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Enhanced Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity threat detection

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Computer Systems Science & Engineering 2023年第6期45卷 3037-3052页

作者： Fatma S.Alrayes Najm Alotaibi Jaber S.Alzahrani Sana Alazwari Areej Alhogail Ali M.Al-Sharafi Mahmoud Othman Manar Ahmed Hamza Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Prince Saud Al Faisal Institute for Diplomatic Studies RiyadhSaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Information Systems College of Computer and Information SciencesKing Saud UniversitySaudi Arabia Department of Computer Science College of Computers and Information TechnologyUniversity of BishaSaudi 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

Recent developments in computer networks and Internet of Things(IoT)have enabled easy access to *** the government and business sectors face several difficulties in resolving cybersecurity network issues,like novel attacks,hackers,internet criminals,and so ***,malware attacks and software piracy pose serious risks in compromising the security of *** can steal confidential data which results infinancial and reputational *** advent of machine learning(ML)and deep learning(DL)models has been employed to accomplish security in the IoT cloud *** article pre-sents an Enhanced Artificial Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity threat detection(EAGTODL-CTD)in IoT Cloud *** presented EAGTODL-CTD model encompasses the identification of the threats in the IoT cloud *** proposed EAGTODL-CTD mod-el mainly focuses on the conversion of input binaryfiles to color images,where the malware can be detected using an image classification *** EAG-TODL-CTD model pre-processes the input data to transform to a compatible *** threat detection and classification,cascaded gated recurrent unit(CGRU)model is exploited to determine class ***,EAGTO approach is employed as a hyperparameter optimizer to tune the CGRU parameters,showing the novelty of our *** performance evaluation of the EAGTODL-CTD model is assessed on a dataset comprising two class labels namely malignant and *** experimental values reported the supremacy of the EAG-TODL-CTD model with increased accuracy of 99.47%.

关键词： Cybersecurity computer networks threat detection internet of things cloud computing deep learning

On designing an unaided authentication service with threat detection and leakage control for defeating opportunistic adversaries

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Frontiers of Computer Science 2021年第2期15卷 161-179页

作者： Nilesh CHAKRABORTY Samrat MONDAL College of Computer Science and Software Engineering Shenzhen UniversityShenzhen518060China Department of Computer Science Indian Institute of Technology PatnaBihar801106India

Unaided authentication services provide the flexibility to login without being dependent on any additional *** power of recording attack resilient unaided authentication services(RARUAS)is undeniable as,in some aspects,they are even capable of offering better security than the biometric based authentication ***,high login complexity of these RARUAS makes them far from usable in *** adopted information leakage control strategies have often been identified as the primary cause behind such high login *** recent proposals have made some significant efforts in designing a usable RARUAS by reducing its login complexity,most of them have failed to achieve the desired usability *** this paper,we have introduced a new notion of controlling the information leakage *** maintaining a good security standard,the introduced idea helps to reduce the login complexity of our proposed mechanism—named as Textual-Graphical Password-based Mechanism or TGPM,by a significant *** with resisting the recording attack,TGPM also achieves a remarkable property of threat *** the best of our knowledge,TGPM is the first RARUAS,which can both prevent and detect the activities of the opportunistic recording attackers who can record the complete login activity of a genuine user for a few login *** study reveals that TGPM assures much higher session resiliency compared to the existing authentication services,having the same or even higher login ***,TGPM stores the password information in a distributed way and thus restricts the adversaries to learn the complete secret from a single compromised server.A thorough theoretical analysis has been performed to prove the strength of our proposal from both the security and usability *** have also conducted an experimental study to support the theoretical argument made on the usability standard of TGPM.

关键词： authentication recording attack premature attack opportunistic adversary leakage control threat prevention threat detection

Machine Learning Based Cybersecurity threat detection for Secure IoT Assisted Cloud Environment

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Computer Systems Science & Engineering 2023年第10期47卷 855-871页

作者： Z.Faizal Khan Saeed M.Alshahrani Abdulrahman Alghamdi Someah Alangari Nouf Ibrahim Altamami Khalid A.Alissa Sana Alazwari Mesfer Al Duhayyim Fahd N.Al-Wesabi Department of Computer Engineering College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Computer Science College of Science and Humanities DawadmiShaqra UniversityShaqraSaudi Arabia Department of Mathematics College of EducationShaqra UniversityShaqraSaudi Arabia Saudi Aramco Cybersecurity Chair Networks and Communications DepartmentCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAlaflaj16828Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia

