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Future Internet
Special Issues
Artificial Intelligence and Machine Learning in Cybercrime Detection
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Artificial Intelligence and Machine Learning in Cybercrime Detection

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Cybersecurity".

Deadline for manuscript submissions: closed (31 August 2020) | Viewed by 20775

Special Issue Editor

Prof. Dr. Mamoun Alazab

E-Mail Website
Guest Editor
Faculty of Science and Technology, Charles Darwin University, Darwin 0812, Australia
Interests: cyber security; data analytics; digital forensics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cybercrime is a multidisciplinary area that encompasses law, computer science, psychology, psychophysiology, economics and finance, telecommunications, data analytics, and policing. Cybercrime is a timely issue, which presents numerous and constantly evolving challenges to academic, private sector, government, and law enforcement agencies. The decentralized nature of the Internet makes this a global issue that cannot be solved by a single company or country alone. This is especially the case given the high level of sophistication, commercialization, and organization of cybercrime attacks. Cybercrime has implications for national, supranational, and international legislation, cooperation between law enforcement organizations, cooperation between the public and private sectors, and for international coordination against transnational crime. This call for papers will produce a journal on cybercrime and cyber security. Researchers, academics, and practitioners are invited to submit original work, research papers, laboratory experiments, case studies, and experience reports.

This Special Issue aims to foster state-of-the-art research in the area of cybercrime and cyber security. Topics include but are not limited to:

  • The changing nature of cybercrime: threats and trends;
  • Cybercrime regulation, policy recommendations, and responses;
  • Technical measures to combat cybercrime: techniques, judicial processes, legal/ethical issues, and cybercrime legislations;
  • Electronic evidence and criminal justice;
  • Cybercrime detection and prevention;
  • Malware analysis, attribution, forensics, and reverse-engineering;
  • Spam emails, statistical analysis, and data mining;
  • Cybercrime victims and offenders: psychology and profiling;
  • Cybercrime investigations, concerning, e.g., computer and mobile forensics, online fraud, money laundering, hacking, malware, and botnets, sexual abuse of children on the Internet, software and media piracy, etc.;
  • Cloud security, privacy, and compliance challenges;
  • Misuse of personal data and the right to online privacy vs. anonymity.

Prof. Dr. Mamoun Alazab
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Cybercrime
  • Cyber security
  • Information security
  • Malware
  • Spam
  • Cybercrime prevention
  • Cybercrime detection
  • Cloud security
  • Computer forensic investigation
  • Cybercrime victims
  • Cybercrime offenders
  • Cybercrime regulation
  • Child pornography

Published Papers (3 papers)

