International Journal of Safety and Security Engineering最新文献_第2页

Network Master Node Assessed Trust Factor with Arbitrary Neighbor Assessment for Secure Route Detection in 6G Enabled Wireless Sensor Networks 在支持 6G 的无线传感器网络中，利用网络主节点评估信任因子和任意邻居评估实现安全路由检测

Q3 Engineering

International Journal of Safety and Security Engineering

Pub Date : 2024-02-29 DOI: 10.18280/ijsse.140111

Pavan Vamsi Mohan Movva, Radhika Rani Chintala

ABSTRACT

摘要

引用次数: 0

Quantitative Analysis of Global Research Trends in Crowd Management for Tawaf: A Bibliometric Study (1997–2023) 对全球 Tawaf 人群管理研究趋势的定量分析：文献计量学研究（1997-2023 年）

Q3 Engineering

International Journal of Safety and Security Engineering

Pub Date : 2024-02-29 DOI: 10.18280/ijsse.140107

A. M. Qamar, Abdulrahman Saad Altami, Ali Ayeel Alsamanni

ABSTRACT

摘要

引用次数: 0

Student Voting on ICP Blockchain: A Decentralized Web3 Approach-An Infrastructure Protection System ICP 区块链上的学生投票：去中心化的 Web3 方法--基础设施保护系统

Q3 Engineering

International Journal of Safety and Security Engineering

Pub Date : 2024-02-29 DOI: 10.18280/ijsse.140109

Koteswara Rao Kodepogu, Mudigonda Dharmateja, Jillela Manoj Kumar, Muraboina Hari Pavan Gopi Krishna, Kodali Ramu

ABSTRACT

摘要

引用次数: 0

Advances in Malware Analysis and Detection in Cloud Computing Environments: A Review 云计算环境中恶意软件分析与检测的进展：综述

Q3 Engineering

International Journal of Safety and Security Engineering

Pub Date : 2024-02-29 DOI: 10.18280/ijsse.140122

S. M. Rao, Arpit Jain

Cloud computing, integral for data storage and online services, presents significant advantages over traditional data storage and distribution methods, including enhanced convenience, on-demand storage, scalability, and cost efficiency. Its growing adoption in securing Internet of Things (IoT) and cyber-physical systems (CPS) against various cyber threats offers numerous opportunities. Despite the continuous evolution of malware and the lack of a universally effective detection method, cloud environments provide a promising approach for malware detection. Cloud computing, recognized for its efficiency, scalability, flexibility, and reliability on elastic resources, is widely utilized in the IT industry to support IT infrastructure and services. However, one of the foremost security challenges faced is malware attacks. Certain antivirus scanners struggle to detect metamorphic or encrypted malware in cloud environments due to complexity and scale, allowing such threats to evade detection. High detection rates with precision in reducing false positives are essential. Machine learning (ML) classifiers, a vital component in Artificial Intelligence (AI) systems, require training on extensive data volumes to develop credible models with high detection rates. Traditional detection methods face challenges in identifying complex malware, as modern malware employs contemporary packaging and obfuscation techniques to circumvent security measures. This paper provides a detailed discussion on detecting malware in cloud environments and the advantages of cloud computing in safeguarding IoT and CPS from cyber attacks. It presents a survey on malware analysis and detection models, aiding researchers in identifying limitations of traditional malware detection models in cloud environments and inspiring the design of innovative models with enhanced quality of service

云计算是数据存储和在线服务不可或缺的组成部分，与传统的数据存储和分发方法相比，它具有显著的优势，包括更高的便利性、按需存储、可扩展性和成本效益。在保护物联网（IoT）和网络物理系统（CPS）免受各种网络威胁方面，云计算的应用日益广泛，这为我们提供了众多机遇。尽管恶意软件在不断演变，而且缺乏普遍有效的检测方法，但云环境为恶意软件检测提供了一种前景广阔的方法。云计算因其在弹性资源上的高效性、可扩展性、灵活性和可靠性而广受认可，在 IT 行业被广泛用于支持 IT 基础设施和服务。然而，恶意软件攻击是面临的首要安全挑战之一。由于云环境的复杂性和规模，某些防病毒扫描程序很难检测到云环境中的变形或加密恶意软件，从而使此类威胁逃避检测。高检测率和精确度对于减少误报至关重要。机器学习（ML）分类器是人工智能（AI）系统的重要组成部分，需要对大量数据进行训练，才能开发出具有高检测率的可靠模型。传统的检测方法在识别复杂的恶意软件时面临挑战，因为现代恶意软件采用现代的包装和混淆技术来规避安全措施。本文详细讨论了在云环境中检测恶意软件以及云计算在保护物联网和 CPS 免受网络攻击方面的优势。本文对恶意软件分析和检测模型进行了调查，帮助研究人员识别云环境中传统恶意软件检测模型的局限性，并启发设计可提高服务质量的创新模型。

{"title":"Advances in Malware Analysis and Detection in Cloud Computing Environments: A Review","authors":"S. M. Rao, Arpit Jain","doi":"10.18280/ijsse.140122","DOIUrl":"https://doi.org/10.18280/ijsse.140122","url":null,"abstract":"Cloud computing, integral for data storage and online services, presents significant advantages over traditional data storage and distribution methods, including enhanced convenience, on-demand storage, scalability, and cost efficiency. Its growing adoption in securing Internet of Things (IoT) and cyber-physical systems (CPS) against various cyber threats offers numerous opportunities. Despite the continuous evolution of malware and the lack of a universally effective detection method, cloud environments provide a promising approach for malware detection. Cloud computing, recognized for its efficiency, scalability, flexibility, and reliability on elastic resources, is widely utilized in the IT industry to support IT infrastructure and services. However, one of the foremost security challenges faced is malware attacks. Certain antivirus scanners struggle to detect metamorphic or encrypted malware in cloud environments due to complexity and scale, allowing such threats to evade detection. High detection rates with precision in reducing false positives are essential. Machine learning (ML) classifiers, a vital component in Artificial Intelligence (AI) systems, require training on extensive data volumes to develop credible models with high detection rates. Traditional detection methods face challenges in identifying complex malware, as modern malware employs contemporary packaging and obfuscation techniques to circumvent security measures. This paper provides a detailed discussion on detecting malware in cloud environments and the advantages of cloud computing in safeguarding IoT and CPS from cyber attacks. It presents a survey on malware analysis and detection models, aiding researchers in identifying limitations of traditional malware detection models in cloud environments and inspiring the design of innovative models with enhanced quality of service","PeriodicalId":37802,"journal":{"name":"International Journal of Safety and Security Engineering","volume":"16 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140412234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Genetic Algorithm Using Feistel and Genetic Operator Acting at the Bit Level for Images Encryption 利用费斯特尔和遗传算子在比特级作用的遗传算法进行图像加密

Q3 Engineering

International Journal of Safety and Security Engineering

Pub Date : 2024-02-29 DOI: 10.18280/ijsse.140102

Abdellah Abid, Mariem Jarjar, Mourad Kattass, Hicham Rrghout, A. Jarjar, A. Benazzi

ABSTRACT

摘要

引用次数: 0

Comprehensive Risk Classification and Mitigation in the Petroleum Cyber-Physical Systems of the Oil and Gas Industry 石油天然气行业石油网络物理系统的全面风险分类与缓解

Q3 Engineering

International Journal of Safety and Security Engineering