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A social computing method for energy safety

Journal of Safety Science and Resilience 2024年第1期5卷 64-82页

作者： Pengfei Zhao Shuangqi Li Zhidong Cao Paul Jen-Hwa Hu Daniel Dajun Zeng Da Xie Yichen Shen Jiangfeng Li Tianyi Luo Institute of Automation Chinese Academy of SciencesUniversity of Chinese Academy of SciencesBeijing 100090China Systems Engineering Cornell UniversityIthacaNY 14853USA David Eccles School of Business the University of UtahUtahUSA Department of Electrical Engineering Shanghai Jiao Tong UniversityShanghai 200240China China University of Mining and Technology China

Information and communication technologies enable the transformation of traditional energy systems into cyber-physical energy systems(CPESs),but such systems have also become popular targets of Cyberattacks.Currently,available methods for evaluating the impacts of Cyberattacks suffer from limited resilience,efficacy,and practical value.To mitigate their potentially disastrous consequences,this study suggests a two-stage,discrepancy-based optimization approach that considers both preparatory actions and response measures,integrating concepts from social computing.The proposed Kullback-Leibler divergence-based,distributionally robust optimization(KDR)method has a hierarchical,two-stage objective function that incorporates the operating costs of both system infrastructures(e.g.,energy resources,reserve capacity)and real-time response measures(e.g.,load shedding,demand-side management,electric vehicle charging station management).By incorporating social computing principles,the optimization framework can also capture the social behavior and interactions of energy consumers in response to Cyberattacks.The preparatory stage entails day-ahead operational decisions,leveraging insights from social computing to model and predict the behaviors of individuals and communities affected by potential Cyberattacks.The mitigation stage generates responses designed to contain the consequences of the attack by directing and optimizing energy use from the demand side,taking into account the social context and preferences of energy consumers,to ensure resilient,economically efficient CPES operations.Our method can determine optimal schemes in both stages,accounting for the social dimensions of the problem.An original disaster mitigation model uses an abstract formulation to develop a risk-neutral model that characterizes Cyberattacks through KDR,incorporating social computing techniques to enhance the understanding and response to cyber threats.This approach can mitigate the impacts more effectively than several existing methods,even

关键词： Cyberattacks Cyber-physical energy systems Distributionally robust optimization False data injections Two-stage modeling Social computing

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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

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Artificial Intelligence Adoption for Cybersecurity in Africa

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Journal of Information Security 2024年第2期15卷 134-147页

作者： Nadine Nibigira Vincent Havyarimana Zhu Xiao School of Engineering Sciences University of Burundi Doctoral School Bujumbura Burundi Department of Applied Sciences Burundi Higher Institute of Education (ENS) Bujumbura Burundi College of Computer Science and Electronic Engineering Hunan University Yongzhou Hunan

Legacy-based threat detection systems have not been able to keep up with the exponential growth in scope, frequency, and effect of cybersecurity threats. Artificial intelligence is being used as a result to help with the issue. This paper’s primary goal is to examine how African nations are utilizing artificial intelligence to defend their infrastructure against Cyberattacks. Artificial intelligence (AI) systems will make decisions that impact Africa’s future. The lack of technical expertise, the labor pool, financial resources, data limitations, uncertainty, lack of structured data, absence of government policies, ethics, user attitudes, insufficient investment in research and development, and the requirement for more adaptable and dynamic regulatory systems all pose obstacles to the adoption of AI technologies in Africa. The paper discusses how African countries are adopting artificial intelligence solutions for cybersecurity. And it shows the impact of AI to identify shadow data, monitor for abnormalities in data access and alert cyber security professionals about potential threats by anyone accessing the data or sensitive information saving valuable time in detecting and remediating issues in real-time. The study finds that 69.16% of African companies are implementing information security strategies and of these, 45% said they use technologies based on AI algorithms. This study finds that a large number of African businesses use tools that can track and analyze user behaviour in designated areas and spot anomalies, such as new users, strange IP addresses and login activity, changes to permissions on files, folders, and other resources, and the copying or erasure of massive amounts of data. Thus, we discover that just 18.18% of the target has no national cybersecurity strategy or policy. The study proposes using big data security analytics to integrate AI. Adopting it would be beneficial for all African nations, as it provides a range of cyberattack defense techniques.

