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Cyber Resilience through Real-Time Threat Analysis in information security

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International Journal of Communications, Network and System Sciences 2024年第4期17卷 51-67页

作者： Aparna Gadhi Ragha Madhavi Gondu Hitendra Chaudhary Olatunde Abiona Department of Computer information Systems Indiana University Northwest Gary IN USA

This paper examines how cybersecurity is developing and how it relates to more conventional information security. Although information security and cyber security are sometimes used synonymously, this study contends that they are not the same. The concept of cyber security is explored, which goes beyond protecting information resources to include a wider variety of assets, including people [1]. Protecting information assets is the main goal of traditional information security, with consideration to the human element and how people fit into the security process. On the other hand, cyber security adds a new level of complexity, as people might unintentionally contribute to or become targets of cyberattacks. This aspect presents moral questions since it is becoming more widely accepted that society has a duty to protect weaker members of society, including children [1]. The study emphasizes how important cyber security is on a larger scale, with many countries creating plans and laws to counteract cyberattacks. Nevertheless, a lot of these sources frequently neglect to define the differences or the relationship between information security and cyber security [1]. The paper focus on differentiating between cybersecurity and information security on a larger scale. The study also highlights other areas of cybersecurity which includes defending people, social norms, and vital infrastructure from threats that arise from online in addition to information and technology protection. It contends that ethical issues and the human factor are becoming more and more important in protecting assets in the digital age, and that cyber security is a paradigm shift in this regard [1].

关键词： Cybersecurity information security Network security Cyber Resilience Real-Time Threat Analysis Cyber Threats Cyberattacks Threat Intelligence Machine Learning Artificial Intelligence Threat Detection Threat Mitigation Risk Assessment Vulnerability Management Incident Response security Orchestration Automation Threat Landscape Cyber-Physical Systems Critical Infrastructure Data Protection Privacy Compliance Regulations Policy Ethics Cybercrime Threat Actors Threat Modeling security Architecture

Metaheuristics with Machine Learning Enabled information security on Cloud Environment

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Computers, Materials & Continua 2022年第10期73卷 1557-1570页

作者： Haya Mesfer Alshahrani Faisal S.Alsubaei Taiseer Abdalla Elfadil Eisa Mohamed K.Nour Manar Ahmed Hamza Abdelwahed Motwakel Abu Sarwar Zamani Ishfaq Yaseen Department of information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia Department of information Systems-Girls Section King Khalid UniversityMahayil62529Saudi Arabia Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The increasing quantity of sensitive and personal data being gathered by data controllers has raised the security needs in the cloud environment.Cloud computing(CC)is used for storing as well as processing data.Therefore,security becomes important as the CC handles massive quantity of outsourced,and unprotected sensitive data for public access.This study introduces a novel chaotic chimp optimization with machine learning enabled information security(CCOML-IS)technique on cloud environment.The proposed CCOML-IS technique aims to accomplish maximum security in the CC environment by the identification of intrusions or anomalies in the network.The proposed CCOML-IS technique primarily normalizes the networking data by the use of data conversion and min-max normalization.Followed by,the CCOML-IS technique derives a feature selection technique using chaotic chimp optimization algorithm(CCOA).In addition,kernel ridge regression(KRR)classifier is used for the detection of security issues in the network.The design of CCOA technique assists in choosing optimal features and thereby boost the classification performance.A wide set of experimentations were carried out on benchmark datasets and the results are assessed under several measures.The comparison study reported the enhanced outcomes of the CCOML-IS technique over the recent approaches interms of several measures.

关键词： information security cloud computing intrusion anomalies data mining feature selection classification

Neural variability fingerprint predicts individuals,information security violation intentions

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Fundamental Research 2022年第2期2卷 303-310页

作者： Qin Duan Zhengchuan Xu Qing Hu Siyang Luo Department of Psychology Guangdong Provincial Key Laboratory of Social Cognitive Neuroscience and Mental HealthGuangdong Provincial Key Laboratory of Brain Function and DiseaseSun Yat-Sen UniversityGuangzhou 510006China Fudan University Shanghai 200433China The Koppelman School of Business Brooklyn CollegeThe City University of New YorkNew YorkUSA

As the weakest links in information security defense are the individuals in an organizations,it is important to understand their information security behaviors.In the current study,we tested whether the neural variability pattern could predict an individual's intention to engage in information security violations.Because cognitive neuroscience methods can provide a new perspective into psychological processes without common methodological biases or social desirability,we combined an adapted version of the information security paradigm(ISP)with functional magnetic resonance imaging(fMRI)technology.While completing an adapted ISP task,participants underwent an fMRI scan.We adopted a machine learning method to build a neural variability predictive model.Consistent with previous studies,we found that people were more likely to take actions under neutral conditions than in minor violation contexts and major violation contexts.Moreover,the neural variability predictive model,including nodes within the task control,default mode,visual,salience and attention networks,can predict information security violation intentions.These results illustrate the predictive value of neural variability for information security violations and provide a new perspective for combining ISP with the fMRI technique to explore a neural predictive model of information security violation intention.

