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Computers
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Big Data Analytic for Cyber Crime Investigation and Prevention 2023
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Big Data Analytic for Cyber Crime Investigation and Prevention 2023

A special issue of Computers (ISSN 2073-431X). This special issue belongs to the section "ICT Infrastructures for Cybersecurity".

Deadline for manuscript submissions: closed (1 June 2023) | Viewed by 24520

Special Issue Editors

Dr. Andrii Shalaginov

E-Mail Website
Guest Editor
Department of Technology, School of Economics, Innovation and Technology, Kristiania University College, Oslo, Norway
Interests: internet of things; cyber security; smart applications; digital forensics; malware analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Asif Iqbal

E-Mail Website
Guest Editor
School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden
Interests: digital forensic and security investigations; cybercrime investigations; forensic intelligence information security modeling; cyber warfare; IoT; IIoT; SCADA/ICS
Special Issues, Collections and Topics in MDPI journals
Dr. Igor Kotsiuba

E-Mail Website
Guest Editor
Business School, Durham University, Durham DH1 3LB, UK
Interests: cybersecurity; digital forensics
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,

The 6th International Workshop on Big Data Analytic for Cyber Crime Investigation and Prevention will take place together with the IEEE Big Data 2022 conference in Osaka, Japan on 17–20 December 2022. More details can be found here:

https://smartseclab.com/bdaccip2022/.

The big data paradigm has become an inevitable aspect of today’s digital forensics investigations. Acquiring a forensic copy of seized data mediums already takes several hours due to the increasing storage size. In addition, several other time-consuming laboratory analysis steps are required, such as evidence identification, corresponding data preprocessing, analysis, linkage, and final reporting. These steps have to be repeated for every physical device examined in the criminal case. Conventional digital forensics data preprocessing and analysis methods struggle when handling the contemporary variety, variability, volume, and velocity of case data. Thus, proactive approaches have to be developed and integrated in daily law enforcement operations for timely detection and prevention of illegal activities in a data-intensive environments. Thus, there is a need for advanced big data analytics to aid in cybercrime investigations, which requires novel approaches for automated analysis. This workshop is organized to bring together recent development in big data analysis to aid in current challenges in cybercrime investigations.

The authors of selected papers that are presented at this workshop are invited to submit their extended versions to this Special Issue of the journal Computers after the conference. Submitted papers should be extended to the size of regular research or review articles, with at least a 50% extension of new results (e.g., in the form of technical extensions, more in-depth evaluations, or additional use cases, and not exceeding 30% copy/paste from conference paper). All submitted papers will undergo our standard peer-review procedure. Accepted papers will be published in open access format in Computers and collected together on this Special Issue’s website.

We are also inviting original research work covering algorithms, data, infrastructure, and application areas that can potentially lead to significant advances in Big Data Analytic for Cyber Crime Investigation and Prevention.

The topics of the workshop include but are not limited to the following:

Algorithms:

  • Machine Learning-aided analysis
  • Graph-based detection
  • Topic modeling
  • Improvements of existing methods
  • Decision support systems

Data:

  • Novel datasets
  • New data formats
  • Digital forensics data simulation
  • Anonymized case data
  • New data formats and taxonomies

Infrastructure

  • Secure collaborative platforms
  • Distributed storage and processing
  • Technologies for data streams
  • Hardware/software architectures for large-scale data

Application areas

  • Cyber threats intelligence
  • Network forensics readiness
  • Malware analysis and detection
  • Emails mining and authorship identification
  • Social network mining
  • Events correlations
  • Access logs analysis
  • Mobile forensics
  • Fraud detection
  • Database forensics
  • IoT forensics
  • Blockchain technologies
  • Industrial systems

Dr. Andrii Shalaginov
Dr. Asif Iqbal
Dr. Igor Kotsiuba
Dr. Mamoun Alazab
Guest Editors

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. Computers 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 1800 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.

