Using Naive Bayes with AdaBoost to Enhance Network Anomaly Intrusion Detection

Anomaly intrusion detection normally has high false alarm rates, and a high volume of false alarms will prevent system administrators identifying the real attacks. Machine learning methods provide an effective way to decrease the false alarm rate and improve the detection rate of anomaly intrusion detection. In this research, we propose a novel approach using kernel methods and Support Vector Machine (SVM) for improving anomaly intrusion detectors' accuracy. Two kernels, STIDE kernel and Markov Chain kernel, are developed specially for intrusion detection applications. The experiments show the STIDE and Markov Chain kernel based two class SVM anomaly detectors have better accuracy rate than the original STIDE and Markov Chain anomaly detectors. Generally, anomaly intrusion detection approaches build normal profiles from labeled training data. However, labeled training data for intrusion detection is expensive and not easy to obtain. We propose an anomaly detection approach, using STIDE kernel and Markov Chain kernel based one class SVM, that does not need labeled training data. To further increase the detection rate and lower the false alarm rate, an approach of integrating specification based intrusion detection with anomaly intrusion detection is also proposed. This research also establish a platform which generates automatically both misuse and anomaly intrusion detection software agents. In our method, a SIFT representing an intrusion is automatically converted to a Colored Petri Net (CPNs) representing an intrusion detection template, subsequently, the CPN is compiled into code for misuse intrusion detection software agents using a compiler and dynamically loaded and launched for misuse intrusion detection. On the other hand, a model representing a normal profile is automatically generated from training data, subsequently, an anomaly intrusion detection agent which carries this model is generated and launched for anomaly intrusion detection. By engaging both misuse and anomaly intrusion detection agents, our system can detect known attacks as well as novel unknown attacks.

The rapid increasing of security incidents imposes a great burden on Internet users and system administrators. In this paper we discuss a parallel analysis for lightweight network incident detection using nonlinear adaptive systems. We run AID (anomaly intrusion detection) and MID (misuse intrusion detection) systems in parallel. Two detectors generate binary output misuse = {YES/NO} and anomaly = {YES/NO}. Then, we can determine whether we need to perform network or security operation. We apply clustering algorithm for AID and classification algorithm for MID. The nonlinear adaptive system is trained for running MID and AID in parallel. Proposed parallel system is more lightweight and simple to operate even if the number of incident patterns is increased. Experimental results in the case where false positive is frequently caused show that our method is functional with a recognition rate of attacks less than 10%, while finding the anomaly status. Also, performance evaluation show that proposed system can work with reasonable CPU utilization compared with conventional serial search based system.

With the enlarged utilization of computer networks, security has become one of the critical issues. A network intrusion by malicious or unauthorized consumers may cause severe interruption to networks. So, the progress of a strong and dependable network intrusion detection system (IDS) is gradually significant. Intrusion detection relates to a suite of models employed to recognize attacks against network infrastructures and computers. There are dual main intrusion detection models, such as misuse and anomaly detection. Anomaly detection is a central part of intrusion detection in which disruptions of normal behaviour propose the presence of unintentionally or intentionally induced attacks, defects, faults, etc. With the arrival of anomaly-based IDS, many models have progressed in tracking new threats to the systems. Machine learning (ML) and deep learning (DL) models are currently leveraged for anomaly intrusion detection in cybersecurity. This manuscript proposes an Enhanced Anomaly Intrusion Detection using an Optimization Algorithm with Dimensionality Reduction and Hybrid Model (EAID-OADRHM) technique. The proposed EAID-OADRHM technique presents a new approach for perceiving and migrating attacks in cybersecurity. Min–max scaling normalization is primarily employed at the data pre-processing level to clean and transform input data into a consistent range. Furthermore, the proposed EAID-OADRHM technique utilizes the equilibrium optimizer (EO) model for the dimensionality reduction process. Additionally, the classification is performed by employing the long short-term memory and autoencoder (LSTM–AE) model. Finally, the improved Snow Ablation Optimizer (ISAO) model optimally tunes the hyperparameters of the LSTM–AE model, leading to enhanced classification performance. The simulation validation of the EAID-OADRHM approach is examined under the CIC-IDS2017 dataset, and the outcomes are computed using numerous measures. The experimental assessment of the EAID-OADRHM approach portrayed a superior accuracy value of 99.46% over existing methods in the anomaly intrusion detection process.

