International Journal for Electronic Crime Investigation最新文献

{"title":"The role of Artificial Intelligence in Cyber Security and Incident Response","authors":"Syed Khurram Hassan, Asif Ibrahim","doi":"10.54692/ijeci.2023.0702154","DOIUrl":"https://doi.org/10.54692/ijeci.2023.0702154","url":null,"abstract":"The escalating number and intricacy of cyber attacks have underscored the urgent requirement for innovative solutions to bolster the security of digital infrastructure. Among these solutions, Artificial Intelligence (AI) emerges as a promising technology with the potential to significantly enhance cyber security and incident response. It has the aptitude to improve the speed and accuracy of threat detection, response and mitigation while also reducing the workload on security professionals. This research paper focuses on the role of AI in key areas of cyber security and incident response, specifically vulnerability assessment, intrusion detection and prevention, and digital forensics analysis. It elucidates how AI, with its innate capabilities, can be a game-changer by empowering organizations to detect, respond to, and mitigate threats more effectively. However, AI is not a silver bullet for Cyber Security. Like any technology, it possesses its limitations and potential vulnerabilities. Consequently, this paper also addresses the need for ongoing development in the field of AI to overcome these limitations and challenges. By recognizing the need for continuous advancements, the research paper emphasizes the importance of future research and development efforts to maximize the potential benefits of AI in the realm of cyber security.","PeriodicalId":156403,"journal":{"name":"International Journal for Electronic Crime Investigation","volume":"83 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139363596","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":"Optimizing Virtualization for Client-Based Workloads in Cloud Computing","authors":"Muhammad Imran Sarwar","doi":"10.54692/ijeci.2023.0702151","DOIUrl":"https://doi.org/10.54692/ijeci.2023.0702151","url":null,"abstract":"Cloud computing has transformed the IT field by offering adaptable and versatile resources to cater to the increasing demands of businesses and organizations. Virtualization technologies are instrumental in facilitating the efficient deployment and management of resources within cloud environments. However, there are notable concerns regarding the security implications of virtualization in the cloud. This research paper thoroughly examines the security aspects of virtualization technologies in cloud computing, primarily focusing on identifying potential weaknesses, risks, and strategies to mitigate security threats. Additionally, the study investigates the security features and mechanisms provided by leading virtualization platforms and management tools. It scrutinizes access controls, isolation methods, network security, data protection, and integrity mechanisms offered by virtualization technologies to safeguard the cloud infrastructure and customer data. Furthermore, the paper addresses emerging security concerns associated with containerization technologies, encompassing vulnerabilities related to container escape, risks stemming from shared kernels, and issues concerning image integrity. It explores the effectiveness of container security measures, such asisolation, sandboxing, and access controls, in reducing these risks. Lastly, the paper summarizes the main findings and provides recommendations to enhance the security of virtualization technologies in cloud computing. It emphasizes the importance of continuous monitoring, regular security updates, robust access controls, and threat intelligence integration to mitigate security risks and uphold a secure cloud infrastructure.","PeriodicalId":156403,"journal":{"name":"International Journal for Electronic Crime Investigation","volume":"149 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139363793","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":"Detecting Phishing Websites using Decision Trees: A Machine Learning Approach","authors":"Ashar Ahmed Fazal, Maryam Daud","doi":"10.54692/ijeci.2023.0702155","DOIUrl":"https://doi.org/10.54692/ijeci.2023.0702155","url":null,"abstract":"This study emphasises the value of feature selection and preprocessing in improving model performance and demonstrates the efficiency of decision trees in identifying phishing websites. Internet users are significantly threatened by phishing websites, hence a strong detection strategy is required. The Phishing Websites Dataset from the UCI Machine Learning Repository, which contains 30 website-related features, is used in the study together with a decision tree classifier from the scikit-learn package. The dataset is preprocessed to remove invalid and missing values, and the most pertinent features are chosen for model training. 80% of the dataset is utilised to train the model, while the remaining 20% is used for testing. The findings demonstrate the decision tree classifier's precision in detecting phishing websites, scoring 95.97% accurate and showing a high true positive rate (96.64%) and a negligible (3.04%) false positive rate using the confusion matrix. This study highlights the significance of feature selection and preprocessing for optimal model performance in addition to validating the efficacy of decision trees in phishing detection. The method described here can be helpful for businesses and individuals looking to protect themselves from phishing assaults, and the given data visualisations make it easier to understand datasets and assess models.","PeriodicalId":156403,"journal":{"name":"International Journal for Electronic Crime Investigation","volume":"42 