Development Of Intrusion Detection System Research Articles

Blockchain integration for in-vehicle CAN bus intrusion detection systems with ISO/SAE 21434 compliant reporting.

The development of Intrusion Detection Systems (IDS) for in-vehicle buses has gained a lot of momentum in recent years as the number of reported vulnerabilities and the degree of interconnectivity for modern vehicles are on the rise. Since intrusion detection is resource consuming, it can be performed on computationally capable Android head units that are now present inside vehicles. Moreover, these units are connected to the internet, which enables the use of more complex algorithms that run in cloud environments. In this work we develop one such approach: an IDS that consists of a locally installed copy, running on head units, and a centralized instance of it that runs in the cloud and monitors traffic for groups of similar vehicles. Additionally, the centralized instance is part of a cloud service for intrusion detection which is continuously updated with the most recent types of attacks. The classification results of the cloud-based service are further analyzed by an incident response team which confirms the presence of known attacks, analyzes new types of attacks and assesses their impact. The output of this activity is stored on the Blockchain as ISO/SAE 21434 compliant reports, ensuring the transparency and traceability of the reported incidents.

Enhanced Intrusion Detection with LSTM-Based Model, Feature Selection, and SMOTE for Imbalanced Data

This study introduces a sophisticated intrusion detection system (IDS) that has been specifically developed for internet of things (IoT) networks. By utilizing the capabilities of long short-term memory (LSTM), a deep learning model renowned for its proficiency in modeling sequential data, our intrusion detection system (IDS) effectively discerns between regular network traffic and potential malicious attacks. In order to tackle the issue of imbalanced data, which is a prevalent concern in the development of intrusion detection systems (IDSs), we have integrated the synthetic minority over-sampling technique (SMOTE) into our approach. This incorporation allows our model to accurately identify infrequent incursion patterns. The rebalancing of the dataset is accomplished by SMOTE through the generation of synthetic samples belonging to the minority class. Various strategies, such as the utilization of generative adversarial networks (GANs), have been put forth in order to tackle the issue of data imbalance. However, SMOTE (synthetic minority over-sampling technique) presents some distinct advantages when applied to intrusion detection. The SMOTE is characterized by its simplicity and proven efficacy across diverse areas, including in intrusion detection. The implementation of this approach is straightforward and does not necessitate intricate adversarial training techniques such as generative adversarial networks (GANs). The interpretability of SMOTE lies in its ability to generate synthetic samples that are aligned with the properties of the original data, rendering it well suited for security applications that prioritize transparency. The utilization of SMOTE has been widely embraced in the field of intrusion detection research, demonstrating its effectiveness in augmenting the detection capacities of intrusion detection systems (IDSs) in internet of things (IoT) networks and reducing the consequences of class imbalance. This study conducted a thorough assessment of three commonly utilized public datasets, namely, CICIDS2017, NSL-KDD, and UNSW-NB15. The findings indicate that our LSTM-based intrusion detection system (IDS), in conjunction with the implementation of SMOTE to address data imbalance, outperforms existing methodologies in accurately detecting network intrusions. The findings of this study provide significant contributions to the domain of internet of things (IoT) security, presenting a proactive and adaptable approach to safeguarding against advanced cyberattacks. Through the utilization of LSTM-based deep learning techniques and the mitigation of data imbalance using SMOTE, our AI-driven intrusion detection system (IDS) enhances the security of internet of things (IoT) networks, hence facilitating the wider implementation of IoT technologies across many industries.

An Artificial Neural Network Technique for Prediction of Cyber-Attack using Intrusion Detection System

An intrusion detection system, often known as IDS, is a piece of equipment or a piece of software that monitors a network or collection of devices in order to search for indications of possible intrusion. The frequency of cyber assaults has grown in recent years, and with it, the damage they do to society. The study of cyber security and the avoidance of cyber assaults, such as the use of intrusion detection as a defensive mechanism, is therefore needed. The internet services are widely used. Services based on computers, the internet, and other forms of technology are all considered part of the cyber world. The cyber world has advanced greatly thanks to new protocols and technologies. Cyber security is a major issue for every service that operates online. Network and host-based intrusion detection systems (NIDS/HIDS) are the backbones of any cyber security infrastructure. The NSL-KDD dataset is often used in algorithm research and verification and is widely employed in both the study and development of intrusion detection systems. In this study, we provide a neural network approach to intrusion detection system threat prediction. In this paper, the Python Spyder software is used for the simulation.