컴퓨터 네트워크의 확대 및 인터넷 이용의 급격한 증가에 따른 최근의 정보통신 기반구조는 컴퓨터 시스템의 네트워크를 통한 연결로 다양한 서비스를 제공하고 있다. 특히 인터넷은 개방형 구조를 가지고 있어 서비스 품질의 보장과 네트워크의 관리가 어렵고, 기반구조의 취약성으로 인하여 타인으로부터의 해킹 및 정보유출 둥의 위협으로부터 노출되어 있다. 보안 위협에 대한 능동적인 대처 및 침입 이후에 동일한 또는 유사한 유형의 사건 발생에 대해 실시간 대응할 수 있는 방법이 중요하게 되었으며 이러한 해결책으로서 침임 탐지 시스템에 대한 연구가 활발히 진행되고 있다. 본 논문에서는 지도학습 알고리즘이 의한 침입탐지 시스템의 성능을 향상시키기 위해서 불확실성을 해결하기 위한 방법인 퍼지를 적용한 뉴로-퍼지 모델의 이상 침입 탐지 시스템에 대해서 연구한다. 즉, 신경망 학습의 전달함수를 불확실성을 해결하기 위한 퍼지 멤버쉽 함수로 수정하여 지도학습을 수행하였다. 제안한 뉴로-퍼지기법을 DARPA 침입 데이터를 이용하여 오용 탐지의 한계성을 극복한 네트워크기반의 이상침입 탐지에 적용하여 성능을 검증하였다. By the help of expansion of computer network and rapid growth of Internet, the information infrastructure is now able to provide a wide range of services. Especially open architecture - the inherent nature of Internet - has not only got in the way of offering QoS service, managing networks, but also made the users vulnerable to both the threat of backing and the issue of information leak. Thus, people recognized the importance of both taking active, prompt and real-time action against intrusion threat, and at the same time, analyzing the similar patterns of in-trusion already known. There are now many researches underway on Intrusion Detection System(IDS). The paper carries research on the in-trusion detection system which hired supervised learning algorithm and Fuzzy membership function especially with Neuro-Fuzzy model in order to improve its performance. It modifies tansigmoid transfer function of Neural Networks into fuzzy membership function, so that it can reduce the uncertainty of anomaly intrusion detection. Finally, the fuzzy logic suggested here has been applied to a network-based anomaly intrusion detection system, tested against intrusion data offered by DARPA 2000 Intrusion Data Sets, and proven that it overcomes the shortcomings that Anomaly Intrusion Detection usually has.

Chapter 5 - Intrusion Prevention and Detection Systems

Intrusion detection is an emerging area of research in the computer security and net-works with the growing usage of internet in everyday life. Most intrusion detection systems (IDSs) mostly use a single classifier algorithm to classify the network traffic data as normal behavior or anomalous. However, these single classifier systems fail to provide the best possible attack detection rate with low false alarm rate. In this paper,we propose to use a hybrid intelligent approach using a combination of classifiers in order to make the decision intelligently, so that the overall performance of the resul-tant model is enhanced. The general procedure in this is to follow the supervised or un-supervised data filtering with classifier or cluster first on the whole training dataset and then the output are applied to another classifier to classify the data. In this re- search, we applied Neural Network with Supervised and Unsupervised Learning in order to implement the intrusion detection system. Moreover, in this project, we used the method of Parallelization with real time application of the system processors to detect the systems intrusions.Using this method enhanced the speed of the intrusion detection. In order to train and test the neural network, NSLKDD database was used. Creating some different intrusion detection systems, each of which considered as a single agent, we precisely proceeded with the signature-based intrusion detection of the network.In the proposed design, the attacks have been classified into 4 groups and each group is detected by an Agent equipped with intrusion detection system (IDS).These agents act independently and report the intrusion or non-intrusion in the system; the results achieved by the agents will be studied in the Final Analyst and at last the analyst reports that whether there has been an intrusion in the system or not. Keywords: Intrusion Detection, Multi-layer Perceptron, False Positives, Signature- based intrusion detection, Decision tree, Nave Bayes Classifier