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

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36 pages, 702 KiB  
Article
Determining Resampling Ratios Using BSMOTE and SVM-SMOTE for Identifying Rare Attacks in Imbalanced Cybersecurity Data
by Sikha S. Bagui, Dustin Mink, Subhash C. Bagui and Sakthivel Subramaniam
Computers 2023, 12(10), 204; https://doi.org/10.3390/computers12100204 - 11 Oct 2023
Cited by 10 | Viewed by 2573
Abstract
Machine Learning is widely used in cybersecurity for detecting network intrusions. Though network attacks are increasing steadily, the percentage of such attacks to actual network traffic is significantly less. And here lies the problem in training Machine Learning models to enable them to [...] Read more.
Machine Learning is widely used in cybersecurity for detecting network intrusions. Though network attacks are increasing steadily, the percentage of such attacks to actual network traffic is significantly less. And here lies the problem in training Machine Learning models to enable them to detect and classify malicious attacks from routine traffic. The ratio of actual attacks to benign data is significantly high and as such forms highly imbalanced datasets. In this work, we address this issue using data resampling techniques. Though there are several oversampling and undersampling techniques available, how these oversampling and undersampling techniques are most effectively used is addressed in this paper. Two oversampling techniques, Borderline SMOTE and SVM-SMOTE, are used for oversampling minority data and random undersampling is used for undersampling majority data. Both the oversampling techniques use KNN after selecting a random minority sample point, hence the impact of varying KNN values on the performance of the oversampling technique is also analyzed. Random Forest is used for classification of the rare attacks. This work is done on a widely used cybersecurity dataset, UNSW-NB15, and the results show that 10% oversampling gives better results for both BMSOTE and SVM-SMOTE. Full article
(This article belongs to the Special Issue Big Data Analytic for Cyber Crime Investigation and Prevention 2023)
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17 pages, 498 KiB  
Article
A Novel Deep Learning-Based Intrusion Detection System for IoT Networks
by Albara Awajan
Computers 2023, 12(2), 34; https://doi.org/10.3390/computers12020034 - 5 Feb 2023
Cited by 145 | Viewed by 16322
Abstract
The impressive growth rate of the Internet of Things (IoT) has drawn the attention of cybercriminals more than ever. The growing number of cyber-attacks on IoT devices and intermediate communication media backs the claim. Attacks on IoT, if they remain undetected for an [...] Read more.
The impressive growth rate of the Internet of Things (IoT) has drawn the attention of cybercriminals more than ever. The growing number of cyber-attacks on IoT devices and intermediate communication media backs the claim. Attacks on IoT, if they remain undetected for an extended period, cause severe service interruption resulting in financial loss. It also imposes the threat of identity protection. Detecting intrusion on IoT devices in real-time is essential to make IoT-enabled services reliable, secure, and profitable. This paper presents a novel Deep Learning (DL)-based intrusion detection system for IoT devices. This intelligent system uses a four-layer deep Fully Connected (FC) network architecture to detect malicious traffic that may initiate attacks on connected IoT devices. The proposed system has been developed as a communication protocol-independent system to reduce deployment complexities. The proposed system demonstrates reliable performance for simulated and real intrusions during the experimental performance analysis. It detects the Blackhole, Distributed Denial of Service, Opportunistic Service, Sinkhole, and Workhole attacks with an average accuracy of 93.74%. The proposed intrusion detection system’s precision, recall, and F1-score are 93.71%, 93.82%, and 93.47%, respectively, on average. This innovative deep learning-based IDS maintains a 93.21% average detection rate which is satisfactory for improving the security of IoT networks. Full article
(This article belongs to the Special Issue Big Data Analytic for Cyber Crime Investigation and Prevention 2023)
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Review

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25 pages, 433 KiB  
Review
On the Robustness of ML-Based Network Intrusion Detection Systems: An Adversarial and Distribution Shift Perspective
by Minxiao Wang, Ning Yang, Dulaj H. Gunasinghe and Ning Weng
Computers 2023, 12(10), 209; https://doi.org/10.3390/computers12100209 - 17 Oct 2023
Cited by 7 | Viewed by 4343
Abstract
Utilizing machine learning (ML)-based approaches for network intrusion detection systems (NIDSs) raises valid concerns due to the inherent susceptibility of current ML models to various threats. Of particular concern are two significant threats associated with ML: adversarial attacks and distribution shifts. Although there [...] Read more.
Utilizing machine learning (ML)-based approaches for network intrusion detection systems (NIDSs) raises valid concerns due to the inherent susceptibility of current ML models to various threats. Of particular concern are two significant threats associated with ML: adversarial attacks and distribution shifts. Although there has been a growing emphasis on researching the robustness of ML, current studies primarily concentrate on addressing specific challenges individually. These studies tend to target a particular aspect of robustness and propose innovative techniques to enhance that specific aspect. However, as a capability to respond to unexpected situations, the robustness of ML should be comprehensively built and maintained in every stage. In this paper, we aim to link the varying efforts throughout the whole ML workflow to guide the design of ML-based NIDSs with systematic robustness. Toward this goal, we conduct a methodical evaluation of the progress made thus far in enhancing the robustness of the targeted NIDS application task. Specifically, we delve into the robustness aspects of ML-based NIDSs against adversarial attacks and distribution shift scenarios. For each perspective, we organize the literature in robustness-related challenges and technical solutions based on the ML workflow. For instance, we introduce some advanced potential solutions that can improve robustness, such as data augmentation, contrastive learning, and robustness certification. According to our survey, we identify and discuss the ML robustness research gaps and future direction in the field of NIDS. Finally, we highlight that building and patching robustness throughout the life cycle of an ML-based NIDS is critical. Full article
(This article belongs to the Special Issue Big Data Analytic for Cyber Crime Investigation and Prevention 2023)
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