Adaptive Network Intrusion Detection Research Articles

Designing IDS to Analyze Malicious Attacks using Machine Learning

Abstract: This study presents a novel approach to enhance network security through the design and implementation of an Intrusion Detection System (IDS) employing machine learning (ML) techniques. The primary aim is to address current challenges in real-time threat detection by seamlessly integrating ML models, including Logistic Regression, Random Forest, and XGBoost. The research tackles drawbacks in existing systems, emphasizing the struggle with real-time threat detection, the complexity of ML integration. The working model classifies incoming requests as normal or intrusions, categorizing the latter into DDoS, R2L, U2R, or Probing attacks. Leveraging insights from four referenced papers, this study builds on efficient activation functions, optimized feature selection, and empirical analyses of ML models for adaptive network intrusion detection. Results showcase promising comparisons between ML algorithms which include Logistic Regression, Random Forest, XG Boost

ADCL: Toward an Adaptive Network Intrusion Detection System Using Collaborative Learning in IoT Networks

With the widespread of cyber attacks, network intrusion detection system (NIDS) is becoming an important and essential tool to protect Internet of Things (IoT) environments. However, it is well-known that the NIDS performance depends heavily on the effectiveness of detection model, which can be influenced significantly by the learning mechanism and the available training data. Many existing studies try to mitigate the above challenges, but few of them consider the adaptability and the cost of deploying an NIDS, the integrity of learning process, the capacity of model based on concrete traffic samples at the same time. To fill this gap and improve the detection performance, we propose a collaborative learning based detection framework called ADCL, which can mitigate the limitations on the knowledge of a single model by leveraging multiple models trained in similar environments and detecting intrusions in a collaborative manner. Our evaluation results indicate that ADCL can provide better performance compared with a single model on detecting various attacks in IoT networks. Specifically, ADCL improves F-score by up to 80% for adaptability, 42% in mitigating the reliance on learning integrity, 85% for model capacity. Furthermore, the detection results of ADCL guide those single models to update and increase the F-score by 15%.

A novel adaptive network intrusion detection system for internet of things.

Cyber-attack is one of the most challenging aspects of information technology. After the emergence of the Internet of Things, which is a vast network of sensors, technology started moving towards the Internet of Things (IoT), many IoT based devices interplay in most of the application wings like defence, healthcare, home automation etc., As the technology escalates, it gives an open platform for raiders to hack the network devices. Even though many traditional methods and Machine Learning algorithms are designed hot, still it "Have a Screw Loose" in detecting the cyber-attacks. To "Pull the Plug on" an effective "Intrusion Detection System (IDS)" is designed with "Deep Learning" technique. This research work elucidates the importance in detecting the cyber-attacks as "Anomaly" and "Normal". Fast Region-Based Convolution Neural Network (Fast R-CNN), a deep convolution network is implemented to develop an efficient and adaptable IDS. After hunting many research papers and articles, "Gradient Boosting" is found to be a powerful optimizer algorithm that gives us a best results when compared to other existing methods. This algorithm uses "Regression" tactics, a statistical technique to predict the continuous target variable that correlates between the variables. To create a structured valid dataset, a stacked model is made by implementing the two most popular dimensionality reduction techniques Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) algorithms. The brainwaves made us to hybridize Fast R-CNN and Gradient Boost Regression (GBR) which reduces the loss function, processing time and boosts the model's performance. All the above said methods are trained and tested with NIDS dataset V.10 2017. Finally, the "Decision Making" model decides the best result by giving an alert message. Our proposed model attains a high accuracy of 99.5% in detecting the "Cyber Attacks". The experiment results revealed that the effectiveness of our proposed model surpasses other deep neural network and machine learning techniques which have less accuracy.

A real-time adaptive network intrusion detection for streaming data: a hybrid approach

A real-time adaptive network intrusion detection for streaming data: a hybrid approach

Model design artificial intelligence and research of adaptive network intrusion detection and defense system using fuzzy logic

Along with improvement of technology in network and continuous expansion of network economy and network applications, the Internet has gradually become an indispensable part of the modern society. However, an endless stream of hacker attacks and network virus events make network security issues stand out. Therefore, network security has become a hot spot in computer network research and development. This paper aims at establishing a real-time detection and dynamic defense security system and makes an in-depth study of intrusion detection technology and defense decision-making technology. The strategy involved in finding the intrusion behavior since the fuzzy base contains the better group of rules. We have utilized an automated fuzzy rule generation strategy. An adaptive network intrusion detection and defense system model is established, and the architecture of the model is discussed in detail. The platform independence, good self-adaptability, expansibility, multi-level data analysis and dynamic defense decision-making are expounded. The experiment proves that the model proposed in this article has a good self-adaptability and open construction, and effectively combines the functions of intrusion detection and defense decision-making.

