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142 results on '"Jantan, Aman"'

2. Towards Understanding the Challenges of Data Remanence in Cloud Computing: A Review

Author

Gana, Usman Mohammed, Jantan, Aman, Yusoff, Mohd. Najwadi, Abdullahi, Ibrahim, Kiru, Ubale Muhammed, Kazaure, A. A., Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Anbar, Mohammed, editor, Abdullah, Nibras, editor, and Manickam, Selvakumar, editor

Published
2021
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21. Digital Forensic Investigation on Social Media Platforms: A Survey on Emerging Machine Learning Approaches.

Author

Kazaure, Abdullahi Aminu, Yusoff, Mohd Najwadi, and Jantan, Aman

Subjects
DIGITAL forensics, FORENSIC sciences, ONLINE social networks, MACHINE learning, SOCIAL media, INTELLECTUAL property theft
Abstract

An online social network is a platform that is continuously expanding, which enables groups of people to share their views and communicate with one another using the Internet. The social relations among members of the public are significantly improved because of this gesture. Despite these advantages and opportunities, criminals are continuing to broaden their attempts to exploit people by making use of techniques and approaches designed to undermine and exploit their victims for criminal activities. The field of digital forensics, on the other hand, has made significant progress in reducing the impact of this risk. Even though most of these digital forensic investigation techniques are carried out manually, most of these methods are not usually appropriate for use with online social networks due to their complexity, growth in data volumes, and technical issues that are present in these environments. In both civil and criminal cases, including sexual harassment, intellectual property theft, cyberstalking, online terrorism, and cyberbullying, forensic investigations on social media platforms have become more crucial. This study explores the use of machine learning techniques for addressing criminal incidents on social media platforms, particularly during forensic investigations. In addition, it outlines some of the difficulties encountered by forensic investigators while investigating crimes on social networking sites. [ABSTRACT FROM AUTHOR]

Published
2024
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29. Privacy Risk Diagnosis: Mining l-Diversity

Author

Zare-Mirakabad, Mohammad-Reza, Jantan, Aman, Bressan, Stéphane, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Chen, Lei, editor, Liu, Chengfei, editor, Liu, Qing, editor, and Deng, Ke, editor

Published
2009
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30. Clustering-Based Frequency l-Diversity Anonymization

Author

Zare-Mirakabad, Mohammad-Reza, Jantan, Aman, Bressan, Stéphane, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Park, Jong Hyuk, editor, Chen, Hsiao-Hwa, editor, Atiquzzaman, Mohammed, editor, Lee, Changhoon, editor, Kim, Tai-hoon, editor, and Yeo, Sang-Soo, editor

Published
2009
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31. Honeybee-Based Model to Detect Intrusion

Author

Ali, Ghassan Ahmed, Jantan, Aman, Ali, Abdulghani, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Park, Jong Hyuk, editor, Chen, Hsiao-Hwa, editor, Atiquzzaman, Mohammed, editor, Lee, Changhoon, editor, Kim, Tai-hoon, editor, and Yeo, Sang-Soo, editor

Published
2009
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35. Digital Forensics Investigation Approaches in Mitigating Cybercrimes: A Review.

Author

Kazaure, Abdullahi Aminu, Yusoff, Mohd Najwadi, and Jantan, Aman

Subjects
DIGITAL forensics, FORENSIC sciences, COMPUTER crimes, COMPUTER systems, COMPUTER networks, CYBERSPACE
Abstract

Cybercrime is a significant threat to Internet users, involving crimes committed using computers or computer networks. The landscape of cyberspace presents a complex terrain, making the task of tracing the origins of sensitive data a formidable and often elusive endeavor. However, tracing the source of sensitive data in online cyberspace is critically challenging, and detecting cyber-criminals on the other hand remains a time-consuming process, especially in social networks. Cyber-criminals target individuals for financial gain or to cause harm to their assets, resulting in the loss or theft of millions of user data over the past few decades. Forensic professionals play a vital role in conducting successful investigations and acquiring legally acceptable evidence admissible in court proceedings using modern techniques. This study aims to provide an overview of forensic investigation methods for extracting digital evidence from computer systems and mobile devices to combat persistent cybercrime. It also discusses current cybercrime issues and mitigation procedures. [ABSTRACT FROM AUTHOR]

Published
2023
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38. A Machine Learning Classification Approach to Detect TLS-based Malware using Entropy-based Flow Set Features

Author

Keshkeh, Kinan, Jantan, Aman, Alieyan, Kamal, Keshkeh, Kinan, Jantan, Aman, and Alieyan, Kamal

Abstract

Transport Layer Security (TLS) based malware is one of the most hazardous malware types, as it relies on encryption to conceal connections. Due to the complexity of TLS traffic decryption, several anomaly-based detection studies have been conducted to detect TLS-based malware using different features and machine learning (ML) algorithms. However, most of these studies utilized flow features with no feature transformation or relied on inefficient flow feature transformations like frequency-based periodicity analysis and outliers percentage. This paper introduces TLSMalDetect, a TLS-based malware detection approach that integrates periodicity-independent entropy-based flow set (EFS) features generated by a flow feature transformation technique to solve flow feature utilization issues in related research. EFS features effectiveness was evaluated in two ways: (1) by comparing them to the corresponding outliers percentage and flow features using four feature importance methods, and (2) by analyzing classification performance with and without EFS features. Moreover, new Transmission Control Protocol features not explored in literature were incorporated into TLSMalDetect, and their contribution was assessed. This study’s results proved EFS features of the number of packets sent and received were superior to related outliers percentage and flow features and could remarkably increase the performance up to ~42% in the case of Support Vector Machine accuracy. Furthermore, using the basic features, TLSMalDetect achieved the highest accuracy of 93.69% by Naïve Bayes (NB) among the ML algorithms applied. Also, from a comparison view, TLSMalDetect’s Random Forest precision of 98.99% and NB recall of 92.91% exceeded the best relevant findings of previous studies. These comparative results demonstrated the TLSMalDetect’s ability to detect more malware flows out of total malicious flows than existing works. It could also generate more actual alerts from overall alerts than earlier re

Published
2022

40. Cyber Intrusion Detection System Based on a Multiobjective Binary Bat Algorithm for Feature Selection and Enhanced Bat Algorithm for Parameter Optimization in Neural Networks

Author

Ghanem, Waheed Ali H. M., primary, Ghaleb, Sanaa Abduljabbar Ahmed, additional, Jantan, Aman, additional, Nasser, Abdullah B., additional, Saleh, Sami Abdulla Mohsen, additional, Ngah, Amir, additional, Alhadi, Arifah Che, additional, Arshad, Humaira, additional, Saad, Abdul-Malik H. Y., additional, Omolara, Abiodun Esther, additional, El-Ebiary, Yousef A. Baker, additional, and Abiodun, Oludare Isaac, additional

Published
2022
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