Mesopotamian Journal of Cyber Security最新文献

{"title":"Cybersecurity and Artificial Intelligence Applications: A Bibliometric Analysis Based on Scopus Database","authors":"O. Albahri, A. Alamoodi","doi":"10.58496/mjcsc/2023/018","DOIUrl":"https://doi.org/10.58496/mjcsc/2023/018","url":null,"abstract":"The intersection of Cybersecurity and AI has garnered increasing attention in recent years due to the growing importance of securing digital assets in an interconnected world. This bibliometric analysis aims to provide valuable insights into the research trends and developments within this interdisciplinary domain. Using data extracted from the Scopus database, a total of 501 papers were selected and analyzed to uncover key patterns and themes. The methodology involved conducting a comprehensive literature search using specific keywords related to Cybersecurity and AI applications. The initial search yielded 736 papers, which were subsequently filtered to include research articles, conference papers, editorial papers, and review papers, resulting in the final dataset of 501 papers. The analysis of publication trends revealed a remarkable surge in research output since 2015, indicating the escalating interest in this field. Collaboration patterns among researchers and institutions were analyzed through co-authorship networks, highlighting a well-connected research community that fosters knowledge exchange. Keyword analysis exposed common areas of application, such as network security, deep learning, and the Internet of Things, underscoring the importance of AI technologies in enhancing Cybersecurity measures. Furthermore, examination of the most cited documents showcased influential contributions that have shaped the trajectory of Cybersecurity and AI research. The study emphasizes the significance of Cybersecurity and AI applications research, considering the ever-increasing reliance on technology in various aspects of modern life. By integrating AI technologies, Cybersecurity measures can be fortified with automated threat detection, adaptive defense mechanisms, and proactive risk mitigation, thereby bolstering overall cybersecurity resilience. The findings of this bibliometric analysis have several implications for researchers and policymakers. Researchers can leverage the identified trends and gaps to explore new research directions and potential collaborations. Policymakers can utilize these insights to make informed decisions regarding resource allocation for research initiatives aimed at addressing emerging Cybersecurity challenges. This bibliometric analysis provides a comprehensive overview of the evolving landscape of Cybersecurity and AI applications research. It underscores the growing importance of this interdisciplinary field and its potential to reshape the future of cybersecurity. As technology continues to advance, the integration of AI in Cybersecurity will play a pivotal role in safeguarding digital assets and ensuring the secure functioning of critical systems in an increasingly interconnected world.","PeriodicalId":369414,"journal":{"name":"Mesopotamian Journal of Cyber Security","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114082995","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}

{"title":"Machine Learning-Based Detection of Smartphone Malware: Challenges and Solutions","authors":"","doi":"10.58496/mjcs/2023/017","DOIUrl":"https://doi.org/10.58496/mjcs/2023/017","url":null,"abstract":"The goal of this research is to review the researcher's different attempts with respect to new and emerging technology in malware detection techniques based on machine learning approaches over smartphones. The aim is to evaluate and benchmark these techniques, identify the current landscape of research in this area, and construct a cohesive taxonomy. The available options and gaps will be analyzed to provide valuable insights for researchers regarding the technological environments within this research area. A deep analysis review was conducted to identify studies addressing smartphone security based on machine learning approaches in order to identify all related articles. The outcomes of the last classification scheme of these articles were categorized into types of detection: dynamic analysis, static analysis, hybrid analysis, and uniform resource locator (URL) analysis. The evaluation criteria used in malware detection techniques, with respect to machine learning approaches for smartphones, include accuracy, precision rates (including true positive, false positive, true negative, false negative), training time, f-measure, detection time, area under the curve, true positive, true negative, false positive, false negative, and error rate. Additionally, our classification covers the main machine learning techniques used in the reviewed studies. The taxonomy includes three distinct layers, each reflecting one aspect of the analysis. We also reviewed the details of various types of malicious and benign datasets used within malware detection. Furthermore, open issues and challenges were identified in terms of evaluation and benchmarking, which jeopardize the utilization of this technology. We have described a new recommendation pathway solution that aims to enhance the measurement process of smartphone security applications.","PeriodicalId":369414,"journal":{"name":"Mesopotamian Journal of Cyber Security","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129764124","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}