关键词： Artificial Intelligence (AI) Cybersecurity Cyberattacks Cybercriminals

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COVID-19 pandemic and the cyberthreat landscape:Research challenges and opportunities

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Digital Communications and Networks 2023年第1期9卷 211-222页

作者： Heba Saleous Muhusina Ismail Saleh HAlDaajeh Nisha Madathil Saed Alrabaee Kim-Kwang Raymond Choo Nabeel Al-Qirim Information Systems and Security College of ITUnited Arab Emirates UniversityAl AinUnited Arab Emirates Department of Information Systems and Cyber Security The University of Texas at San AntonioSan AntonioTX78249USA College of Technological Innovations(CIT) Zayed UniversityUAE

Although cyber technologies benefit our society,there are also some related cybersecurity risks.For example,cybercriminals may exploit vulnerabilities in people,processes,and technologies during trying times,such as the ongoing COVID-19 pandemic,to identify opportunities that target vulnerable individuals,organizations(e.g.,medical facilities),and systems.In this paper,we examine the various cyberthreats associated with the COVID-19 pandemic.We also determine the attack vectors and surfaces of cyberthreats.Finally,we will discuss and analyze the insights and suggestions generated by different Cyberattacks against individuals,organizations,and systems.

关键词： COVID-19 Cyberattacks Security and privacy Mitigation Potential solutions

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A novel hybrid cybersecurity scheme against false data injection attacks in automated power systems

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Protection and Control of Modern Power Systems 2023年第3期8卷 1-15页

作者： Shahbaz Hussain S.M.Suhail Hussain Marziyeh Hemmati Atif Iqbal Rashid Alammari Stefano Zanero Enrico Ragaini Giambattista Gruosso Department of Electrical Engineering Qatar UniversityDohaQatar Dipartimento di Elettronica Informazione e BioingegneriaPolitecnico di MilanoMilanItaly Department of Electrical Engineering King Fahd University of Petroleum and Minerals(KFUPM)DhahranSaudi Arabia Interdisciplinary Research Center for Renewable Energy and Power Systems(IRC-REPS) King Fahd University of Petroleum and Minerals(KFUPM)DhahranSaudi Arabia

The conventional power systems are evolving as smart grids.In recent times Cyberattacks on smart grids have been increasing.Among different attacks,False Data Injection(FDI)is considered as an emerging threat that has significant impact.By exploiting the vulnerabilities of IEC 61850 Generic Object-Oriented Substation Events(GOOSE)and Sam-pled Values(SV)attackers can launch different FDI attacks.In this paper,a real-time set up capable of simulating FDI on GOOSE and SV protocols is developed to evaluate the impact of such attacks on power grid.IEC 62351 stipulates cybersecurity guidelines for GOOSE and SV,but only at communication or Information Technology(IT)level.Hence there is a need to develop a holistic security both at IT and Operation Technology(OT)level.In this regard,a novel sequence content resolver-based hybrid security scheme suitable to tackle FDI attacks on GOOSE and SV is proposed.Furthermore,the computational performance of the proposed hybrid security scheme is presented to demonstrate its applicability to the time critical GOOSE and SV protocols.

关键词： Cyberattacks False data injection Real time digital simulation IEC 61850 Communication protocols Control authority Countermeasures

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Distributed Fault-Tolerant Consensus Tracking of Multi-Agent Systems Under Cyber-Attacks

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IEEE/CAA Journal of Automatica Sinica 2022年第6期9卷 1037-1048页

作者： Chun Liu Bin Jiang Xiaofan Wang Huiliao Yang Shaorong Xie School of Mechatronic Engineering and Automation Shanghai UniversityShanghai 200444 School of Artificial Intelligence Shanghai UniversityShanghai 200444China IEEE College of Automation Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016China College of Energy and Electrical Engineering Hohai UniversityNanjing 210016China School of Computer Engineering and Science Shanghai UniversityShanghai 200444China

This paper investigates the distributed fault-tolerant consensus tracking problem of nonlinear multi-agent systems with general incipient and abrupt time-varying actuator faults under cyber-attacks.First,a decentralized unknown input observer is established to estimate relative states and actuator faults.Second,the estimated and output neighboring information is combined with distributed fault-tolerant consensus tracking controllers.Criteria of reaching leader-following exponential consensus tracking of multi-agent systems under both connectivity-maintained and connectivity-mixed attacks are derived with average dwelling time,attack frequency,and attack activation rate technique,respectively.Simulation example verifies the effectiveness of the fault-tolerant consensus tracking algorithm.