Adaptive intrusion detection via GA-GOGMM-based pattern learning with fuzzy rough set-based attribute selection

In this paper, an adaptive network intrusion detection method using fuzzy rough set-based feature selection and GA-GOGMM-based pattern learning is presented. Based on the fuzzy rough set theory, the optimal attribute subset of network connection records is achieved by the information gain ratio criterion in advance. A greedy algorithm-based global optimal Gaussian mixture model (GMM) clustering method, termed GA-GOGMM, is introduced, to extract the intrinsic structure of network instances to achieve highly-discernable and stable normal and intrusion pattern libraries for the subsequent network intrusion detection (NID). GA-GOGMM-based pattern learning can achieve the optimal GMM of network traffic instances for the pattern clustering while avoiding the negative effect of the empirical initialization of clustering numbers and random initialization of clustering centers with a low computational complexity. An adaptive model updating mechanism is further introduced for the online updating of normal and intrusion pattern libraries to ensure the adaptability of the NID model. Extensive validation and comparative experiments, conducted on a benchmark dataset NSL-KDD and a self-built Nidsbench-based network simulation platform, show that the proposed ANID approach leads to a significant improvement in detection accuracies with low false alarms and missing reports on both known and unknown attacks. It can effectively adapt to the dynamic changing network environments with high detection accuracy and low false alarm rate as well as low missing reporting rate, which has significant application prospects.

ANID-SEoKELM: Adaptive network intrusion detection based on selective ensemble of kernel ELMs with random features

This paper presents an adaptive network intrusion detection (ANID) method based on the selective ensemble of kernel extreme learning machines (KELMs) with random features (termed ANID-SEoKELM), aiming at identifying various unauthorized uses, misuses and abuses of computer systems in real time. To generate a lightweight intrusion detector, multiple KELMs are learned independently based on the Bagging strategy with sparse random feature representation (SRFR), to reduce noise and redundant or irrelevant information in network connection instances and ensure the diversity of base learners for the effective ensemble of base learners. A marginal distance minimization (MDM)-based selective ensemble (MDMbSE) method is introduced to generate the ultimate intrusion detector. To ensure the adaptability of the intrusion detector, an incremental learning-based detection-model updating procedure is also derived. Extensive validation and comparative experiments on the benchmark KDD99 dataset and a hybrid heterogeneous network simulation platform mixed with wireless networks and Ethernet networks demonstrate that the ANID-SEoKELM is able to adapt to the dynamically changing network environments hence it can achieve higher detection accuracies stably and efficiently than classic single learner-based intrusion detection methods and representative ensemble-based intrusion detection methods.

Adaptive Network Intrusion Detection and Mitigation Model using Clustering and bayesian Algorithm in a Dynamic Environment

Adaptive Network Intrusion Detection and Mitigation Model using Clustering and bayesian Algorithm in a Dynamic Environment

A New Negative Selection Algorithm for Adaptive Network Intrusion Detection System

Negative Selection Algorithm (NSA) is one of the widely used techniques for Intrusion Detection Systems (IDS) designing. In this paper, the proposed is an IDS based on a new model of NSA namely HNSA-IDSA (Hybrid NSA for Intrusion Detection System Adaptation). The proposed system can detect unknown attacks; moreover can be adapted automatically when new profiles' changes of the system are detected. To determine the efficiency of the proposed approach, the standard KDD99 dataset was used for performing experiments. The obtained results show that the authors' mechanism outperforms some literature techniques providing variant important properties as high detection rate, low false positive, adaptability and new attacks detection.

Combining Naive Bayes and Decision Tree for Adaptive Intrusion Detection

In this paper, a new learning algorithm for adaptive network intrusion detection using naive Bayesian classifier and decision tree is presented, which performs balance detections and keeps false positives at acceptable level for different types of network attacks, and eliminates redundant attributes as well as contradictory examples from training data that make the detection model complex. The proposed algorithm also addresses some difficulties of data mining such as handling continuous attribute, dealing with missing attribute values, and reducing noise in training data. Due to the large volumes of security audit data as well as the complex and dynamic properties of intrusion behaviours, several data miningbased intrusion detection techniques have been applied to network-based traffic data and host-based data in the last decades. However, there remain various issues needed to be examined towards current intrusion detection systems (IDS). We tested the performance of our proposed algorithm with existing learning algorithms by employing on the KDD99 benchmark intrusion detection dataset. The experimental results prove that the proposed algorithm achieved high detection rates (DR) and significant reduce false positives (FP) for different types of network intrusions using limited computational resources.