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Electronics
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Hybrid Developments in Cyber Security and Threat Analysis
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Hybrid Developments in Cyber Security and Threat Analysis

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 23568

Special Issue Editor

Prof. Dr. Ankit Chaudhary

E-Mail Website
Guest Editor
Department of Computer Science, The University of Missouri, Columbia, MO 63121, USA
Interests: cyber security; data sciences
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

At present, network connectivity is a necessity for most people. The minimum requirements for data transfer are security and privacy. Additionally, intrusion, data poisoning, and port bombarding are common issues in network communication. Cyber attackers can build cyber armies that can pursue computationally challenging tasks by leveraging the untapped processing power of a very large number of everyday endpoints. Cyber-attacks are at an all-time high, even during the pandemic. The capability to detect, analyze, and defend against such threats in near-real-time conditions is not possible without the employment of threat intelligence, big data, and machine-learning techniques.

Prof. Dr. Ankit Chaudhary
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. Electronics is an international peer-reviewed open access semimonthly 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 2400 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

  • detection and analysis of advanced threat actors’ tactics, techniques, and procedures
  • analytic techniques for the detection and analysis of cyber threats
  • application of machine-learning tools and techniques in cyber threat intelligence
  • theories and models for the detection and analysis of advanced persistent threats
  • automated and smart tools for the collection, preservation, and analysis of digital evidence
  • threat intelligence techniques for constructing, detecting, and reacting to advanced intrusion campaigns
  • applying machine-learning tools and techniques for malware analysis and fighting against cyber-crimes
  • intelligent forensic tools, techniques, and procedures for cloud, mobile, and data-center forensics
  • intelligent analysis of different types of data collected from different layers of network security solutions
  • threat intelligence in the cyber security domain utilizing big data solutions such as hadoop
  • intelligent methods to manage, share, and receive logs and data relevant to a variety of adversary groups
  • interpretation of cyber threat and forensic data utilizing intelligent data analysis techniques
  • infer intelligence of existing cyber security data generated using different monitoring and defense solutions
  • automated and intelligent methods for adversary profiling
  • automated integration of analyzed data within incident response and cyber forensics capabilities

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Published Papers (4 papers)