The Internet of Things(IoT)is determine enormous economic openings for industries and allow stimulating innovation which obtain between domains in childcare for eldercare,in health service to energy,and in developed to *** develops a difficult problem in IoT platform whereas the presence of cyber-attack requires that *** progress of automatic devices for cyber-attack classifier and detection employing Artificial Intelligence(AI)andMachine Learning(ML)devices are crucial fact to realize security in IoT *** can be required for minimizing the issues of security based on IoT devices ***,this research proposal establishes novel mayfly optimized with Regularized Extreme Learning Machine technique called as MFO-RELM model for Cybersecurity threat classification and detection fromthe cloud and IoT *** proposed MFORELM model provides the effective detection of cybersecurity threat which occur in the cloud and IoT *** accomplish this,the MFO-RELM technique pre-processed the actual cloud and IoT data as to meaningful ***,the proposed models will receive the pre-processing data and carry out the classifier *** boosting the efficiency of the proposed models,theMFOtechnique was utilized to *** experiential outcome of the proposed technique was tested utilizing the standard CICIDS 2017 dataset,and the outcomes are examined under distinct aspects.

关键词： Mayfly optimization machine learning artificial intelligence cybersecurity threat detection

Securing Cloud-Encrypted Data:Detecting Ransomware-as-a-Service(RaaS)Attacks through Deep Learning Ensemble

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Computers, Materials & Continua 2024年第4期79卷 857-873页

作者： Amardeep Singh Hamad Ali Abosaq Saad Arif Zohaib Mushtaq Muhammad Irfan Ghulam Abbas Arshad Ali Alanoud Al Mazroa School of Computer Information Sciences University of CumberlandsWilliamsburgKentucky40769USA Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Electrical Engineering The University of LahoreLahore54000Pakistan Faculty of Computer and Information Systems Islamic University of MadinahMadinah42351Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries,especially in light of the growing number of cybersecurity threats.A major and everpresent threat is Ransomware-as-a-Service(RaaS)assaults,which enable even individuals with minimal technical knowledge to conduct ransomware *** study provides a new approach for RaaS attack detection which uses an ensemble of deep learning *** this purpose,the network intrusion detection dataset“UNSWNB15”from the Intelligent Security Group of the University of New South Wales,Australia is *** the initial phase,the rectified linear unit-,scaled exponential linear unit-,and exponential linear unit-based three separate Multi-Layer Perceptron(MLP)models are ***,using the combined predictive power of these three MLPs,the RansoDetect Fusion ensemble model is introduced in the suggested *** proposed ensemble technique outperforms previous studieswith impressive performance metrics results,including 98.79%accuracy and recall,98.85%precision,and 98.80%*** empirical results of this study validate the ensemble model’s ability to improve cybersecurity defenses by showing that it outperforms individual *** expanding the field of cybersecurity strategy,this research highlights the significance of combined deep learning models in strengthening intrusion detection systems against sophisticated cyber threats.

关键词： Cloud encryption RaaS ensemble threat detection deep learning cybersecurity

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

Adaptive Polling Rate for SNMP for Detecting Elusive DDOS

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Journal of Cyber Security 2022年第1期4卷 17-28页

作者： Yichiet Aun Yen-Min Jasmina Khaw Ming-Lee Gan Vasaki Ponnusamy Faculty of Information Communication and Technology Kampar31900Malaysia

Resilient network infrastructure is pivotal for business entities that are growing reliance on the *** Denial-of-Service(DDOS)is a common network threat that collectively overwhelms and exhausts network resources using coordinated botnets to interrupt access to network services,devices,and *** is typically deployed to detect DDOS based on Snort *** being fairly accurate,IDS operates on a computeintensive packet inspection technique and lacks ***,SNMP is a comparably lightweight countermeasure for fast ***,this SNMP trigger is often circumvented if the DDOS burst rate is coordinated to flood the network smaller than theSNMPpolling ***,SNMP does not scale well if the poll rate is set extremely fine for improved detection *** this paper,a lightweight 3D SNMP scaling method is proposed to optimize the SNMP poll rate forDDOSmitigation *** 3D-SNMP uses horizontal scaling to dynamically adjust the optimal poll rate through random packet inspection that is *** a sign of DDOS is detected,3D-SNMP scales down the poll rate for finer *** DDOS subsides,3D-SNMP scales the poll rate up for faster DDOS *** equilibrium between scalability and accuracy is determined on the fly depending on the types of DDOS variants.3D-SNMP also adds a vertical scaling to detect non-salient DDOS that falls below the detection *** experimental results showed that 3D-SNMP achieved DDOS detection of 92%while remaining scalable to different DDOS variants and volumes.

关键词： SNMP DDOS poll rate network security threat detection