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Electronics
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Intelligent Security and Privacy Approaches against Cyber Threats
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Intelligent Security and Privacy Approaches against Cyber Threats

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

Deadline for manuscript submissions: closed (20 January 2022) | Viewed by 134411

Special Issue Editor

Dr. Nour Moustafa

E-Mail Website1 Website2
Guest Editor
School of Engineering and Information Technology, University of New South Wales at ADFA, Canberra, ACT 2612, Australia
Interests: intrusion detection; threat intelligence; privacy preservation; digital forensics; machine/deep learning; network systems; IoT; cloud
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

As many organizations have moved to work from home, cyber attackers have expanded their advanced persistent threats (APT), such as phishing, spear-phishing and zero-day attacks, to exploit vulnerabilities of home networks. It is urgent to develop well-designed privacy security approaches, algorithms, protocols, standards and policies for safeguarding home and organization networks against new cyber threats.

The reason for this challenge is that the policy of Bring your Own device (BYOD) allows individuals to use various Internet of Things (IoT) devices, operating systems and tools, which are different in the settings of security and privacy. The technical and humanized practices of ‘Security-Based Organization’ and ‘Security-Based Home’ will enrich individuals' knowledge for protecting their home networks and securing their organizations’ assets. ‘Security-Based Organization’ denotes that organizations often provide security services and tools and training to employees with less effort from the employees and high visibility of security services, while ‘Security-Based Home’ denotes that individuals need new cyber practices which adapt security to home networks. The transition to work from home needs new security and privacy models that would employ Artificial Intelligence (AI), blockchain, human factor models, cognitive models. and secure big data analytics to secure home networks and safeguard organization assets.

Topics of interest include but are not limited to:

- Intelligent security practices and model-based AI against COVID-19 threats;

- Privacy-enabled human factor models against COVID-19 cyberattacks;

- AI-based Intrusion Detection Systems for discovering COVID-19 cyberattacks;

- AI-based cognitive models against COVID-19 cyberattacks;

- Privacy-driven human analytical behaviours in home networks;

- Privacy-preserving algorithms and approaches for protecting data of home networks;

- Secure Big Data analytics to analyze heterogeneous IoT and home elements;

- Secure and distributed semantic techniques for modeling home networks;

- Blockchain technologies for trusting home and organization systems and networks;

- Threat intelligence for pivoting COVID-19 cyber-attacks.

Dr. Nour Moustafa
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

  • security
  • privacy
  • artificial intelligence
  • intrusion detection
  • human factors
  • privacy preservation

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Published Papers (7 papers)