Design and Development of Intrusion Detection System for Wireless Sensor Network

Design and Development of Intrusion Detection System for Wireless Sensor Network

Research of Machine Learning Algorithms for the Development of Intrusion Detection Systems in 5G Mobile Networks and Beyond.

The introduction of fifth generation mobile networks is underway all over the world which makes many people think about the security of the network from any hacking. Over the past few years, researchers from around the world have raised this issue intensively as new technologies seek to integrate into many areas of business and human infrastructure. This paper proposes to implement an IDS (Intrusion Detection System) machine learning approach into the 5G core architecture to serve as part of the security architecture. This paper gives a brief overview of intrusion detection datasets and compares machine learning and deep learning algorithms for intrusion detection. The models are built on the basis of two network data CICIDS2017 and CSE-CIC-IDS-2018. After testing, the ML and DL models are compared to find the best fit with a high level of accuracy. Gradient Boost emerged as the top method when we compared the best results based on metrics, displaying 99.3% for a secure dataset and 96.4% for attacks on the test set.

Retracted: Development of intrusion detection system using machine learning for the analytics of Internet of Things enabled enterprises

AbstractThe above article from IET Communications, published online on 28 October 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Interim Editor‐in‐Chief, Jian Ren, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal's peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract.

Detecting abnormal behaviors by using intelligent system

With the rising use of Internet technologies around the world, the number of network intruders and attackers has skyrocketed. For this reason, introducing systems for detecting attacks within the network security measures stops hackers from gaining access to data. In order to detect various forms of assaults, the development of intrusion detection systems is quite crucial. The election of features and elimination of the unrelated information can improve the classifier's accuracy execution because the network traffic dataset contains many useful and unhelpful features. Along these lines, in this work, three hybrid strategies for selecting the feature were implemented, which include the constant feature by standard deviation, the Quasi constant by variance threshold, and the information gain. These approaches were used to order and rank features, after which the best higher ranking was selected for classification and intrusion detection. These features were tested on three classifiers long shortـterm memory, convolutional neural network, and CNN-LSTM. From the acquired results, a high level of attacks was detected, and classification accuracy was achieved by cross-breeding the selection of the different best features. Furthermore, the convolutional neural network classifier achieved the highest accuracy rate, which exceeded the value of 99.5%.

Machine Learning for IoT based networks intrusion detection: a comparative study

Internet of Things (IoT) refers to technologies that enable the connection of objects to the internet and also collect data with minimal human intervention. Nowadays IoT is applied to all areas, particularly in logistics, industry, and health. These new IoT applications expand the functionality of smart objects and also introduce security vulnerabilities. In this context, the development of intrusion detection systems (IDS) adopted to IoT networks has become an important field of research. Machine learning (ML) and deep learning (DL) approaches are solutions used by many researchers in IDS. In this work, we propose a state of the art on IoT network intrusion detection using ML techniques during the last few years. We aim to detect the most used and efficient machine learning techniques. To support and confirm the proposed state-of-the-art results, we carried out an experiment to compare the machine learning techniques extracted from our analysis on the same dataset. we also analyzed the performance and execution time of two feature selection techniques and a feature extraction technique for intrusion detection in the case of binary classification, and multi-classification (5 classes: Normal, DOS, Probe, U2R, and R2L), The experimental results reveal that the Decision Tree with Fisher score gave the best performance with an accuracy of 99.26% and a minimum prediction time of 0.4 seconds.

