摘要：
Purpose-Virtually unlimited amounts of data collection by cybersecurity systems put people at risk of having their privacy violated.Social networks like Facebook on the Internet provide an overplus of knowledge concerning their users.Although users relish exchanging data online,only some data are meant to be interpreted by those who see value in it.It is now essential for online social network(OSN)to regulate the privacy of their users on the Internet.This paper aims to propose an efficient privacy violation detection model(EPVDM)for OSN.Design/methodology/approach-In recent months,the prominent position of both industry and academia has been dominated by privateness,its breaches and strategies to dodge privacy violations.Corporations around the world have become aware of the effects of violating privacy and its effect on them and other stakeholders.Once privacy violations are detected,they must be reported to those affected and it’s supposed to be mandatory to make them to take the next action.Although there are different approaches to detecting breaches of privacy,most strategies do not have a functioning tool that can show the values of its subject heading.An EPVDM for Facebook,based on a deep neural network,is proposed in this research paper.Findings-The main aim of EPVDM is to identify and avoid potential privacy breaches on Facebook in the future.Experimental analyses in comparison with major intrusion detection system(IDS)to detect privacy violation show that the proposed methodology is robust,precise and scalable.The chances of breaches or possibilities of privacy violations can be identified very accurately.Originality/value-All the resultant is compared with well popular methodologies like adaboost(AB),decision tree(DT),linear regression(LR),random forest(RF)and support vector machine(SVM).It’s been identified from the analysis that the proposed model outperformed the existing techniques in terms of accuracy(94%),precision(99.1%),recall(92.43%),f-score(95.43%)and violation detection rate(>98.5%).
摘要：
Intrusion Detection System(IDS)plays a crucial role in detecting and identifying the DoS and DDoS type of attacks on IoT devices.However,anomaly-based techniques do not provide acceptable accuracy for efficacious intrusion detection.Also,we found many difficulty levels when applying IDS to IoT devices for identifying attempted attacks.Given this background,we designed a solution to detect intrusions using the Convolutional Neural Network(CNN)for Enhanced Data rates for GSM Evolution(EDGE)Computing.We created two separate categories to handle the attack and non-attack events in the system.The findings of this study indicate that this approach was significantly effective.We attempted both multiclass and binary classification.In the case of binary,we clustered all malicious traffic data in a single class.Also,we developed 13 layers of Sequential 1-D CNN for IDS detection and assessed them on the public dataset NSL-KDD.Principal Component Analysis(PCA)was implemented to decrease the size of the feature vector based on feature extraction and engineering.The approach proposed in the current investigation obtained accuracies of 99.34%and 99.13%for binary and multiclass classification,respectively,for the NSL-KDD dataset.The experimental outcomes showed that the proposed Principal Component-based Convolution Neural Network(PCCNN)approach achieved greater precision based on deep learning and has potential use in modern intrusion detection for IoT systems.
摘要：
The rapid development of the Internet of Things(IoT)in the industrial domain has led to the new term the Industrial Internet of Things(IIoT).The IIoT includes several devices,applications,and services that connect the physical and virtual space in order to provide smart,cost-effective,and scalable systems.Although the IIoT has been deployed and integrated into a wide range of industrial control systems,preserving security and privacy of such a technology remains a big challenge.An anomaly-based Intrusion Detection System(IDS)can be an effective security solution for maintaining the confidentiality,integrity,and availability of data transmitted in IIoT environments.In this paper,we propose an intelligent anomalybased IDS framework in the context of fog-to-things communications to decentralize the cloud-based security solution into a distributed architecture(fog nodes)near the edge of the data source.The anomaly detection system utilizes minimum redundancy maximum relevance and principal component analysis as the featured engineering methods to select the most important features,reduce the data dimensionality,and improve detection performance.In the classification stage,anomaly-based ensemble learning techniques such as bagging,LPBoost,RUSBoost,and Adaboost models are implemented to determine whether a given flow of traffic is normal or malicious.To validate the effectiveness and robustness of our proposed model,we evaluate our anomaly detection approach on a new driven IIoT dataset called XIIoTID,which includes new IIoT protocols,various cyberattack scenarios,and different attack protocols.The experimental results demonstrated that our proposed anomaly detection method achieved a higher accuracy rate of 99.91%and a reduced false alarm rate of 0.1%compared to other recently proposed techniques.
摘要：
Due to the drastic increase in the number of critical infrastructures like nuclear plants,industrial control systems(ICS),transportation,it becomes highly vulnerable to several attacks.They become the major targets of cyberattacks due to the increase in number of interconnections with other networks.Several research works have focused on the design of intrusion detection systems(IDS)using machine learning(ML)and deep learning(DL)models.At the same time,Blockchain(BC)technology can be applied to improve the security level.In order to resolve the security issues that exist in the critical infrastructures and ICS,this study designs a novel BC with deep learning empowered cyber-attack detection(BDLE-CAD)in critical infrastructures and ICS.The proposed BDLE-CAD technique aims to identify the existence of intrusions in the network.In addition,the presented enhanced chimp optimization based feature selection(ECOA-FS)technique is applied for the selection of optimal subset of features.Moreover,the optimal deep neural network(DNN)with search and rescue(SAR)optimizer is applied for the detection and classification of intrusions.Furthermore,a BC enabled integrity checking scheme(BEICS)has been presented to defend against the misrouting attacks.The experimental result analysis of the BDLE-CAD technique takes place and the results are inspected under varying aspects.The simulation analysis pointed out the supremacy of the BDLE-CAD technique over the recent state of art techniques with the accuy of 92.63%.
摘要：
The limitations of IDS products are introduced. The necessary modules in IDS are discussed. A practical IDS method based on database trigger is proposed which can be easily implemented. The principle and implementation of this newly proposed method are detailed. And some typical examples of this method are detailed. The effect of control on network attacks is discussed.
摘要：
The limitations of IDS products are introduced. The necessary modules in IDS are discussed. A practical IDS method based on database trigger is proposed which can be easily implemented. The principle and implementation of this newly proposed method are detailed. And some typical examples of this method are detailed. The effect of control on network attacks is discussed.
摘要：
The limitations of IDS products are introduced. The necessary modules in IDS are discussed. A practical IDS method based on database trigger is proposed which can be easily implemented. The principle and implementation of this newly proposed method are detailed. And some typical examples of this method are detailed. The effect of control on network attacks is discussed.
AI期刊論文
查重降重
文獻(xiàn)查詢
文獻(xiàn)下載
文獻(xiàn)閱讀
文獻(xiàn)發(fā)表