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Research

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16 pages, 3767 KiB  
Article
Ensemble of Deep Convolutional Learning Classifier System Based on Genetic Algorithm for Database Intrusion Detection
by Seok-Jun Bu, Han-Bit Kang and Sung-Bae Cho
Electronics 2022, 11(5), 745; https://doi.org/10.3390/electronics11050745 - 28 Feb 2022
Cited by 12 | Viewed by 2938
Abstract
Methods of applying deep learning to database protection have increased over the years. To secure role-based access control (RBAC) by learning the mapping function between query features and roles, it is known that the convolutional neural networks combined with learning classifier systems (LCS) [...] Read more.
Methods of applying deep learning to database protection have increased over the years. To secure role-based access control (RBAC) by learning the mapping function between query features and roles, it is known that the convolutional neural networks combined with learning classifier systems (LCS) can reach formidable accuracy. However, current methods are focused on using a singular model architecture and fail to fully exploit features that other models are capable of utilizing. Different deep architectures, such as ResNet and Inception, can exploit different spatial correlations within the feature space. In this paper, we propose an ensemble of multiple models with different deep convolutional architectures to improve the overall coverage of features used in role classification. By combining models with heterogeneous topologies, the ensemble-LCS model shows significantly increased performance compared to previous single architecture LCS models and achieves better robustness in the case of training data imbalance. Full article
(This article belongs to the Special Issue Intelligent Security and Privacy Approaches against Cyber Threats)
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13 pages, 495 KiB  
Article
Performance Evaluation of Deep Learning Based Network Intrusion Detection System across Multiple Balanced and Imbalanced Datasets
by Azizjon Meliboev, Jumabek Alikhanov and Wooseong Kim
Electronics 2022, 11(4), 515; https://doi.org/10.3390/electronics11040515 - 9 Feb 2022
Cited by 43 | Viewed by 5602
Abstract
In the modern era of active network throughput and communication, the study of Intrusion Detection Systems (IDS) is a crucial role to ensure safe network resources and information from outside invasion. Recently, IDS has become a needful tool for improving flexibility and efficiency [...] Read more.
In the modern era of active network throughput and communication, the study of Intrusion Detection Systems (IDS) is a crucial role to ensure safe network resources and information from outside invasion. Recently, IDS has become a needful tool for improving flexibility and efficiency for unexpected and unpredictable invasions of the network. Deep learning (DL) is an essential and well-known tool to solve complex system problems and can learn rich features of enormous data. In this work, we aimed at a DL method for applying the effective and adaptive IDS by applying the architectures such as Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM), Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU). CNN models have already proved an incredible performance in computer vision tasks. Moreover, the CNN can be applied to time-sequence data. We implement the DL models such as CNN, LSTM, RNN, GRU by using sequential data in a prearranged time range as a malicious traffic record for developing the IDS. The benign and attack records of network activities are classified, and a label is given for the supervised-learning method. We applied our approaches to three different benchmark data sets which are UNSW NB15, KDDCup ’99, NSL-KDD to show the efficiency of DL approaches. For contrast in performance, we applied CNN and LSTM combination models with varied parameters and architectures. In each implementation, we trained the models until 100 epochs accompanied by a learning rate of 0.0001 for both balanced and imbalanced train data scenarios. The single CNN and combination of LSTM models have overcome compared to others. This is essentially because the CNN model can learn high-level features that characterize the abstract patterns from network traffic records data. Full article
(This article belongs to the Special Issue Intelligent Security and Privacy Approaches against Cyber Threats)
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20 pages, 1212 KiB  
Article
Detecting Nuisance Calls over Internet Telephony Using Caller Reputation
by Ibrahim Tariq Javed, Khalifa Toumi, Fares Alharbi, Tiziana Margaria and Noel Crespi
Electronics 2021, 10(3), 353; https://doi.org/10.3390/electronics10030353 - 2 Feb 2021
Cited by 7 | Viewed by 3944
Abstract
Internet telephony permit callers to manage self-asserted profiles without any subscription contract nor identification proof. These cost-free services have attracted many telemarketers and spammers who generate unsolicited nuisance calls. Upon detection, they simply rejoin the network with a new identity to continue their [...] Read more.
Internet telephony permit callers to manage self-asserted profiles without any subscription contract nor identification proof. These cost-free services have attracted many telemarketers and spammers who generate unsolicited nuisance calls. Upon detection, they simply rejoin the network with a new identity to continue their malicious activities. Nuisance calls are highly disruptive when compared to email and social spam. They not only include annoying telemarketing calls but also contain scam and voice phishing which involves security risk for subscribers. Therefore, it remains a major challenge for Internet telephony providers to detect and avoid nuisance calls efficiently. In this paper, we present a new approach that uses caller reputation to detect different kinds of nuisance calls generated in the network. The reputation is computed in a hybrid manner by extracting information from call data records and using recommendations from reliable communicating participants. The behavior of the caller is assessed by extracting call features such as call-rate, call duration, and call density. Long term and short term reputations are computed to quickly detect the changing behavior of callers. Furthermore, our approach involves an efficient mechanism to combat whitewashing attacks performed by malicious callers to continue generating nuisance calls in the network. We conduct simulations to compute the performance of our proposed model. The experiments conclude that the proposed reputation model is an effective method to detect different types of nuisance calls while avoiding false detection of legitimate calls. Full article
(This article belongs to the Special Issue Intelligent Security and Privacy Approaches against Cyber Threats)
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31 pages, 2404 KiB  
Article
Supply Chain 4.0: A Survey of Cyber Security Challenges, Solutions and Future Directions
by Theresa Sobb, Benjamin Turnbull and Nour Moustafa
Electronics 2020, 9(11), 1864; https://doi.org/10.3390/electronics9111864 - 6 Nov 2020
Cited by 111 | Viewed by 23444
Abstract
Supply chain 4.0 denotes the fourth revolution of supply chain management systems, integrating manufacturing operations with telecommunication and Information Technology processes. Although the overarching aim of supply chain 4.0 is the enhancement of production systems within supply chains, making use of global reach, [...] Read more.
Supply chain 4.0 denotes the fourth revolution of supply chain management systems, integrating manufacturing operations with telecommunication and Information Technology processes. Although the overarching aim of supply chain 4.0 is the enhancement of production systems within supply chains, making use of global reach, increasing agility and emerging technology, with the ultimate goal of increasing efficiency, timeliness and profitability, Supply chain 4.0 suffers from unique and emerging operational and cyber risks. Supply chain 4.0 has a lack of semantic standards, poor interoperability, and a dearth of security in the operation of its manufacturing and Information Technology processes. The technologies that underpin supply chain 4.0 include blockchain, smart contracts, applications of Artificial Intelligence, cyber-physical systems, Internet of Things and Industrial Internet of Things. Each of these technologies, individually and combined, create cyber security issues that should be addressed. This paper explains the nature of the military supply chains 4.0 and how it uniquely differs from the commercial supply chain, revealing their strengths, weaknesses, dependencies and the fundamental technologies upon which they are built. This encompasses an assessment of the cyber risks and opportunities for research in the field, including consideration of connectivity, sensing and convergence of systems. Current and emerging semantic models related to the standardization, development and safety assurance considerations for implementing new technologies into military supply chains 4.0 are also discussed. This is examined from a holistic standpoint and through technology-specific lenses to determine current states and implications for future research directions. Full article
(This article belongs to the Special Issue Intelligent Security and Privacy Approaches against Cyber Threats)
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21 pages, 1723 KiB  
Article
Survey of Countering DoS/DDoS Attacks on SIP Based VoIP Networks
by Waleed Nazih, Wail S. Elkilani, Habib Dhahri and Tamer Abdelkader
Electronics 2020, 9(11), 1827; https://doi.org/10.3390/electronics9111827 - 2 Nov 2020
Cited by 15 | Viewed by 5002
Abstract
Voice over IP (VoIP) services hold promise because of their offered features and low cost. Most VoIP networks depend on the Session Initiation Protocol (SIP) to handle signaling functions. The SIP is a text-based protocol that is vulnerable to many attacks. Denial of [...] Read more.
Voice over IP (VoIP) services hold promise because of their offered features and low cost. Most VoIP networks depend on the Session Initiation Protocol (SIP) to handle signaling functions. The SIP is a text-based protocol that is vulnerable to many attacks. Denial of Service (DoS) and distributed denial of service (DDoS) attacks are the most harmful types of attacks, because they drain VoIP resources and render SIP service unavailable to legitimate users. In this paper, we present recently introduced approaches to detect DoS and DDoS attacks, and classify them based on various factors. We then analyze these approaches according to various characteristics; furthermore, we investigate the main strengths and weaknesses of these approaches. Finally, we provide some remarks for enhancing the surveyed approaches and highlight directions for future research to build effective detection solutions. Full article
(This article belongs to the Special Issue Intelligent Security and Privacy Approaches against Cyber Threats)
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Review