{"title":"A Comprehensive Review of Security Measures in Database Systems: Assessing Authentication, Access Control, and Beyond","authors":"Habeeb Omotunde, Maryam Ahmed","doi":"10.58496/mjcsc/2023/016","DOIUrl":"https://doi.org/10.58496/mjcsc/2023/016","url":null,"abstract":"This paper presents a comprehensive review of security measures in database systems, focusing on authentication, access control, encryption, auditing, intrusion detection, and privacy-enhancing techniques. It aims to provide valuable insights into the latest advancements and best practices in securing databases. The review examines the challenges, vulnerabilities, and mitigation strategies associated with database security. It explores various authentication methods, access control models, encryption techniques, auditing and monitoring approaches, intrusion detection systems, and data leakage prevention mechanisms. The paper also discusses the impact of emerging trends such as cloud computing, big data, and the Internet of Things on database security. By synthesizing existing research, this review aims to contribute to the advancement of database security and assist organizations in protecting their sensitive data.","PeriodicalId":369414,"journal":{"name":"Mesopotamian Journal of Cyber Security","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122577325","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}

{"title":"The impact of Blockchain technique on trustworthy healthcare sector","authors":"","doi":"10.58496/mjcs/2023/015","DOIUrl":"https://doi.org/10.58496/mjcs/2023/015","url":null,"abstract":"Following the COVID epidemic, the healthcare sector faced numerous issues as telehealth became more prevalent and the necessity for a safe and efficient healthcare record system became critical. Many issues plague the healthcare industry today, including security, trust, data availability, and drug traceability. Blockchain technology is a relatively new technology that has demonstrated its effectiveness in a variety of industries, including finance, banking, bitcoin, and healthcare. This research investigates the impact of blockchain on the trustworthiness of healthcare. The authors adopt a descriptive-analytical methodology. The trustworthiness of the healthcare sector is considered a dependent variable. However, blockchain technology is considered an independent variable. Three dimensions of dependent variables were explored: integrity, confidence, and reliability. For studying this impact, a questionnaire of 25 items was created and distributed to the stakeholders in the healthcare sector in the north of Lebanon. The collected answers were analyzed using the SPSS application and statistical tools. The results verify that the use of blockchain technology has a great impact on healthcare trustworthiness with its three dimensions (integrity, confidence, and reliability).","PeriodicalId":369414,"journal":{"name":"Mesopotamian Journal of Cyber Security","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115495268","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}

{"title":"A Process of Penetration Testing Using Various Tools","authors":"","doi":"10.58496/mjcs/2023/014","DOIUrl":"https://doi.org/10.58496/mjcs/2023/014","url":null,"abstract":"In the present world, information and data are the greatest assets one can possess. If one cannot secure their information from cyber-attacks, they would lose everything in the blink of an eye. Penetration testing can help reduce this cyber-risk exposure of clients' data and protect them. Penetration testing (also called \"pen testing\") is a part of ethical hacking that exposes the weak areas, vulnerabilities, or loopholes in the core of a PC, its networks, and its applications with the purpose of securing the system. The main idea of pen testing is to find vulnerabilities in systems and fix them before attackers can take advantage of them. These vulnerabilities are identified, exploited, and analyzed in five phases: information gathering, scanning, gaining access, maintaining access, and covering tracks. Penetration testing is done regularly in order to maintain high-security standards. As it pertains to any organization’s secrecy and privacy, this testing is also constrained by a number of legal agreements. The work provided by many researchers in the field of penetration (PEN) testing is reviewed and analyzed in this paper. This report gives a detailed description of the process and tools used to conduct penetration testing","PeriodicalId":369414,"journal":{"name":"Mesopotamian Journal of Cyber Security","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128002752","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}

{"title":"An Analysis of the Internet of Everything","authors":"","doi":"10.58496/mjcs/2023/013","DOIUrl":"https://doi.org/10.58496/mjcs/2023/013","url":null,"abstract":"","PeriodicalId":369414,"journal":{"name":"Mesopotamian Journal of Cyber Security","volume":"189 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134175142","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}

{"title":"Review on Blockchain Technology : Architecture, Characteristics, Benefits, Algorithms, Challenges and Applications","authors":"","doi":"10.58496/mjcs/2023/012","DOIUrl":"https://doi.org/10.58496/mjcs/2023/012","url":null,"abstract":"Today, blockchain technology is playing a vital role in the professional world as one of the most important discoveries and creative developments. Blockchain technology is continuously advancing and revolutionizing our society. An overview of blockchain technology is presented in this paper. The blockchain is a distributed ledger technology that allows different transactions and operations to be recorded in a chain of blocks without requiring a third party. A growing number of businesses and industrial communities are exploring blockchain technology as a technological option. In the last decade, blockchain technology has completely changed the paradigm of computer applications. Blockchain was created with the vision of creating an immutable, confidential, open-source, decentralized P2P network located on a decentralized network to facilitate data sharing. An overview of blockchain applications in different areas is presented. We provide a timely summary of blockchain research for individuals and organizations interested in this field. This paper presents an overview of blockchain technology, listing all the key characteristics, benefits, and features which make blockchain technology both superior and unique. We also discuss popular consensus protocols and taxonomy for blockchain systems.","PeriodicalId":369414,"journal":{"name":"Mesopotamian Journal of Cyber Security","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125942172","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}