关键词： Cyberattacks fault-tolerant consensus tracking incipient and abrupt actuator faults nonlinear multi-agent systems unknown input observer

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Hunger Search Optimization with Hybrid Deep Learning Enabled Phishing Detection and Classification Model

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Computers, Materials & Continua 2022年第12期73卷 6425-6441页

作者： Hadil Shaiba Jaber S.Alzahrani Majdy M.Eltahir Radwa Marzouk Heba Mohsen Manar Ahmed Hamza Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Phishing is one of the simplest ways in cybercrime to hack the reliable data of users such as passwords,account identifiers,bank details,etc.In general,these kinds of Cyberattacks are made at users through phone calls,emails,or instant messages.The anti-phishing techniques,currently under use,aremainly based on source code features that need to scrape the webpage content.In third party services,these techniques check the classification procedure of phishing Uniform Resource Locators(URLs).Even thoughMachine Learning(ML)techniques have been lately utilized in the identification of phishing,they still need to undergo feature engineering since the techniques are not well-versed in identifying phishing offenses.The tremendous growth and evolution of Deep Learning(DL)techniques paved the way for increasing the accuracy of classification process.In this background,the current research article presents a Hunger Search Optimization with Hybrid Deep Learning enabled Phishing Detection and Classification(HSOHDL-PDC)model.The presented HSOHDL-PDC model focuses on effective recognition and classification of phishing based on website URLs.In addition,SOHDL-PDC model uses character-level embedding instead of word-level embedding since the URLs generally utilize words with no importance.Moreover,a hybrid Convolutional Neural Network-Long Short Term Memory(HCNN-LSTM)technique is also applied for identification and classification of phishing.The hyperparameters involved in HCNN-LSTM model are optimized with the help of HSO algorithm which in turn produced improved outcomes.The performance of the proposed HSOHDL-PDC model was validated using different datasets and the outcomes confirmed the supremacy of the proposed model over other recent approaches.

关键词： Uniform resource locators phishing Cyberattacks machine learning deep learning hyperparameter optimization

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Hybrid Metaheuristics Feature Selection with Stacked Deep Learning-Enabled Cyber-Attack Detection Model

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Computer Systems Science & Engineering 2023年第5期45卷 1679-1694页

作者： Mashael M Asiri Heba G.Mohamed Mohamed K Nour Mesfer Al Duhayyim Amira Sayed A.Aziz Abdelwahed Motwakel Abu Sarwar Zamani Mohamed I.Eldesouki Department of Computer Science College of Science&Art atMahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Due to exponential increase in smart resource limited devices and high speed communication technologies,Internet of Things(IoT)have received significant attention in different application areas.However,IoT environment is highly susceptible to cyber-attacks because of memory,processing,and communication restrictions.Since traditional models are not adequate for accomplishing security in the IoT environment,the recent developments of deep learning(DL)models find beneficial.This study introduces novel hybrid metaheuristics feature selection with stacked deep learning enabled cyber-attack detection(HMFS-SDLCAD)model.The major intention of the HMFS-SDLCAD model is to recognize the occurrence of Cyberattacks in the IoT environment.At the preliminary stage,data pre-processing is carried out to transform the input data into useful format.In addition,salp swarm optimization based on particle swarm optimization(SSOPSO)algorithm is used for feature selection process.Besides,stacked bidirectional gated recurrent unit(SBiGRU)model is utilized for the identification and classification of Cyberattacks.Finally,whale optimization algorithm(WOA)is employed for optimal hyperparameter optimization process.The experimental analysis of the HMFS-SDLCAD model is validated using benchmark dataset and the results are assessed under several aspects.The simulation outcomes pointed out the improvements of the HMFS-SDLCAD model over recent approaches.