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Research

20 pages, 2737 KiB  
Article
A Comparative Analysis of Machine Learning Techniques for Cyberbullying Detection on Twitter
by Amgad Muneer and Suliman Mohamed Fati
Future Internet 2020, 12(11), 187; https://0-doi-org.brum.beds.ac.uk/10.3390/fi12110187 - 29 Oct 2020
Cited by 102 | Viewed by 11272
Abstract
The advent of social media, particularly Twitter, raises many issues due to a misunderstanding regarding the concept of freedom of speech. One of these issues is cyberbullying, which is a critical global issue that affects both individual victims and societies. Many attempts have [...] Read more.
The advent of social media, particularly Twitter, raises many issues due to a misunderstanding regarding the concept of freedom of speech. One of these issues is cyberbullying, which is a critical global issue that affects both individual victims and societies. Many attempts have been introduced in the literature to intervene in, prevent, or mitigate cyberbullying; however, because these attempts rely on the victims’ interactions, they are not practical. Therefore, detection of cyberbullying without the involvement of the victims is necessary. In this study, we attempted to explore this issue by compiling a global dataset of 37,373 unique tweets from Twitter. Moreover, seven machine learning classifiers were used, namely, Logistic Regression (LR), Light Gradient Boosting Machine (LGBM), Stochastic Gradient Descent (SGD), Random Forest (RF), AdaBoost (ADB), Naive Bayes (NB), and Support Vector Machine (SVM). Each of these algorithms was evaluated using accuracy, precision, recall, and F1 score as the performance metrics to determine the classifiers’ recognition rates applied to the global dataset. The experimental results show the superiority of LR, which achieved a median accuracy of around 90.57%. Among the classifiers, logistic regression achieved the best F1 score (0.928), SGD achieved the best precision (0.968), and SVM achieved the best recall (1.00). Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Cybercrime Detection)
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19 pages, 1625 KiB  
Article
Ensemble Classifiers for Network Intrusion Detection Using a Novel Network Attack Dataset
by Ahmed Mahfouz, Abdullah Abuhussein, Deepak Venugopal and Sajjan Shiva
Future Internet 2020, 12(11), 180; https://0-doi-org.brum.beds.ac.uk/10.3390/fi12110180 - 26 Oct 2020
Cited by 51 | Viewed by 3627
Abstract
Due to the extensive use of computer networks, new risks have arisen, and improving the speed and accuracy of security mechanisms has become a critical need. Although new security tools have been developed, the fast growth of malicious activities continues to be a [...] Read more.
Due to the extensive use of computer networks, new risks have arisen, and improving the speed and accuracy of security mechanisms has become a critical need. Although new security tools have been developed, the fast growth of malicious activities continues to be a pressing issue that creates severe threats to network security. Classical security tools such as firewalls are used as a first-line defense against security problems. However, firewalls do not entirely or perfectly eliminate intrusions. Thus, network administrators rely heavily on intrusion detection systems (IDSs) to detect such network intrusion activities. Machine learning (ML) is a practical approach to intrusion detection that, based on data, learns how to differentiate between abnormal and regular traffic. This paper provides a comprehensive analysis of some existing ML classifiers for identifying intrusions in network traffic. It also produces a new reliable dataset called GTCS (Game Theory and Cyber Security) that matches real-world criteria and can be used to assess the performance of the ML classifiers in a detailed experimental evaluation. Finally, the paper proposes an ensemble and adaptive classifier model composed of multiple classifiers with different learning paradigms to address the issue of the accuracy and false alarm rate in IDSs. Our classifiers show high precision and recall rates and use a comprehensive set of features compared to previous work. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Cybercrime Detection)
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13 pages, 260 KiB  
Article
Coping Strategies and Anxiety and Depressive Symptoms in Young Adult Victims of Cyberstalking: A Questionnaire Survey in an Italian Sample
by Tatiana Begotti, Martina Bollo and Daniela Acquadro Maran
Future Internet 2020, 12(8), 136; https://0-doi-org.brum.beds.ac.uk/10.3390/fi12080136 - 12 Aug 2020
Cited by 8 | Viewed by 3860
Abstract
Aims: In the last decade, the use of smartphones, computers and devices has progressively increased, and prolonged use of technology and the internet has generated new arenas (and tools) for victimization. The first aim of this study was to analyze the use of [...] Read more.
Aims: In the last decade, the use of smartphones, computers and devices has progressively increased, and prolonged use of technology and the internet has generated new arenas (and tools) for victimization. The first aim of this study was to analyze the use of coping strategies in young adult self-declared victims of cyberstalking. The coping strategies were categorized as proactive behavior, avoidance tactics and passivity. To better understand these strategies, they were analyzed in light of the experience of victimization in terms of incurred misconduct. The second aim was to analyze the coping strategies and the consequences (in terms of depression and anxiety) that occurred in victims; a comparison was made between males and females. Methods: A self-administered questionnaire was distributed to over 433 young adults living in Italy. The questionnaires were filled out by 398 (92%) subjects, 41% males and 59% females. Their ages ranged from 18 to 30 years (M = 23.5, SD = 2.76). Respondents took part on a voluntary basis and did not receive any compensation (or extra credit) for their participation. Results: Findings from this investigation confirmed that among victims, females were more prone than males to experience cyberstalking (respectively, 65% and 35%), with females experiencing a higher percentage of more than one form of cyberstalking behavior than males. Young adult male victims used the internet principally for online gaming, and for this activity, they experienced more cyberstalking behavior than females. In most cases, the perpetrator was a male, and the victim–cyberstalker relationship was a friendship or an acquaintance. For the coping strategies adopted, the findings indicated that the victims were more prone to use avoidance tactics than proactivity behavior and passivity strategies. Young adults involved in this investigation mainly used avoidance tactics to cope with the stressful situation, which implies that they preferred to decrease the use of the internet or stop online contact than collect evidence and try to contact and reason with the cyberstalker or increase the misuse of alcohol of psychotropic substances. Moreover, females were less prone to use proactive behavior than expected. Our findings suggested that males were more prone than females to adopt passivity strategies, while females were more prone to adopt avoidance tactics. Moreover, the data showed that proactivity behavior was adopted more in the case of online contacts and online identity fraud, while passivity strategies were adopted in the case of online threats. Conclusion: Findings from this investigation show the importance of improving the knowledge about the coping strategies that could be suggested to victims and the impact on their psychological health. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Cybercrime Detection)
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