Modern IoT devices are expanding digital interconnectivity at a very fast rate and, at the same time, intensifying the risks of possible device malfunctions and DDoS attacks. Classical intrusion detection systems (IDS), dependent on static rules and old datasets, are incapable of keeping up with the changing traffic behavior of modern IoT networks. This paper proposes an innovative Hybrid Machine Learning – Deep Learning (ML-DL) model which autonomously detects IoT anomalies by combining the interpretability of ML algorithms and the representational strength of DL architectures. The proposed framework combines Random Forest (RF) and Support Vector Machine (SVM) models with a two-stage fusion process involving a Convolutional Neural Network (CNN) on the CICIoT2023 dataset, collected through traffic network data from 105 IoT devices and 33 categories of diverse attacks. Evaluation of the model included accuracy, precision, recall, and F1-Score and was marked as a through-and-through rigorous experiment. The hybrid model achieved an accuracy of 99.1%, with precision and F1-Score also valued at 0.99, thus outperforming standalone ML and DL models. The hybridization, then, does enhance detection reliability and generalization while being computationally inexpensive for IoT edge deployment. Thus, this work lays down a practically implementable and scalable basis for real-time automation of malfunctioning and intrusion detection in the IoT space.

With the rapid expansion of digital networks and the increasing complexity of cyber threats, network security has emerged as a crucial field within information and communications technology (ICT). Modern organizations face growing risks from sophisticated cyber-attacks that threaten the confidentiality, integrity, and availability of critical systems. Intrusion Detection Systems (IDS) have become a foundational tool for identifying and mitigating unauthorized access and suspicious activities within networks. These systems are broadly categorized into Signature-Based Intrusion Detection Systems (SIDS), which rely on known threat patterns, and Anomaly-Based Intrusion Detection Systems (AIDS), which detect deviations from normal behavior to identify unknown attacks. Despite their strengths, both types face significant limitations, including high false-positive rates and challenges in real-time detection. This study explores the evolving landscape of network security threats and critically analyzes existing intrusion detection techniques. It highlights the integration of alert correlation and prediction mechanisms that enhance early detection and response capabilities. Furthermore, the study addresses key limitations and challenges in IDS implementation and identifies emerging trends aimed at improving scalability, intelligence, and adaptability of detection mechanisms. By evaluating current systems and proposing directions for future research, this work contributes to the development of more robust and proactive network defense strategies in an increasingly interconnected digital world.

Intrusion detection has been at the center of intense research in the last decade owing to the rapid increase of sophisticated attacks on computer systems. Typically, intrusion detection refers to a variety of techniques for detecting attacks in the form of malicious and unauthorized activities. There are three broad categories of detection approaches: (a) misuse-based technique that relies on pre-specified attack signatures, (b) anomaly-based approach, that typically depends on normal patterns classifying any deviation from normal as malicious; and (c) specification-based technique that although operates in a similar fashion to anomaly-based approach, employs a model of valid program behavior in a form of specifications requiring user expertise. When intrusive behavior is detected, it is desirable to take (evasive and/or corrective) actions to thwart attacks and ensure safety of the computing environment. Such countermeasures are referred to as intrusion response. Although the intrusion response component is often integrated with the Intrusion Detection System (IDS), it receives considerably less attention than IDS research owing to the inherent complexity in developing and deploying response in an automated fashion. As such, traditionally, triggering an intrusion response is left as part of the administrators responsibility, requiring a high-degree of expertise. In this work we present an integrated approach to intrusion detection and response based on the technique for monitoring abnormal patterns in the program behavior. The proposed model effectively combines the advantages of anomaly-based and specification-based approaches recognizing a known behavior through the specifications of normal and abnormal patterns and classifying unknown patterns using a machine-learning algorithm. Such combination not only allows adaptation of the specification-based detection to the new patterns, but also provides a method for automatic development of specifications. In addition to detection, our framework incorporates preemptive response. By preemption, we imply deploying response before a monitored pattern is classified completely as an intrusion. Such response deployment is likely to stop an intrusion before it can affect the system. However, preemption also inherently suffers from false positives; i.e., responses are deployed to deter correct execution which may look intrusive in its initial phase. To reduce false positives, we have developed a multi-phase response selection and deployment mechanism based on the evaluation of the cost information of the system damage caused by potential intrusion and candidate responses.

