Browsing by Author "Ahmed, Abdulghani Ali"

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    Comprehensive Review of Cybercrime Detection Techniques
    (IEEE, 2020-07-22) Ahmed, Abdulghani Ali; Al-Khater, Wadha Abdullah; Al-Maadeed, Somaya; Sadiq, Ali Safaa; Khan, Muhammad Khurram
    Cybercrimes are cases of indictable offences and misdemeanors that involve computers or communication tools as targets and commission instruments or are associated with the prevalence of computer technology. Common forms of cybercrimes are child pornography, cyberstalking, identity theft, cyber laundering, credit card theft, cyber terrorism, drug sale, data leakage, sexually explicit content, phishing, and other forms of cyber hacking. They mostly lead to a privacy breach, security violation, business loss, financial fraud, or damage in public and government properties. Thus, this study intensively reviews cybercrime detection and prevention techniques. It first explores the different types of cybercrimes and discusses their threats against privacy and security in computer systems. Then, it describes the strategies that cybercriminals may utilize in committing these crimes against individuals, organizations, and societies. It also reviews the existing techniques of cybercrime detection and prevention. It objectively discusses the strengths and critically analyzes the vulnerabilities of each technique. Finally, it provides recommendations for the development of a cybercrime detection model that can detect cybercrimes effectively compared with the existing techniques.
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    Data-Driven Decision-Making for Bank Target Marketing Using Supervised Learning Classifiers on Imbalanced Big Data
    (Tech Science Press, 2024-10-15) Nasir, Fahim; Ahmed, Abdulghani Ali; Kiraz, Mehmet Sabir; Yevseyeva, Iryna; Saif, Mubarak
    Integrating machine learning and data mining is crucial for processing big data and extracting valuable insights to enhance decision-making. However, imbalanced target variables within big data present technical challenges that hinder the performance of supervised learning classifiers on key evaluation metrics, limiting their overall effectiveness. This study presents a comprehensive review of both common and recently developed Supervised Learning Classifiers (SLCs) and evaluates their performance in data-driven decision-making. The evaluation uses various metrics, with a particular focus on the Harmonic Mean Score (F-1 score) on an imbalanced real-world bank target marketing dataset. The findings indicate that grid-search random forest and random-search random forest excel in Precision and area under the curve, while Extreme Gradient Boosting (XGBoost) outperforms other traditional classifiers in terms of F-1 score. Employing oversampling methods to address the imbalanced data shows significant performance improvement in XGBoost, delivering superior results across all metrics, particularly when using the SMOTE variant known as the BorderlineSMOTE2 technique. The study concludes several key factors for effectively addressing the challenges of supervised learning with imbalanced datasets. These factors include the importance of selecting appropriate datasets for training and testing, choosing the right classifiers, employing effective techniques for processing and handling imbalanced datasets, and identifying suitable metrics for performance evaluation. Additionally, factors also entail the utilisation of effective exploratory data analysis in conjunction with visualisation techniques to yield insights conducive to data-driven decision-making.
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    Dynamic Reciprocal Authentication Protocol for Mobile Cloud Computing
    (IEEE, 2020-08-31) Ahmed, Abdulghani Ali; Wendy, Kwan; Kabir, Muhammad Nomani; Sadiq, Ali Safaa
    A combination of mobile and cloud computing delivers many advantages such as mobility, resources, and accessibility through seamless data transmission via the Internet anywhere at any time. However, data transmission through vulnerable channels poses security threats such as man-in-the-middle, playback, impersonation, and asynchronization attacks. To address these threats, we define an explicit security model that can precisely measure the practical capabilities of an adversary. A systematic methodology consisting of 16 evaluation criteria is used for comparative evaluation, thereby leading other approaches to be evaluated through a common scale. Finally, we propose a dynamic reciprocal authentication protocol to secure data transmission in mobile cloud computing (MCC). In particular, our proposed protocol develops a secure reciprocal authentication method, which is free of Diffie–Hellman limitations, and has immunity against basic or sophisticated known attacks. The protocol utilizes multifactor authentication of usernames, passwords, and a one-time password (OTP). The OTP is automatically generated and regularly updated for every connection. The proposed protocol is implemented and tested using Java to demonstrate its efficiency in authenticating communications and securing data transmitted in the MCC environment. Results of the evaluation process indicate that compared with the existing works, the proposed protocol possesses obvious capabilities in security and in communication and computation costs.