Development of Intrusion Detection System for Security Threats in Internet of Things Using Artificial Neural Network

Internet of things (IoT) is a revolutionary technology which changes our life and work. Many industry sectors such as manufacturing, transportation, utilities, health care, consumer electronics and automobiles are invested and adopted towards IoT technology. The major inconvenience with IoT is its safety, as it is prone to attack by hackers. Detection Systems are used to detect these intrusions to protect the information and communication systems. Hence it is essential to design an intrusion detection system for security threats of IoT networks. This paper focuses, on the development of Artificial Neural Network (ANN) based Intrusion Detection System for threat analysis in IoT network. KDD-99 data set with Denial of Service (DoS) type attack is used to train and test three different ANN models. In this research, a Feed Forward Back Propagation (FFBP) network is used to detect the DoS attack. The process of optimization of a FFBP network involves comparison of classification accuracy during both training and testing in terms of true positive and false positive rates. For the data set considered the optimised network has achieved 100% efficiency during both training and testing.

Comparison between Support Vector Machine and Fuzzy C-Means as Classifier for Intrusion Detection System

In this globalization era, cybercrime has been entering every aspect through internet network. The development of Intrusion Detection System (IDS) is being studied deeply to solve the problem. There are several classifier algorithms for Intrusion Detection System such as Support Vector Machine (SVM) and Fuzzy C-Means (FCM). In this study, we will compare proposed model using both Support Vector Machine and Fuzzy C-Means to find a better result that increase accuracy of the network attacks. KDD Cup 1999 will be used to evaluate which algorithms work best. The results are very encouraging and show that SVM and FCM can be a useful tool for intrusion detection system. We found that SVM achieved 94.43% average accuracy rate while FCM achieved 95.09% average accuracy rate.

A Hybrid Spectral Clustering and Deep Neural Network Ensemble Algorithm for Intrusion Detection in Sensor Networks.

The development of intrusion detection systems (IDS) that are adapted to allow routers and network defence systems to detect malicious network traffic disguised as network protocols or normal access is a critical challenge. This paper proposes a novel approach called SCDNN, which combines spectral clustering (SC) and deep neural network (DNN) algorithms. First, the dataset is divided into k subsets based on sample similarity using cluster centres, as in SC. Next, the distance between data points in a testing set and the training set is measured based on similarity features and is fed into the deep neural network algorithm for intrusion detection. Six KDD-Cup99 and NSL-KDD datasets and a sensor network dataset were employed to test the performance of the model. These experimental results indicate that the SCDNN classifier not only performs better than backpropagation neural network (BPNN), support vector machine (SVM), random forest (RF) and Bayes tree models in detection accuracy and the types of abnormal attacks found. It also provides an effective tool of study and analysis of intrusion detection in large networks.

DIDS Using Cooperative Agents Based on Ant Colony Clustering

Intrusion detection systems (IDS) play an important role in information security. Two major problems in the development of IDSs are the computational aspect and the architectural aspect. The computational or algorithmic problems include lacking ability of novel-attack detection and computation overload caused by large data traffic. The architectural problems are related to the communication between components of detection, including difficulties to overcome distributed and coordinated attacks because of the need of large amounts of distributed information and synchronization between detection components. This paper proposes a multi-agent architecture for a distributed intrusion detection system (DIDS) based on ant-colony clustering (ACC), for recognizing new and coordinated attacks, handling large data traffic, synchronization, co-operation between components without the presence of centralized computation, and good detection performance in real-time with immediate alarm notification. Feature selection based on principal component analysis (PCA) is used for dimensional reduction of NSL-KDD. Initial features are transformed to new features in smaller dimensions, where probing attacks (Ra-Probe) have a characteristic sign in their average value that is different from that of normal activity. Selection is based on the characteristics of these factors, resulting in a two-dimensional subset of the 75% data reduction.

Based on the Ant Colony Algorithm is a Distributed Intrusion Detection Method

This paper analyzes the present situation of the current network security problems and points out the research and development of intrusion detection system has very important significance on the basis of comparative analysis of the traditional static security model and PPDR dynamic security model, and according to this model, using ant colony algorithm is a distributed computing network intrusion of metrics, the determination of index contrast and invasion route, increase the accuracy of testing operation and calculation results show that the effectiveness of the solution and the convergence speed. For distributed network intrusion is put forward a new kind of means.

