test for the extraction of critical features from the preprocessed data; (d) Application of various ML algorithms (DT, RF, SVM, and TwinSVM) to analyze the extracted features for potential intrusion; (e) Implementing an eBPF program to capture network traffic and harness trained model parameters for efficient attack detection directly within the kernel. The experimental results reveal outstanding accuracy rates of 99.38%, 99.44%, 88.73%, and 93.82% for DT, RF, SVM, and TwinSVM, respectively, alongside remarkable precision values of 99.71%, 99.65%, 84.31%, and 98.49%. This high‐speed, accurate detection model is ideally suited for high‐traffic environments such as data centers. Furthermore, its foundational architecture paves the way for future advancements, including the potential integration of eBPF with XDP to achieve even lower‐latency packet processing. The experimental code is available at the GitHub repository link: https://github.com/NemalikantiAnand/Project .

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.

Distributed Denial of Service (DDoS) persists in Online Applications as One of those significant threats. Attackers can execute DDoS by the more natural steps. Then with the high productivity to slow the consumer access services down. To detect an attack on DDoS and using machine learning algorithms. The Overseen to detect and mitigate the attack, machine learning algorithms such as Naive Bayes, decision tree, k-nearest neighbours (k-NN) and random forest are used. There are three steps: gathering information, preprocessing and feature Extraction in "Normal or DDoS" classification algorithm for detection Attack use Dataset NSL-KDD. Similar algorithms have different functions Conduct that is dependent on the features selected. DDOS-attack performance Detection is compared, and it indicates the best algorithm. Attempts at Distributed Denial of Service ( DDoS) Were the most powerful attacks of the last period. A Virtual Network. The intrusion detection system (NIDS) should be designed seamlessly to Fight the latest strategies and trends of those attackers NIDS on DDoS. In this paper, we propose an NIDS capable of detecting Current DDoS attacks, as well as new forms. The main characteristic of Our NIDS is the combination of various classifiers using an ensemble Models, with the concept of each classifier being able to target different Aspects/types of intrusions, and more effective in doing so Mechanism for protecting against new intrusions. Additionally, we perform a detailed study of and based on, DDoS attacks check the reduced set of functions [27, 28] to be essential to Enhance accuracy. We are playing with and analyzing NSL-KDD Dataset with a feature set reduced, and our proposed NIDS will Detect 99.1 per cent of active DDoS attacks. Let's compare our Tests with other methods which already exist. Our approach to NIDS has Able to learn to keep up with existing and evolving DDoS Attack trends.

DDoS (Distributed Denial of Service) attacks consist of a large number of compromised computer systems that launch joint attacks at a targeted victim, such as a server, website, or other network equipment, simultaneously. DDoS has become a widespread and severe threat to the integrity of computer networks. DDoS can lead to system paralysis, making it difficult to troubleshoot. As a key building block in the development of an integrated defense system, it is essential to detect DDoS attacks as early as possible. With the popularization of artificial intelligence, more and more researchers apply machine learning (ML) and deep learning (DL) to the detection of DDoS attacks and have achieved satisfactory accomplishments. The complexity and sophistication of DDoS attacks have kept increasing and evolving since the first DDoS attack was reported in 1996. Regarding the headways in this problem, a new type of DDoS attack, named adversarial DDoS attack, is investigated in this study. The generating adversarial DDoS traffic is carried out using a symmetric Generative Adversarial Networks (GAN) architecture called Cycle-GAN to demonstrate the severe impact of adversarial DDoS attacks. Experiment results reveal that the synthesized traffic can easily penetrate ML-based detection systems, including Random Forest, KNN (k-Nearest Neighbor), SVM (Support Vector Machine), and Naïve Bayes. These alarming results intimate the urgent need for countermeasures against adversary DDoS attacks.

The substantial growth of Internet-connected devices within the Internet of Things (IoT) has given rise to significant security challenges. Among the various threats confronting these interconnected devices, Denial of Service (DoS)/Distributed Denial of Service (DDoS) attacks emerge as significant concerns. The attacks, which seek to disrupt IoT services by flooding networks with unnecessary traffic, there is a critical need for robust security measures. Intrusion Detection Systems (IDS) are vital in identifying suspicious activities, yet many existing systems lack real-time capabilities to address evolving attack strategies. This study investigates the vulnerabilities of IoT networks and the pressing need to detect and classify DoS/DDoS attacks in real time. Traditional IDS, while effective in recognizing known attack patterns, fall short in identifying new attack types due to their reliance on historical data. To bridge this gap, this research focuses on developing an enhanced hybrid network intrusion detection and classification system. This study seeks to make contribution to the advancement of resilient security measures within IoT environments. The framework uses the Kth Nearest Neighbor (KNN) algorithm and dense neural networks to efficiently detect and categorize DoS/DDoS attacks in real time. To achieve this, a simulation model implementing the proposed hybrid algorithm will be developed. The framework utilized the MQTT-IoT-IDS2020 dataset, and a comparison was presented with recent approaches found in the literature. The results demonstrated enhanced performance in terms of true positive rate, accuracy, speed of detection.