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12 pages, 849 KiB  
Review
The k-means Algorithm: A Comprehensive Survey and Performance Evaluation
by Mohiuddin Ahmed, Raihan Seraj and Syed Mohammed Shamsul Islam
Electronics 2020, 9(8), 1295; https://doi.org/10.3390/electronics9081295 - 12 Aug 2020
Cited by 883 | Viewed by 62937
Abstract
The k-means clustering algorithm is considered one of the most powerful and popular data mining algorithms in the research community. However, despite its popularity, the algorithm has certain limitations, including problems associated with random initialization of the centroids which leads to unexpected convergence. [...] Read more.
The k-means clustering algorithm is considered one of the most powerful and popular data mining algorithms in the research community. However, despite its popularity, the algorithm has certain limitations, including problems associated with random initialization of the centroids which leads to unexpected convergence. Additionally, such a clustering algorithm requires the number of clusters to be defined beforehand, which is responsible for different cluster shapes and outlier effects. A fundamental problem of the k-means algorithm is its inability to handle various data types. This paper provides a structured and synoptic overview of research conducted on the k-means algorithm to overcome such shortcomings. Variants of the k-means algorithms including their recent developments are discussed, where their effectiveness is investigated based on the experimental analysis of a variety of datasets. The detailed experimental analysis along with a thorough comparison among different k-means clustering algorithms differentiates our work compared to other existing survey papers. Furthermore, it outlines a clear and thorough understanding of the k-means algorithm along with its different research directions. Full article
(This article belongs to the Special Issue Intelligent Security and Privacy Approaches against Cyber Threats)
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45 pages, 3901 KiB  
Review
A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions
by Javed Asharf, Nour Moustafa, Hasnat Khurshid, Essam Debie, Waqas Haider and Abdul Wahab
Electronics 2020, 9(7), 1177; https://doi.org/10.3390/electronics9071177 - 20 Jul 2020
Cited by 229 | Viewed by 28781
Abstract
The Internet of Things (IoT) is poised to impact several aspects of our lives with its fast proliferation in many areas such as wearable devices, smart sensors and home appliances. IoT devices are characterized by their connectivity, pervasiveness and limited processing capability. The [...] Read more.
The Internet of Things (IoT) is poised to impact several aspects of our lives with its fast proliferation in many areas such as wearable devices, smart sensors and home appliances. IoT devices are characterized by their connectivity, pervasiveness and limited processing capability. The number of IoT devices in the world is increasing rapidly and it is expected that there will be 50 billion devices connected to the Internet by the end of the year 2020. This explosion of IoT devices, which can be easily increased compared to desktop computers, has led to a spike in IoT-based cyber-attack incidents. To alleviate this challenge, there is a requirement to develop new techniques for detecting attacks initiated from compromised IoT devices. Machine and deep learning techniques are in this context the most appropriate detective control approach against attacks generated from IoT devices. This study aims to present a comprehensive review of IoT systems-related technologies, protocols, architecture and threats emerging from compromised IoT devices along with providing an overview of intrusion detection models. This work also covers the analysis of various machine learning and deep learning-based techniques suitable to detect IoT systems related to cyber-attacks. Full article
(This article belongs to the Special Issue Intelligent Security and Privacy Approaches against Cyber Threats)
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