关键词： Cyberattacks security deep learning internet of things feature selection data classification

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DeepIoT.IDS:Hybrid Deep Learning for Enhancing IoT Network Intrusion Detection

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Computers, Materials & Continua 2021年第12期69卷 3945-3966页

作者： Ziadoon K.Maseer Robiah Yusof Salama A.Mostafa Nazrulazhar Bahaman Omar Musa Bander Ali Saleh Al-rimy Faculty of Information and Communication Technology Universiti Teknikal MalaysiaMelaka76100Malaysia Center of Intelligent and Autonomous Systems Faculty of Computer Science and Information TechnologyUniversiti Tun Hussein Onn MalaysiaJohor86400Malaysia Faculty of Business and Technology UNITAR International UniversitySelangor47301Malaysia School of Computing Faculty of EngineeringUniversiti Teknologi MalaysiaJohor81310Malaysia

With an increasing number of services connected to the internet,including cloud computing and Internet of Things(IoT)systems,the prevention of Cyberattacks has become more challenging due to the high dimensionality of the network traffic data and access points.Recently,researchers have suggested deep learning(DL)algorithms to define intrusion features through training empirical data and learning anomaly patterns of attacks.However,due to the high dynamics and imbalanced nature of the data,the existing DL classifiers are not completely effective at distinguishing between abnormal and normal behavior line connections for modern networks.Therefore,it is important to design a self-adaptive model for an intrusion detection system(IDS)to improve the detection of attacks.Consequently,in this paper,a novel hybrid weighted deep belief network(HW-DBN)algorithm is proposed for building an efficient and reliable IDS(DeepIoT.IDS)model to detect existing and novel Cyberattacks.The HW-DBN algorithm integrates an improved Gaussian–Bernoulli restricted Boltzmann machine(Deep GB-RBM)feature learning operator with a weighted deep neural networks(WDNN)classifier.The CICIDS2017 dataset is selected to evaluate the DeepIoT.IDS model as it contains multiple types of attacks,complex data patterns,noise values,and imbalanced classes.We have compared the performance of the DeepIoT.IDS model with three recent models.The results show the DeepIoT.IDS model outperforms the three other models by achieving a higher detection accuracy of 99.38%and 99.99%for web attack and bot attack scenarios,respectively.Furthermore,it can detect the occurrence of low-frequency attacks that are undetectable by other models.

关键词： Cyberattacks internet of things intrusion detection system deep learning neural network supervised and unsupervised deep learning

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Conceptualisation of Cyberattack prediction with deep learning

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Cybersecurity 2018年第1期1卷 788-801页

作者： Ayei E.Ibor Florence A.Oladeji Olusoji B.Okunoye Obeten O.Ekabua Department of Computer Science University of CalabarCalabarNigeria Department of Computer Sciences University of LagosLagosNigeria

The state of the cyberspace portends uncertainty for the future Internet and its accelerated number of users.New paradigms add more concerns with big data collected through device sensors divulging large amounts of information,which can be used for targeted attacks.Though a plethora of extant approaches,models and algorithms have provided the basis for cyberattack predictions,there is the need to consider new models and algorithms,which are based on data representations other than task-specific techniques.Deep learning,which is underpinned by representation learning,has found widespread relevance in computer vision,speech recognition,natural language processing,audio recognition,and drug design.However,its non-linear information processing architecture can be adapted towards learning the different data representations of network traffic to classify benign and malicious network packets.In this paper,we model cyberattack prediction as a classification problem.Furthermore,the deep learning architecture was co-opted into a new model using rectified linear units(ReLU)as the activation function in the hidden layers of a deep feed forward neural network.Our approach achieves a greedy layer-by-layer learning process that best represents the features useful for predicting Cyberattacks in a dataset of benign and malign traffic.The underlying algorithm of the model also performs feature selection,dimensionality reduction,and clustering at the initial stage,to generate a set of input vectors called hyper-features.The model is evaluated using CICIDS2017 and UNSW_NB15 datasets on a Python environment test bed.Results obtained from experimentation show that our model demonstrates superior performance over similar models.

关键词： Cyberattacks Prediction Deep learning Python Dimensionality reduction

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