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    A Honeybee-Inspired Framework for a Smart City Free of Internet Scams
    (MDPI, 2023-04-26) Ahmed, Abdulghani Ali; Al-byatti, Ali; Saif, Mubarak; A. Jabbar, Waheb; Rassem, Taha H.
    Internet scams are fraudulent attempts aim to lure computer users to reveal their credentials or redirect their connections to spoofed webpages rather than the actual ones. Users’ confidential information, such as usernames, passwords, and financial account numbers, is the main target of these fraudulent attempts. Internet scammers often use phishing attacks, which have no boundaries, since they could exceed hijacking conventional cyber ecosystems to hack intelligent systems, which emerged recently for the use within smart cities. This paper therefore develops a real-time framework inspired by the honeybee defense mechanism in nature for filtering phishing website attacks in smart cities. In particular, the proposed framework filters phishing websites through three main phases of investigation: PhishTank-Match (PM), Undesirable-Absent (UA), and Desirable-Present (DP) investigation phases. The PM phase is used at first in order to check whether the requested URL is listed in the blacklist of the PhishTank database. On the other hand, the UA phase is used for investigation and checking for the absence of undesirable symbols in uniform resource locators (URLs) of the requested website. Finally, the DP phase is used as another level of investigation in order to check for the presence of the requested URL in the desirable whitelist. The obtained results show that the proposed framework is deployable and capable of filtering various types of phishing website by maintaining a low rate of false alarms.
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    IoT Forensics: Current Perspectives and Future Directions
    (MDPI, 2024-08-12) Ahmed, Abdulghani Ali; Farhan, Khalid; Jabbar, Waheb A.; Al-Othmani, Abdulaleem; Abdulrahman, Abdullahi Gara
    The Internet of Things forensics is a specialised field within digital forensics that focuses on the identification of security incidents, as well as the collection and analysis of evidence with the aim of preventing future attacks on IoT networks. IoT forensics differs from other digital forensic fields due to the unique characteristics of IoT devices, such as limited processing power and connectivity. Although numerous studies are available on IoT forensics, the field is rapidly evolving, and comprehensive surveys are needed to keep up with new developments, emerging threats, and evolving best practices. In this respect, this paper aims to review the state of the art in IoT forensics and discuss the challenges in current investigation techniques. A qualitative analysis of related reviews in the field of IoT forensics has been conducted, identifying key issues and assessing primary obstacles. Despite the variety of topics and approaches, common issues emerge. The majority of these issues are related to the collection and pre-processing of evidence because of the counter-analysis techniques and challenges associated with gathering data from devices and the cloud. Our analysis extends beyond technological problems; it further identifies the procedural problems with preparedness, reporting, and presentation as well as ethical issues. In particular, it provides insights into emerging threats and challenges in IoT forensics, increases awareness and understanding of the importance of IoT forensics in preventing cybercrimes, and ensures the security and privacy of IoT devices and networks. Our findings make a substantial contribution to the field of IoT forensics, as they not only involve a critical analysis of the challenges presented in existing works but also identify numerous problems. These insights will greatly assist researchers in identifying appropriate directions for their future research. d
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    Slicing-based enhanced method for privacy-preserving in publishing big data
    (Tech Science Press, 2022-03-29) BinJubier, Mohammed; Ismail, Mohd Arfian; Ahmed, Abdulghani Ali; Sadiq, Ali Safaa
    Publishing big data and making it accessible to researchers is important for knowledge building as it helps in applying highly efficient methods to plan, conduct, and assess scientific research. However, publishing and processing big data poses a privacy concern related to protecting individuals’ sensitive information while maintaining the usability of the published data. Several anonymization methods, such as slicing and merging, have been designed as solutions to the privacy concerns for publishing big data. However, the major drawback of merging and slicing is the random permutation procedure, which does not always guarantee complete protection against attribute or membership disclosure. Moreover, merging procedures may generate many fake tuples, leading to a loss of data utility and subsequent erroneous knowledge extraction. This study therefore proposes a slicing-based enhanced method for privacy-preserving big data publishing while maintaining the data utility. In particular, the proposed method distributes the data into horizontal and vertical partitions. The lower and upper protection levels are then used to identify the unique and identical attributes’ values. The unique and identical attributes are swapped to ensure the published big data is protected from disclosure risks. The outcome of the experiments demonstrates that the proposed method could maintain data utility and provide stronger privacy preservation.