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139363854","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":"Analysis of Network Security in IoT-based Cloud Computing Using Machine Learning","authors":"Humaira Naeem","doi":"10.54692/ijeci.2023.0702153","DOIUrl":"https://doi.org/10.54692/ijeci.2023.0702153","url":null,"abstract":"Network security in IoT-based cloud computing can benefit greatly from the application of machine learning techniques. IoT devices introduce unique security challenges with their large-scale deployments and diverse nature. Machine learning can help address these challenges by analyzing IoT network traffic, detecting anomalies, identifying potential threats, and enhancing overall network security. The security of cloud networks is validated using binary classification to detect attacks. Random forest classifiers achieved an accuracy of 96%, while K nearest classifier had an accuracy of 93% and a precision value of 0.96. The proposed model ensures security of big data against intrusion attacks on the network. Although machine learning techniques can be powerful for protecting cloud computing networks, challenges still need to be addressed before widespread adoption. Understanding the potential and limitations of machine learning approaches to network security canhelp researchers and practitioners develop more effective strategies for safeguarding their systems in an increasingly interconnected world. Network security of big data in cloud computing can be enhanced by applying machine learning techniques. Machine learning algorithms can analyze large amounts of data to detect patterns, anomalies, and potential security threats. Here are several ways machine learning can be utilized to improve network security in the context of big data and cloud computing.","PeriodicalId":156403,"journal":{"name":"International Journal for Electronic Crime Investigation","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139363991","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":"Blockchain Data Analytics: A Review","authors":"Rabia Aslam Khan, Muhammad Bilal But, Sabreena Nawaz","doi":"10.54692/ijeci.2023.0702152","DOIUrl":"https://doi.org/10.54692/ijeci.2023.0702152","url":null,"abstract":"Blockchain technology has emerged as a transformative force with widespread applications across various industries. In particular, the analysis of blockchain data has become crucial for crypto businesses and financial institutions seeking to protect transactions from illicit activities, minimize the risk of financial crimes, and ensure compliance with regulations. This paper presents a comprehensive review of blockchain data analytics, focusing on its significance in these domains. The paper examines the advancements and possibilities in blockchain data analytics, shedding light on their transformative potential. It provides an overview of the techniques and tools used for analyzing blockchain data, including transaction tracing, pattern recognition, and anomaly detection. Moreover, it explores the challenges and opportunities associated with blockchain data analytics, such as scalability, privacy concerns, and regulatory frameworks.","PeriodicalId":156403,"journal":{"name":"International Journal for Electronic Crime Investigation","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139363883","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 Data Security and multi-cloud Privacy concerns","authors":"Nadia Tabassum, Humaria Naeem, Asma Batool","doi":"10.54692/ijeci.2023.0701128","DOIUrl":"https://doi.org/10.54692/ijeci.2023.0701128","url":null,"abstract":"The security, privacy, and challenges of establishing trust in cloud computing are examined in this paper. It discusses the issues that must be resolved to guarantee the security, privacy, and reliability of data processed, stored, and shared in cloud architecture. Cloud computing is a rapidly growing field, with more and more individuals and organizations adopting it as their preferred data storage and processing method. However, with this growth comes the need for increased attention to privacy, security, and trust in cloud computing. In this paper, we review the current state of privacy, security, and trust in cloud computing and examine the various strategies and technologies being used to address these concerns. We discuss the importance of end-to-end encryption, strong access controls, and data anonymization techniques in protecting user data in the cloud. Additionally, we analyze the role of trusted third parties, such as auditors and certifications, in ensuring the integrity of cloud services. Finally, we consider the impact of emerging technologies, such as blockchain and homomorphic encryption, on the future of privacy, security, and trust in cloud computing. Overall, our analysis highlights the need for ongoing research and development in this area to ensure that cloud computing remains a secure and trustworthy platform for users.","PeriodicalId":156403,"journal":{"name":"International Journal for Electronic Crime Investigation","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123103659","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 Nanoforensic: An Advanced Perspective in Crime Investigation","authors":"Syed Khurram Hassan, Hafiza Hadia Shehzad","doi":"10.54692/ijeci.2023.0701126","DOIUrl":"https://doi.org/10.54692/ijeci.2023.0701126","url":null,"abstract":"Nano forensics is the advanced application of nanotechnology-based techniques to resolve cases in forensic science. Forensic science offers scientific methods in a criminal investigation. Nano-forensics deals with the development of new approaches for fingerprint visualization, DNA isolation, forensic toxicology, explosive detection, identification of body fluids, gunshot