Intrusion detection systems (IDSs) are deployed to detect anomalies in real time. They classify a network’s incoming traffic as benign or anomalous (attack). An efficient and robust IDS in software-defined networks i...Intrusion detection systems (IDSs) are deployed to detect anomalies in real time. They classify a network’s incoming traffic as benign or anomalous (attack). An efficient and robust IDS in software-defined networks is an inevitable component of network security. The main challenges of such an IDS are achieving zero or extremely low false positive rates and high detection rates. Internet of Things (IoT) networks run by using devices with minimal resources. This situation makes deploying traditional IDSs in IoT networks unfeasible. Machine learning (ML) techniques are extensively applied to build robust IDSs. Many researchers have utilized different ML methods and techniques to address the above challenges. The development of an efficient IDS starts with a good feature selection process to avoid overfitting the ML model. This work proposes a multiple feature selection process followed by classification. In this study, the Software-defined networking (SDN) dataset is used to train and test the proposed model. This model applies multiple feature selection techniques to select high-scoring features from a set of features. Highly relevant features for anomaly detection are selected on the basis of their scores to generate the candidate dataset. Multiple classification algorithms are applied to the candidate dataset to build models. The proposed model exhibits considerable improvement in the detection of attacks with high accuracy and low false positive rates, even with a few features selected.展开更多
入侵检测作为一种网络主动防御技术,能够有效阻止来自黑客的多种手段攻击。随着机器学习的发展,相关技术也开始应用到入侵检测中。本文采用sklearn库中preprocessing模块的函数对KDD CUP 99数据集进行预处理,基于朴素贝叶斯和逻辑回归算...入侵检测作为一种网络主动防御技术,能够有效阻止来自黑客的多种手段攻击。随着机器学习的发展,相关技术也开始应用到入侵检测中。本文采用sklearn库中preprocessing模块的函数对KDD CUP 99数据集进行预处理,基于朴素贝叶斯和逻辑回归算法,建立了网络入侵检测模型,并利用信息增益算法对入侵相关特征进行选择,然后进行训练与预测。实验结果表明,选择特征子集进行训练和预测能够保证预测准确率并大幅提高检测效率。研究成果可为高速铁路信号系统网络入侵检测模型的设计和建立提供参考。展开更多
This paper proposes a new cost-efficient,adaptive,and self-healing algorithm in real time that detects faults in a short period with high accuracy,even in the situations when it is difficult to detect.Rather than usin...This paper proposes a new cost-efficient,adaptive,and self-healing algorithm in real time that detects faults in a short period with high accuracy,even in the situations when it is difficult to detect.Rather than using traditional machine learning(ML)algorithms or hybrid signal processing techniques,a new framework based on an optimization enabled weighted ensemble method is developed that combines essential ML algorithms.In the proposed method,the system will select and compound appropriate ML algorithms based on Particle Swarm Optimization(PSO)weights.For this purpose,power system failures are simulated by using the PSCA D-Python co-simulation.One of the salient features of this study is that the proposed solution works on real-time raw data without using any pre-computational techniques or pre-stored information.Therefore,the proposed technique will be able to work on different systems,topologies,or data collections.The proposed fault detection technique is validated by using PSCAD-Python co-simulation on a modified and standard IEEE-14 and standard IEEE-39 bus considering network faults which are difficult to detect.展开更多
基金The authors extend their appreciation to the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through the project number(IF2/PSAU/2022/01/23126)。
文摘Intrusion detection systems (IDSs) are deployed to detect anomalies in real time. They classify a network’s incoming traffic as benign or anomalous (attack). An efficient and robust IDS in software-defined networks is an inevitable component of network security. The main challenges of such an IDS are achieving zero or extremely low false positive rates and high detection rates. Internet of Things (IoT) networks run by using devices with minimal resources. This situation makes deploying traditional IDSs in IoT networks unfeasible. Machine learning (ML) techniques are extensively applied to build robust IDSs. Many researchers have utilized different ML methods and techniques to address the above challenges. The development of an efficient IDS starts with a good feature selection process to avoid overfitting the ML model. This work proposes a multiple feature selection process followed by classification. In this study, the Software-defined networking (SDN) dataset is used to train and test the proposed model. This model applies multiple feature selection techniques to select high-scoring features from a set of features. Highly relevant features for anomaly detection are selected on the basis of their scores to generate the candidate dataset. Multiple classification algorithms are applied to the candidate dataset to build models. The proposed model exhibits considerable improvement in the detection of attacks with high accuracy and low false positive rates, even with a few features selected.
文摘入侵检测作为一种网络主动防御技术,能够有效阻止来自黑客的多种手段攻击。随着机器学习的发展,相关技术也开始应用到入侵检测中。本文采用sklearn库中preprocessing模块的函数对KDD CUP 99数据集进行预处理,基于朴素贝叶斯和逻辑回归算法,建立了网络入侵检测模型,并利用信息增益算法对入侵相关特征进行选择,然后进行训练与预测。实验结果表明,选择特征子集进行训练和预测能够保证预测准确率并大幅提高检测效率。研究成果可为高速铁路信号系统网络入侵检测模型的设计和建立提供参考。
文摘This paper proposes a new cost-efficient,adaptive,and self-healing algorithm in real time that detects faults in a short period with high accuracy,even in the situations when it is difficult to detect.Rather than using traditional machine learning(ML)algorithms or hybrid signal processing techniques,a new framework based on an optimization enabled weighted ensemble method is developed that combines essential ML algorithms.In the proposed method,the system will select and compound appropriate ML algorithms based on Particle Swarm Optimization(PSO)weights.For this purpose,power system failures are simulated by using the PSCA D-Python co-simulation.One of the salient features of this study is that the proposed solution works on real-time raw data without using any pre-computational techniques or pre-stored information.Therefore,the proposed technique will be able to work on different systems,topologies,or data collections.The proposed fault detection technique is validated by using PSCAD-Python co-simulation on a modified and standard IEEE-14 and standard IEEE-39 bus considering network faults which are difficult to detect.
