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Artificial Algae Optimization with Deep Belief Network Enabled ransomware detection in IoT Environment

Computer Systems Science & Engineering 2023年第8期46卷 1293-1310页

作者： Mesfer Al Duhayyim Heba G.Mohamed Fadwa Alrowais Fahd N.Al-Wesabi Anwer Mustafa Hilal Abdelwahed Motwakel Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityRiyadhSaudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science and Art at MuhaeylKing Khalid UniversityAbhaSaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The Internet of Things(IoT)has gained more popularity in research because of its large-scale challenges and *** security was the main concern when witnessing the fast development in its applications and *** was a dreary task to independently set security systems in every IoT gadget and upgrade them according to the newer ***,machine learning(ML)techniques optimally use a colossal volume of data generated by IoT *** Learning(DL)related systems were modelled for attack detection in *** the current security systems address restricted attacks and can be utilized outdated datasets for *** study develops an Artificial Algae Optimization Algorithm with Optimal Deep Belief Network(AAA-ODBN)Enabled ransomware detection in an IoT *** presented AAAODBN technique mainly intends to recognize and categorize ransomware in the IoT *** presented AAA-ODBN technique follows a three-stage process:feature selection,classification,and parameter *** the first stage,the AAA-ODBN technique uses AAA based feature selection(AAA-FS)technique to elect feature ***,the AAA-ODBN technique employs the DBN model for ransomware *** last,the dragonfly algorithm(DFA)is utilized for the hyperparameter tuning of the DBN technique.A sequence of simulations is implemented to demonstrate the improved performance of the AAA-ODBN *** experimental values indicate the significant outcome of the AAA-ODBN model over other models.

关键词： Internet of things deep learning cybersecurity ransomware detection feature selection

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Majority Voting ransomware detection System

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Journal of Information Security 2023年第4期14卷 264-293页

作者： Simon R. Davies Richard Macfarlane William J. Buchanan School of Computing Edinburgh Napier University Edinburgh UK

Crypto-ransomware remains a significant threat to governments and companies alike, with high-profile cyber security incidents regularly making headlines. Many different detection systems have been proposed as solutions to the ever-changing dynamic landscape of ransomware detection. In the majority of cases, these described systems propose a method based on the result of a single test performed on either the executable code, the process under investigation, its behaviour, or its output. In a small subset of ransomware detection systems, the concept of a scorecard is employed where multiple tests are performed on various aspects of a process under investigation and their results are then analysed using machine learning. The purpose of this paper is to propose a new majority voting approach to ransomware detection by developing a method that uses a cumulative score derived from discrete tests based on calculations using algorithmic rather than heuristic techniques. The paper describes 23 candidate tests, as well as 9 Windows API tests which are validated to determine both their accuracy and viability for use within a ransomware detection system. Using a cumulative score calculation approach to ransomware detection has several benefits, such as the immunity to the occasional inaccuracy of individual tests when making its final classification. The system can also leverage multiple tests that can be both comprehensive and complimentary in an attempt to achieve a broader, deeper, and more robust analysis of the program under investigation. Additionally, the use of multiple collaborative tests also significantly hinders ransomware from masking or modifying its behaviour in an attempt to bypass detection. The results achieved by this research demonstrate that many of the proposed tests achieved a high degree of accuracy in differentiating between benign and malicious targets and suggestions are offered as to how these tests, and combinations of tests, could be adapted to fur

关键词： ransomware detection Malice Score Score Card Malware NapierOne Dataset

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TLERAD: Transfer Learning for Enhanced ransomware Attack detection

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Computers, Materials & Continua 2024年第11期81卷 2791-2818页

作者： Isha Sood Varsha Sharm a School of Information Technology Rajiv Gandhi Proudyogiki VishwavidyalayaBhopal462033India

ransomware has emerged as a critical cybersecurity threat,characterized by its ability to encrypt user data or lock devices,demanding ransom for their *** ransomware detection methods face limitations due to their assumption of similar data distributions between training and testing phases,rendering them less effective against evolving ransomware *** paper introduces TLERAD(Transfer Learning for Enhanced ransomware Attack detection),a novel approach that leverages unsupervised transfer learning and co-clustering techniques to bridge the gap between source and target domains,enabling robust detection of both known and unknown ransomware *** proposed method achieves high detection accuracy,with an AUC of 0.98 for known ransomware and 0.93 for unknown ransomware,significantly outperforming baseline *** experiments demonstrate TLERAD’s effectiveness in real-world scenarios,highlighting its adapt-ability to the rapidly evolving ransomware *** paper also discusses future directions for enhancing TLERAD,including real-time adaptation,integration with lightweight and post-quantum cryptography,and the incorporation of explainable AI techniques.

关键词： ransomware detection transfer learning unsupervised learning co-clustering cybersecurity machine learning lightweight cryptography post-quantum cryptography explainable AI TLERAD

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