residue analysis, detection of illicit drugs, etc. The nanomaterials used in forensic science are nanocrystals, nanoparticles, quantum dots, nanobelts, nanocomposites, nanoclusters, nanotubes, nanorods, etc. The scope of nanotechnology is very wide.","PeriodicalId":156403,"journal":{"name":"International Journal for Electronic Crime Investigation","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131382321","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 Skin Lesion Detection and Classification Using Deep Learning","authors":"Areej Fatima","doi":"10.54692/ijeci.2023.0701127","DOIUrl":"https://doi.org/10.54692/ijeci.2023.0701127","url":null,"abstract":"Exposure to ionizing radiation (IR) can cause basal cell carcinoma (BCC) development. A skin lesion is a region that is differentiable from another skin surface which can occur because of skin damage, allergy, etc. Even though the majority of skin lesions are mild and are not that dangerous yet few of them are infectious and their severity can turn into skin cancer. In the USA, 5.4 million people are analyzed with skin cancer. The diverse types of skin lesions result in an incorrect diagnosis because of their high similarity. Skin lesions can be treated by dermatologists. The current work proposes a model for the classification of skin lesions. The proposed methodology aims to detect and classify skin lesions using potential and different deep-learning algorithms. The research focuses to achieve state-of-the-art accuracy and compare the performance of algorithms.","PeriodicalId":156403,"journal":{"name":"International Journal for Electronic Crime Investigation","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117050949","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 Security Issues and challenges in Cloud Computing","authors":"Hussain Akbar, Muhammad Zubair, Muhammad Shairoze Malik","doi":"10.54692/ijeci.2023.0701125","DOIUrl":"https://doi.org/10.54692/ijeci.2023.0701125","url":null,"abstract":"A cloud computing model allows customers to use a pool of shared computer resources on-demand or pay-per-use basis. In terms of capital investment and operational cost reductions, cloud-based computing offers users and organizations many benefits. Despite these advantages, several challenges still limit the adoption of cloud computing. A crucial concern that is usually taken into account is security. Without this vital component, the computing model has a negative influence, which causes suffering on the human, ethical, and economic levels. This essay will look at the security issues that cloud entities must deal with. This group includes Cloud Service Provider, the Data Owner, and the Cloud User concentrating on the communication, computation, and service level agreements that make up the crypto-cloud. It will offer the required updates by evaluating the origins and consequences of different cyberattacks.","PeriodicalId":156403,"journal":{"name":"International Journal for Electronic Crime Investigation","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128574124","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 Detection and Control over the offences of White Collar Crimes, Frauds and Hacking of information, by using effectively the relevant Software and Electronic Devices","authors":"Dr Aftab Ahmed Malik, Dr. Mujtaba Asad, Dr. Waqar Azeem","doi":"10.54692/ijeci.2023.0701124","DOIUrl":"https://doi.org/10.54692/ijeci.2023.0701124","url":null,"abstract":"In both developed and emerging nations, fraud, white-collar crime, and malpractices in small and medium-sized businesses, banking, and other sectors are on the rise. The criminals are carrying out their fraud-related offenses by using the most up-to-date information technology structures and similar electronic media to create unjust loss and harm in the fields of white-collar crimes, banking, business, and to persons. The goal of this research paper is to make these businesses understand the need of utilizing the most up-to-date, trustworthy, and legitimate software and the necessity of making their networks more secure from external threats. The thieves are able to accomplish their goals thanks to the usage of sophisticated features for tracking and hacking private information utilizing software and information technology resources. In essence, their tactics rely on deceit and dishonesty to harm others. In essence, their methods of operation rely on deceit and dishonesty to commit the crime. Employees of the affected organizations are frequently complicit in banking and white-collar scams, aiding and abetting outside criminals by transferring sensitive information. Parody, distortion, misrepresentation, twisting of the fact or truth, and concealing of actual information that is harmful to another person are all examples of fraud. The legislation's attempt to punish fraudsters seems extremely naive. There are many reasons why criminals flee, including the fact that their identities are frequently kept a secret. The criminals are able to move toward exoneration and freedom due to a lack of evidence, shoddy investigation, and naive prosecuting tactics. The fraudsters possess high-quality Software Engineering Tools and Electronic Equipment to commit offenses of such to harm entrepreneurs, financial organizations, and commercial banks by depriving and extracting information and money from the concerned accounts, Technically and operationally feasible and valuable suggestions for implementation are presented in this paper to safeguard the Networks of organizations.","PeriodicalId":156403,"journal":{"name":"International Journal for Electronic Crime Investigation","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115780159","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}