Machine learning (ML) techniques are essential in the detection of network attacks and enhancing network security. A device or software that recognizes any unusual pattern in the network and alerts the user about the same is an intrusion detection system (IDS). In this chapter, we have described the use of ML classification algorithms on the UNSW-NB15 dataset, leading to the generation of a network intrusion detection model which classifies incoming traffic into malicious or non-malicious traffic and issues an alert to the user. We have implemented the following ML algorithms – support vector machine, artificial neural network, and one-class support vector machine with an average accuracy of 89.25%, 91.54%, and 93.05%, respectively. Two graphical user interfaces (online and offline versions) have been developed for the system. Thus, the chapter proposes an optimized intrusion detection system that improves upon the existing intrusion detection systems which detect malicious packets in the network.

In this paper, a new intrusion detection system (IDS) is presented to deal with distributed denial of service (DDoS) attacks. A combined algorithm based on Harris Hawks Optimization (HHO) and Dragonfly Algorithm (DA) is proposed to select relevant features and eliminate irrelevant and redundant features from the NSL-KDD dataset. The extracted features are presented to a multilayer perceptron (MLP) neural network. This network (as a classifier) divides the network traffic into two classes, normal and attack categories. Performance of the proposed model is evaluated with two standard and widely-used datasets in the field of intrusion detection: NSL-KDD and UNSW-NB15. The results of the simulations clearly show the superiority of the proposed method compared to the previous methods in terms of critical evaluation criteria such as accuracy, precision, recall, and F-Measure. Specifically, the proposed method exhibited improvements of 96.9%, 97.6%, 96%, and 96.8% in these metrics, respectively (compared to the baseline method). The main reason for these improvements is the ability of the combined algorithm to intelligently select the optimal features and reduce the dimensions of the data. This careful selection of features allows the MLP neural network to focus on critical information, increasing the classification accuracy and ultimately improving the performance of the intrusion detection system. This research showed that combining optimization algorithms and machine learning works well. So, it is effective for tackling DDoS attacks. It can lead to better intrusion detection systems. These systems will be more efficient and accurate.

<p>Cyber-attacks are rapidly increasing in the internet era due to the growth of information technology. The distributed denial of service (DDoS) attacks are increasing dramatically due to the distributed services in cloud networks. In this paper, a new Intrusion Detection System (IDS) is proposed to improve the performance of the networks by detecting DDoS attacks effectively in wireless networks. In this work, we propose a new feature selection method called Split Filter Feature Selection and Spotted Hyena Optimization Based Feature Optimization Method (SFSH-FOM) to select the most contributed features that are helpful for enhancing the classification accuracy. In this work, a new deep learning algorithm named Fuzzy Temporal Features incorporated Convolutional Neural Network (FT-CNN) is proposed for performing effective classification. Here, a new cross layer feature fusion technique is also proposed by using FT-CNN and LSTM for enhancing the performance. The experiments have been carried out to evaluate the proposed IDS using the standard datasets, namely the KDD’99 dataset, the NSL-KDD dataset, and the DDoS dataset by considering the evaluation metrics such as detection accuracy, recall, precision, and F1-score, and it has also been proved as better than other IDSs in terms of accuracy and false alarm rate.</p> <p> </p>

Intrusion detection systems (IDS) are used in analyzing huge data and diagnose anomaly traffic such as DDoS attack; thus, an efficient traffic classification method is necessary for the IDS. The IDS models attempt to decrease false alarm and increase true alarm rates in order to improve the performance accuracy of the system. To resolve this concern, three machine learning algorithms have been tested and evaluated in this research which are decision jungle (DJ), random forest (RF) and support vector machine (SVM). The main objective is to propose a ML-based network intrusion detection system (ML-based NIDS) model that compares the performance of the three algorithms based on their accuracy and precision of anomaly traffics. The knowledge discovery in databases (KDD) methodology and intrusion detection evaluation dataset (CIC-IDS2017) are used in the testing which both are considered as a benchmark in the evaluation of IDS. The average accuracy results of the SVM is 98.18%, RF is 96.76% and DJ is 96.50% in which the highest accuracy is achieved by the SVM. The average precision results of the SVM is 98.74, RF is 97.96 and DJ is 97.82 in which the SVM got a higher average precision compared with the other two algorithms. The average recall results of the SVM is 95.63, RF is 97.62 and DJ is 95.77 in which the RF achieves the highest average of recall than SVM and DJ. In overall, the SVM algorithm is found to be the best algorithm that can be used to detect an intrusion in the system.