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Research

18 pages, 1904 KiB  
Article
A Novel text2IMG Mechanism of Credit Card Fraud Detection: A Deep Learning Approach
by Abdullah Alharbi, Majid Alshammari, Ofonime Dominic Okon, Amerah Alabrah, Hafiz Tayyab Rauf, Hashem Alyami and Talha Meraj
Electronics 2022, 11(5), 756; https://doi.org/10.3390/electronics11050756 - 1 Mar 2022
Cited by 45 | Viewed by 9617
Abstract
Online sales and purchases are increasing daily, and they generally involve credit card transactions. This not only provides convenience to the end-user but also increases the frequency of online credit card fraud. In the recent years, in some countries, this fraud increase has [...] Read more.
Online sales and purchases are increasing daily, and they generally involve credit card transactions. This not only provides convenience to the end-user but also increases the frequency of online credit card fraud. In the recent years, in some countries, this fraud increase has led to an exponential increase in credit card fraud detection, which has become increasingly important to address this security issue. Recent studies have proposed machine learning (ML)-based solutions for detecting fraudulent credit card transactions, but their detection scores still need improvement due to the imbalance of classes in any given dataset. Few approaches have achieved exceptional results on different datasets. In this study, the Kaggle dataset was used to develop a deep learning (DL)-based approach to solve the text data problem. A novel text2IMG conversion technique is proposed that generates small images. The images are fed into a CNN architecture with class weights using the inverse frequency method to resolve the class imbalance issue. DL and ML approaches were applied to verify the robustness and validity of the proposed system. An accuracy of 99.87% was achieved by Coarse-KNN using deep features of the proposed CNN. Full article
(This article belongs to the Special Issue Hybrid Developments in Cyber Security and Threat Analysis)
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21 pages, 6236 KiB  
Article
TweezBot: An AI-Driven Online Media Bot Identification Algorithm for Twitter Social Networks
by Rachit Shukla, Adwitiya Sinha and Ankit Chaudhary
Electronics 2022, 11(5), 743; https://doi.org/10.3390/electronics11050743 - 28 Feb 2022
Cited by 8 | Viewed by 4296
Abstract
In the ultra-connected age of information, online social media platforms have become an indispensable part of our daily routines. Recently, this online public space is getting largely occupied by suspicious and manipulative social media bots. Such automated deceptive bots often attempt to distort [...] Read more.
In the ultra-connected age of information, online social media platforms have become an indispensable part of our daily routines. Recently, this online public space is getting largely occupied by suspicious and manipulative social media bots. Such automated deceptive bots often attempt to distort ground realities and manipulate global trends, thus creating astroturfing attacks on the social media online portals. Moreover, these bots often tend to participate in duplicitous activities, including promotion of hidden agendas and indulgence in biased propagation meant for personal gain or scams. Thus, online bots have eventually become one of the biggest menaces for social media platforms. Therefore, we have proposed an AI-driven social media bot identification framework, namely TweezBot, which can identify fraudulent Twitter bots. The proposed bot detection method analyzes Twitter-specific user profiles having essential profile-centric features and several activity-centric characteristics. We have constructed a set of filtering criteria and devised an exhaustive bag of words for performing language-based processing. In order to substantiate our research, we have performed a comparative study of our model with the existing benchmark classifiers, such as Support Vector Machine, Categorical Naïve Bayes, Bernoulli Naïve Bayes, Multilayer Perceptron, Decision Trees, Random Forest and other automation identifiers. Full article
(This article belongs to the Special Issue Hybrid Developments in Cyber Security and Threat Analysis)
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16 pages, 3849 KiB  
Article
Ensemble Averaging of Transfer Learning Models for Identification of Nutritional Deficiency in Rice Plant
by Mayuri Sharma, Keshab Nath, Rupam Kumar Sharma, Chandan Jyoti Kumar and Ankit Chaudhary
Electronics 2022, 11(1), 148; https://doi.org/10.3390/electronics11010148 - 4 Jan 2022
Cited by 48 | Viewed by 5422
Abstract
Computer vision-based automation has become popular in detecting and monitoring plants’ nutrient deficiencies in recent times. The predictive model developed by various researchers were so designed that it can be used in an embedded system, keeping in mind the availability of computational resources. [...] Read more.
Computer vision-based automation has become popular in detecting and monitoring plants’ nutrient deficiencies in recent times. The predictive model developed by various researchers were so designed that it can be used in an embedded system, keeping in mind the availability of computational resources. Nevertheless, the enormous popularity of smart phone technology has opened the door of opportunity to common farmers to have access to high computing resources. To facilitate smart phone users, this study proposes a framework of hosting high end systems in the cloud where processing can be done, and farmers can interact with the cloud-based system. With the availability of high computational power, many studies have been focused on applying convolutional Neural Networks-based Deep Learning (CNN-based DL) architectures, including Transfer learning (TL) models on agricultural research. Ensembling of various TL architectures has the potential to improve the performance of predictive models by a great extent. In this work, six TL architectures viz. InceptionV3, ResNet152V2, Xception, DenseNet201, InceptionResNetV2, and VGG19 are considered, and their various ensemble models are used to carry out the task of deficiency diagnosis in rice plants. Two publicly available datasets from Mendeley and Kaggle are used in this study. The ensemble-based architecture enhanced the highest classification accuracy to 100% from 99.17% in the Mendeley dataset, while for the Kaggle dataset; it was enhanced to 92% from 90%. Full article
(This article belongs to the Special Issue Hybrid Developments in Cyber Security and Threat Analysis)
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13 pages, 1490 KiB  
Article
Investigating the Experience of Social Engineering Victims: Exploratory and User Testing Study
by Bilikis Banire, Dena Al Thani and Yin Yang
Electronics 2021, 10(21), 2709; https://doi.org/10.3390/electronics10212709 - 6 Nov 2021
Cited by 4 | Viewed by 3132
Abstract
The advent of mobile technologies and social network applications has led to an increase in malicious scams and social engineering (SE) attacks which are causing loss of money and breaches of personal information. Understanding how SE attacks spread can provide useful information in [...] Read more.
The advent of mobile technologies and social network applications has led to an increase in malicious scams and social engineering (SE) attacks which are causing loss of money and breaches of personal information. Understanding how SE attacks spread can provide useful information in curbing them. Artificial Intelligence (AI) has demonstrated efficacy in detecting SE attacks, but the acceptability of such a detection approach is yet to be investigated across users with different levels of SE awareness. This paper conducted two studies: (1) exploratory study where qualitative data were collected from 20 victims of SE attacks to inform the development of an AI-based tool for detecting fraudulent messages; and (2) a user testing study with 48 participants with different occupations to determine the detection tool acceptability. Overall, six major themes emerged from the victims’ actions “experiences: reasons for falling for attacks; attack methods; advice on preventing attacks; detection methods; attack context and victims”. The user testing study showed that the AI-based tool was accepted by all users irrespective of their occupation. The categories of users’ occupations can be attributed to the level of SE awareness. Information security awareness should not be limited to organizational levels but extend to social media platforms as public information. Full article
(This article belongs to the Special Issue Hybrid Developments in Cyber Security and Threat Analysis)
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