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

Network intrusion detection system is a significant guarantee means for network security. However, the current network intrusion detection system generally suffers from less prior knowledge which then leads to poor generalizing ability. Network intrusion detection system based on support vector machine is endowed with favorable generalizing ability even with small sample size (less prior knowledge). The paper first gives an introduction to the basic concept of intrusion detection and the basic principle of the classifier based on support vector machine, then discusses algorithm of support vector machine, and finally forms network intrusion detection system combining anomaly intrusion detection and misuse intrusion detection, based on support vector machine.

The weak foundation of the computing environment caused information leakage and hacking to be uncontrollable. Therefore, dynamic control of security threats and real-time reaction to identical or similar types of accidents after intrusion are considered to be important. As one of the solutions to solve the problem, studies on intrusion detection systems are actively being conducted. To improve the anomaly intrusion detection system using system calls, this study focuses on techniques of neural networks and fuzzy membership function using the Soundex algorithm which is designed to change feature selection and variable length data into a fixed length learning pattern. That is, by changing variable length sequential system call data into a fixed length behavior pattern using the Soundex algorithm, this study conducted neural networks learning by using a back-propagation algorithm and fuzzy membership function. The proposed method and N-gram technique are applied for anomaly intrusion detection of system calls using Sendmail data of UNM to demonstrate its performance.KeywordsWindow SizeIntrusion DetectionAnomaly DetectionSystem CallIntrusion Detection SystemThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

In the paper we discuss the intergration of an support vector machine (SVM) based anomaly detection system with an specification based intrusion detection system (IDS), where the specification based IDS improves the feature-selection function of the SVMs. We demonstrate through experimental results, that extended finite state machine (EFSA) based anomaly detectors performs better than either the EFSA and SVM anomaly detectors individually. Specifically the accuracy of detection improved and the time and space required for SVM learning reduced using the feature reduction based on EFSAs.

Storage systems are the next frontier for providing protection against intrusion. Since storage systems see changes to persistent data, several types of intrusions can be detected by storage systems. Intrusion detection (ID) techniques can be deployed in various storage systems. In this paper, we study how intrusions can be detected at the block storage level and in SAN environments. We propose novel approaches for storage-based intrusion detection and discuss how features of state-of-the-art block storage systems can be used for intrusion detection and recovery of compromised data. In particular we present two prototype systems. First we present a real time intrusion detection system (IDS), which has been integrated within a storage management and virtualization system. In this system incoming requests for storage blocks are examined for signs of intrusions in real time. We then discuss how intrusion detection schemes can be deployed as an appliance loosely coupled with a SAN storage system. The major advantage of this approach is that it does not require any modification and enhancement to the storage system software. In this approach, we use the space and time efficient point-in-time copy operation provided by SAN storage devices. We also present performance results showing that the impact of ID on the overall storage system performance is negligible. Recovering data in compromised systems is also discussed.

It is an important issue for the security of network to detect new intrusion attack and also to increase the detection rates and reduce false positive rates in Intrusion Detection System (IDS). Anomaly intrusion detection focuses on modeling normal behaviors and identifying significant deviations, which could be novel attacks. The normal and the suspicious behavior in computer networks are hard to predict as the boundaries between them cannot be well defined. We apply the idea of the Fuzzy Rough C-means (FRCM) to clustering analysis. FRCM integrates the advantage of fuzzy set theory and rough set theory that the improved algorithm to network intrusion detection. The experimental results on dataset KDDCup99 show that our method outperforms the existing unsupervised intrusion detection methods.