关键词： information security information security violation Neural variability fMRI Machine learning

information security assurance lifecycle research

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The Journal of China Universities of Posts and Telecommunications 2007年第4期14卷 77-81页

作者： XIE Cheng-shan XUJIA Gu-yue WANG Li State Key Laboratory of information security Beijing 100038 China School of Economy and Management Beihang University Beijing 100083 China

This article proposes that problems of information security are mainly caused by the ineffective integration of people, operation, and technology, and not merely by the poor use of technology. Based on the information lifecycle, a model of the information security assurance lifecycle is presented. The crucial parts of the model are further discussed, with the information risk value and protect level, and the solution in each step of the lifecycle is presented with an ensured information risk level, in term of the integration of people, operation, and technology.

关键词： information security information risk information lifecycle

information security through controlled quantum teleportation networks

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Digital Communications and Networks 2020年第4期6卷 463-470页

作者： Hala Hamdoun Alaa Sagheer Center for Artificial Intelligence and RObotics(CAIRO) Department of Computer ScienceAswan UniversityAswanEgypt Department of Computer Science College of Computer Science and Information TechnologyKing Faisal UniversitySaudi Arabia

information security is the backbone of current intelligent systems,such as the Internet of Things(IoT),smart grids,and Machine-to-Machine(M2M)communication.The increasing threat of information security requires new models to ensure the safe transmission of information through such systems.Recently,quantum systems have drawn much attention since they are expected to have a significant impact on the research in information security.This paper proposes a quantum teleportation scheme based on controlled multi-users to ensure the secure information transmission among users.Quantum teleportation is an original key element in a variety of quantum information tasks as well as quantum-based technologies,which plays a pivotal role in the current progress of quantum computing and communication.In the proposed scheme,the sender transmits the information to the receiver under the control of a third user or controller.Here,we show that the efficiency of the proposed scheme depends on the properties of the transmission channel and the honesty of the controller.Compared with various teleportation scheme presented recently in the literature,the most important difference in the proposed scheme is the possibility of suspicion about the honesty of the controller and,consequently,taking proper precautions.

关键词： information security Machine-to-machine communication Intelligent secure systems Quantum teleportation Quantum networks

A convolutional neural network to detect possible hidden data in spatial domain images

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Cybersecurity 2024年第1期7卷 37-52页

作者： Jean De La Croix Ntivuguruzwa Tohari Ahmad Department of Informatics Institut Teknologi Sepuluh Nopember(ITS)Kampus ITS Keputih SukoliloSurabaya60111Indonesia African Center of Excellence in the Internet of Things College of Science and TechnologyUniversity of Rwanda3900KigaliRwanda

Hiding secret data in digital multimedia has been essential to protect the data.Nevertheless,attackers with a steganalysis technique may break them.Existing steganalysis methods have good results with conventional Machine Learning(ML)techniques;however,the introduction of Convolutional Neural Network(CNN),a deep learning paradigm,achieved better performance over the previously proposed ML-based techniques.Though the existing CNN-based approaches yield good results,they present performance issues in classification accuracy and stability in the network training phase.This research proposes a new method with a CNN architecture to improve the hidden data detection accuracy and the training phase stability in spatial domain images.The proposed method comprises three phases:pre-processing,feature extraction,and classification.Firstly,in the pre-processing phase,we use spatial rich model filters to enhance the noise within images altered by data hiding;secondly,in the feature extraction phase,we use two-dimensional depthwise separable convolutions to improve the signal-to-noise and regular convolutions to model local features;and finally,in the classification,we use multi-scale average pooling for local features aggregation and representability enhancement regardless of the input size variation,followed by three fully connected layers to form the final feature maps that we transform into class probabilities using the softmax function.The results identify an improvement in the accuracy of the considered recent scheme ranging between 4.6 and 10.2%with reduced training time up to 30.81%.