{"title":"Cyber Security System Based on Machine Learning Using Logistic Decision Support Vector","authors":"Sahaya Sheela M, Hemanand D, Ranadheer Reddy Vallem","doi":"10.58496/mjcs/2023/011","DOIUrl":"https://doi.org/10.58496/mjcs/2023/011","url":null,"abstract":"Nowadays, we are moving towards cybersecurity against digital attacks to protect systems, networks, and data in developing areas. A collection of technologies and processes is at the core of cybersecurity. A network security system is a feature of network and computer (host) security. Cybercrime leads to billion-dollar losses. Given these crimes, the security of computer systems has become essential to reduce and avoid the impact of cybercrime. We propose the Logistics Decision Support Vector (LDSV) algorithm dealing with this problem. Initially, we collected the KDD Cup 99 dataset to create a network intrusion detection, such as penetrations or attacks, a prognosis model that varies between the \"Non Malicious\" and \"Malicious\" standard links. These method finds the cyber-attack category based on the behavior features. In the second step, data preprocessing should be cleaned from errors, and raw data should be converted into a prepared dataset. The third step is Feature Selection (FS) techniques often improve the feature selection process in an Intrusion Detection System (IDS) that is more convenient for using the mean of the Chi-square test (MAC) method. Finally, a classification is done to classify and detect the network intrusion detection based on LDSV for Cyber security. The proposed LDSV simulation is based on the Precision F-Measure, Recall, and Accuracy for the best result.","PeriodicalId":369414,"journal":{"name":"Mesopotamian Journal of Cyber Security","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127362897","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}

{"title":"Exploring the Top Five Evolving Threats in Cybersecurity: An In-Depth Overview","authors":"Maad M. Mijwil, Omega John Unogwu, Y. Filali, I. Bala, Humam Al-Shahwani","doi":"10.58496/mjcs/2023/010","DOIUrl":"https://doi.org/10.58496/mjcs/2023/010","url":null,"abstract":"The term cybersecurity refers to an environment capable of protecting digital devices, networks and information from unauthorized access and preventing data theft or alteration. It is composed of a collection of carefully crafted techniques, processes, and practices to protect sensitive information and deterring cyber-attacks. In the recent period, the domain of cybersecurity has undergone rapid growth in response to the increasing cyber threats. Cybersecurity includes important tactics that help protect the digital environment, which are firewalls, encryption, secure passwords, and threat detection and response systems. Employees must be trained on these tactics. This article will discuss the five most pressing challenges facing the cybersecurity industry today that must be taken into account by businesses, organizations, and individuals in order to secure their confidential data from cybercrime. The conclusion of the article highlighted the significance of growing awareness about cybersecurity risks in order to effectively handle digital environments and protect them from any electronic threats.","PeriodicalId":369414,"journal":{"name":"Mesopotamian Journal of Cyber Security","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116295775","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}

{"title":"An efficient cyber security system based on flow-based anomaly detection using Artificial neural network","authors":"J. Hephzipah, Ranadheer Reddy Vallem, M. Sheela, G. Dhanalakshmi","doi":"10.58496/mjcs/2023/009","DOIUrl":"https://doi.org/10.58496/mjcs/2023/009","url":null,"abstract":"Cyber security is developing factor for protecting internet resources by handing various monitoring feature based support to improve the security. Increasing internet cries in the defined facts for need of advance met in cyber security. Most internet attacker’s theft the information through malicious activities, false data injection, hacking and make soon creating procedures. In most cases cyber sercuity failed to detect the malicious activities because the monitoring feature analyses improper to predict the result in previous machine learning algorithms. TO resolve this problem to propose an advance cyber security based on flow-based anomaly detection using Min max game theory optimized artificial neural network (MMGT-ANN). The reprocessing was carried out with KDD crime dataset. Then Data driven network model is applied to monitor the feature margins and defect scaling rate. Based on the feature scaling rate Transmission Flow defect rate is estimated and applied with Min max Game theory to select the feature limits. Then features are trained with optimized ANN to detect the crime rate. By the attention of the proposed system achieves higher performance in precision rate to attain higher detection accuracy with lower time complexity compared to the other system.","PeriodicalId":369414,"journal":{"name":"Mesopotamian Journal of Cyber Security","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117309875","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}