Intrusion Detection System Using Self Organizing Map Algorithms

With the rapid expansion of computer usage and computer network the security of the computer system has became very important. Every day new kind of attacks are being faced by industries. Many methods have been proposed for the development of intrusion detection system using artificial intelligence technique. In this paper we will have a look at an algorithm based on neural networks that are suitable for Intrusion Detection Systems (IDS). The name of this algorithm is Self Organizing Maps (SOM). So far, many different methods have been used to build a detector that Wide variety of different ways in the covers. Among the methods used to detect attacks in intrusion detection is done, In this paper we investigate the Self-Organizing Map method.

A Survey of Artificial Immune System Based Intrusion Detection

In the area of computer security, Intrusion Detection (ID) is a mechanism that attempts to discover abnormal access to computers by analyzing various interactions. There is a lot of literature about ID, but this study only surveys the approaches based on Artificial Immune System (AIS). The use of AIS in ID is an appealing concept in current techniques. This paper summarizes AIS based ID methods from a new view point; moreover, a framework is proposed for the design of AIS based ID Systems (IDSs). This framework is analyzed and discussed based on three core aspects: antibody/antigen encoding, generation algorithm, and evolution mode. Then we collate the commonly used algorithms, their implementation characteristics, and the development of IDSs into this framework. Finally, some of the future challenges in this area are also highlighted.

GOOSE 프로토콜 환경에서 Snort 기반의 침입 탐지 시스템 개발

GOOSE 프로토콜 환경에서 Snort 기반의 침입 탐지 시스템 개발

An Ensemble Classification Approach for Intrusion Detection

cyber attacks in various forms compel everyone to implement effective intrusion detection systems for protecting their information wealth. From last two decades, there has been extensive research going on in intrusion detection system development using various techniques. But, designing detection systems producing maximum accuracy with minimum false positive is yet a challenging task for the research community. Ensemble method is one of the major developments in the field of machine learning. In this research work, new ensemble classification method is proposed from different classifiers. Support vector machine techniques, artificial neural network and random forest are used for classification. Ensemble model is formed for producing better result. The model shows promising result for all classes of attacks.

The Development of Intrusion Detection System Based on Improved BP Neural Network

BP neural network is a multilayer feed-forward neural network, it achieved from input to output arbitrary nonlinear mapping, and weights are adjusted by using the back propagation learning algorithm. Intrusion detection systems using the learning ability of neural network to extract the network data profile, and it also can use the neural network has the ability of self-learning and parallel processing ability, through the construction of intelligent neural network classifier to identify abnormal, so as to achieve the purpose of detecting intrusion behavior. The paper proposes the development of intrusion detection system based on improved BP neural network. Experimental results show that the proposed algorithm has high efficiency.

The Application of Network Intrusion Detection Technology in Instrument

The development of intrusion detection systems in the world are reviewed in this article first. On the basis of in-depth analysis of the characteristics of network attacks and intrusions we aim at to solving the problems mentioned above, the characteristics of survival of the fittest genetic algorithm is used to solve the problem. Second, a detection model based on genetic algorithms is established, and finally the model is simulated. The simulation results show that the model can solve its intrusion detection system, security issues, with a theoretical and practical application.

The Development Of Intrusion Detection System Based On Wavelet Network

IDS (Intrusion Detection) is through a computer network or computer system from a number of key points in the collection of information and analyze, from a network or system is found in violation of security policy behavior and signs of an attack, while making response. As a mathematical tool, wavelet analysis has been widely used in signal analysis, image processing, numerical analysis, etc. This paper presents the development of intrusion detection system based on wavelet network and proposes the wavelet network algorithm based intrusion detection system. Finally, the paper designs a wavelet-based network intrusion detection system. And verified through simulation experiments of this intrusion detection system is feasible.