关键词： information security Spatial domain steganalysis Deep learning Convolutional neural network Infrastructure

A Novel High-Efficiency Transaction Verification Scheme for Blockchain Systems

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Computer Modeling in Engineering & Sciences 2024年第5期139卷 1613-1633页

作者： Jingyu Zhang Pian Zhou Jin Wang Osama Alfarraj Saurabh Singh Min Zhu School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410015China Science and Technology on information Systems Engineering Laboratory School of Systems EngineeringNational University of Defense TechnologyChangsha410073China Computer Science Department King Saud UniversityRiyadh11437Saudi Arabia Department of Artificial Intelligence and Big Data Woosong UniversityDaejeon34606South Korea College of information Science and Technology Zhejiang Shuren UniversityHangzhou310015China

Blockchain can realize the reliable storage of a large amount of data that is chronologically related and verifiable within the system.This technology has been widely used and has developed rapidly in big data systems across various fields.An increasing number of users are participating in application systems that use blockchain as their underlying architecture.As the number of transactions and the capital involved in blockchain grow,ensuring information security becomes imperative.Addressing the verification of transactional information security and privacy has emerged as a critical challenge.Blockchain-based verification methods can effectively eliminate the need for centralized third-party organizations.However,the efficiency of nodes in storing and verifying blockchain data faces unprecedented challenges.To address this issue,this paper introduces an efficient verification scheme for transaction security.Initially,it presents a node evaluation module to estimate the activity level of user nodes participating in transactions,accompanied by a probabilistic analysis for all transactions.Subsequently,this paper optimizes the conventional transaction organization form,introduces a heterogeneous Merkle tree storage structure,and designs algorithms for constructing these heterogeneous trees.Theoretical analyses and simulation experiments conclusively demonstrate the superior performance of this scheme.When verifying the same number of transactions,the heterogeneous Merkle tree transmits less data and is more efficient than traditional methods.The findings indicate that the heterogeneous Merkle tree structure is suitable for various blockchain applications,including the Internet of Things.This scheme can markedly enhance the efficiency of information verification and bolster the security of distributed systems.

关键词： Blockchain architecture transaction verification information security heterogeneous Merkle tree distributed systems

TULAM:trajectory-user linking via attention mechanism

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Science China(information Sciences) 2024年第1期67卷 51-68页

作者： Hao LI Shuyu CAO Yaqing CHEN Min ZHANG Dengguo FENG Trusted Computing and information Assurance Laboratory Institute of SoftwareChinese Academy of Sciences University of Chinese Academy of Sciences

Recently,the application of location-based services(LBS) has become a prevalent means to provide convenience in customers’ everyday lives.However,because massive volumes of location information are collected by LBS applications,users may suffer from serious privacy issues.Prior studies have shown that the identities of users can be inferred from historical anonymous trajectories,which is formulated as the trajectory-user linking(TUL) task.Although some recurrent neural network(RNN)-based models have been proposed to capture implicit movement patterns among trajectories to improve TUL performance,they cannot learn the sequential and contextual semantics within any individual trajectory completely,leaving the advantages of RNNs underutilized.We therefore propose an RNN model with an attention mechanism called TULAM to improve the accuracy of the TUL task.TULAM learns sequential relationships within individual trajectories via RNN and captures contextual semantics from trajectories via a multi-head attention mechanism.Additionally,we propose a novel location encoding method called approximate one-hot to solve the corpus shortage problem of trajectory datasets.Evaluations were conducted on real datasets from the Gowalla and Foursquare LBS platforms.The experimental results indicate that TULAM is a practical solution that achieves significant improvements over existing methods with satisfactory model complexity and convergence.

关键词： information security data security and privacy trajectory-user linking deep learning recurrent neural network attention mechanism

A Hybrid Method of Coreference Resolution in information security

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Computers, Materials & Continua 2020年第8期64卷 1297-1315页

作者： Yongjin Hu Yuanbo Guo Junxiu Liu Han Zhang information Engineering University Zhengzhou450000China Intelligent Systems Research Centre School of ComputingEngineering&Intelligent SystemsUlster UniversityMagee CampusNorthern IrelandBT487JLUK Zheng Zhou University Zhengzhou450001China

In the field of information security,a gap exists in the study of coreference resolution of entities.A hybrid method is proposed to solve the problem of coreference resolution in information security.The work consists of two parts:the first extracts all candidates(including noun phrases,pronouns,entities,and nested phrases)from a given document and classifies them;the second is coreference resolution of the selected candidates.In the first part,a method combining rules with a deep learning model(Dictionary BiLSTM-Attention-CRF,or DBAC)is proposed to extract all candidates in the text and classify them.In the DBAC model,the domain dictionary matching mechanism is introduced,and new features of words and their contexts are obtained according to the domain dictionary.In this way,full use can be made of the entities and entity-type information contained in the domain dictionary,which can help solve the recognition problem of both rare and long entities.In the second part,candidates are divided into pronoun candidates and noun phrase candidates according to the part of speech,and the coreference resolution of pronoun candidates is solved by making rules and coreference resolution of noun phrase candidates by machine learning.Finally,a dataset is created with which to evaluate our methods using information security data.The experimental results show that the proposed model exhibits better performance than the other baseline models.

关键词： Coreference resolution hybrid method rules BiLSTM-Attention-CRF information security