Critical infrastructure protection and national security are enhanced by the security and reliability of networks. Various types of information circulate on these networks, ranging in classification from open to closed. The consequences of cyberattacks on these networks can be severe, including reputational damage, financial loss, operational disruption and data leakage. Traditional security methods, such as firewalls and anti-virus software, are becoming less effective against modern and ever-changing cyber threats. As a result, powerful network intrusion detection systems (IDS) have become indispensable for proactive detection and mitigation of cyber attacks. Machine learning has become a viable method for creating adaptive intrusion detection tools that can detect new and complex attack patterns. By learning from huge labelled network traffic datasets, ML models can understand the subtle patterns and differentiating features of normal and abnormal or malicious traffic flows. This allows them to detect possible cyber threats and intrusions that traditional signature-based IDSs cannot detect. Extracting discriminative features and training appropriate classification models from such data is a challenging task. In the presented study, we analyse the effectiveness of ML algorithms for detecting cyberattacks, in particular distributed denial of service (DDoS) attacks, in network traffic data. In the presented study, a network attack detection system is developed using ML and deep learning (DL) models and experimented on the CICIDS2017 dataset. The main objectives of the study are to develop a strategy for extracting valuable information from raw network streams; to study the impact of data preparation on the false positive rate; and to conduct a comparative analysis of ML models for cyberattack detection. The main goal of the study is to provide an understanding of the development of a reliable adaptive network intrusion detection system using ML approaches that increase cybersecurity capabilities and protect against future cyberattacks.

Distributed denial of service attacks (DDoS) are becoming a big threat to the Internet. Recently, some DDoS attacks have infected more than 100,000 vulnerable hosts over Internet within 10 minutes. Consequences of these attacks can be devastating toward many companies whose security policy against this kind of attacks relies only on reconfiguring firewalls. It is judicious to note that no computer network is immune from intrusions in general and distributed denial of service attacks in particular. Intrusion detection systems should be geographically distributed to detect distributed and cooperated attacks. In this paper, we use a cooperative approach, which uses the Intrusion Detection Message Exchange Format (IDMEF) defined by the IETF, that can detect coordinated attack scenarios through alert correlation of distributed IDSs. We present our experience in realizing this cooperative system and the different results obtained from its implementation in a real network.

In the ever-evolving landscape of network security, the sophistication of cyber-attacks, especially Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks, poses a formidable challenge to intrusion detection systems. Recognizing the longstanding application of CatBoost in various domains, this study explores its novel optimization for network intrusion detection, a critical area in need of advanced solutions. Leveraging the strengths of CatBoost in handling categorical data and imbalanced datasets, we meticulously adapt the classifier to meet the complex demands of distinguishing between DoS, DDoS, and benign traffic within the comprehensive CICIDS2017 and CSE-CIC-IDS2018 datasets. This research is an attempt to refine the learning efficiency and detection capabilities of CatBoost through the implementation of advanced feature selection and data preparation, contributing to the field by improving detection accuracy within real-time intrusion detection systems. The results show a notable improvement in performance, underscoring the classifier's role in advancing cybersecurity measures. Furthermore, the study paves the way for future exploration into adversarial machine learning and automated feature engineering, fortifying the resilience and adaptability of intrusion detection systems against the backdrop of a rapidly changing cyber threat landscape. These efforts provide solid approaches to address the current challenges in network security, signaling a move towards more refined and dependable intrusion detection methods.

By quick computer and communication technology improvement, Distributed Denial of Service (DDoS) attack harm is getting more important. DDoS attacks research is the important study domain; a number of methods exist which have been presented like the algorithm of evolutionary as well as artificial intelligence in literature to diagnose attacks of DDoS. Unfortunately, new popular models of DDoS diagnosis are deteriorating for validating DDoS attacks objective and prior identification. Because of DDoS attack modes diversity as well as various attack traffic amount, still there is not the technique of diagnosis with promising accuracy of diagnosis currently. By choosing the best subset of features, a feature selection (FS) approach helps to shorten computing times and increase computational complexity. For mitigating attacks of denial of service, this paper applies honey badger algorithm (HBA) with algorithm of machine learning known as HBIDS. Present strategy is given the making intrusion detection system (IDS) for meeting controlled area needs and could recognize between attack and normal traffics. Moreover, HBIDS chooses the most related from basic dataset of IDS which could aid recognizing normal low-speed DDoS attacks, then chosen features are conveyed to classifiers like decision tree, multilayer perceptron, na\"ive Bayes, and support vector machine for identifying attack kind. Generally accessible dataset as CIC-IDS 2017 and KDD Cup 99 are applied for our experimental research. From simulation outcomes, this is obvious that HBIDS with decision tree needs high diagnosis with the low false–positive rate (0.001) and accuracy (99.9).

The rapid growth of the internet and the increasing reliance on digital infrastructures have posed significant challenges to cybersecurity. Among the other variants of attacks, Distributed Denial of Service (DDoS) attacks have emerged as one of the most destructive and common threats. These attacks disrupt or slow down network services by overwhelming the network infrastructure with a massive volume of malicious traffic. To effectively identify and mitigate DDoS attacks, machine learning techniques have been extensively employed in intrusion detection systems. Machine learning approaches offer the advantage of automating the detection process by learning patterns and characteristics of DDoS attacks from historical data. Researchers have explored various machine learning algorithms such as K-Nearest Neighbours (KNN), Support Vector Machine (SVM), Random Forest (RF), and Naïve Bayes to classify and detect DDoS attacks. These algorithms leverage features extracted from network traffic data, including packet size, packet delay patterns, and traffic behaviour, to differentiate between normal and malicious traffic.