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Applied Sciences
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New Trends in Network and Information Security
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New Trends in Network and Information Security

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 June 2023) | Viewed by 18115

Special Issue Editor

Dr. Mohammed Anbar

E-Mail Website
Guest Editor
National Advanced IPv6 Centre (NAv6), Universiti Sains Malaysia, USM, Gelugor, Penang 11800, Malaysia
Interests: malware detection; web security; intrusion detection system (IDS); intrusion prevention system (IPS); network monitoring; Internet of Things (IoT) and IPv6 security

Special Issue Information

Dear Colleagues,

The new trends in network and information security are a growing concern for many businesses. The rapid growth of technologies, such as mobile computing, cloud computing, and the Internet of Things (IoT), increases potential attack vectors for adversaries targeting networks and data. Furthermore, the COVID-19 pandemic further compounded the attack vectors and the associated security threats. The pandemic altered how and where we all work, making us easier targets to cybercriminals since users are more exposed to cyber attacks than ever because most duties are performed remotely over the internet, and sometimes over a public WiFi networks, away from the more secure corporate networks.

Many researchers employ various technologies, such as artificial intelligence (AI), in network and information security to deal with new threats. For example, using machine learning to detect vulnerabilities, find malicious content online, or predict cyber attacks. Some companies even incorporate AI into their cybersecurity solutions or security service offering.

Another new trend is the use of blockchain for securing data. Blockchain technology is usually associated with digital currency transactions. However, now it is also used to secure sensitive data such as medical or government records because it can store encrypted copies on multiple computers at once, making it challenging for hackers to break.

This Special Issue will publish high-quality, original research papers in the overlapping fields of:  

  • Machine learning and deep learning
  • Adversarial attack
  • Industry 4.0 security
  • Ambient, cloud and edge computing
  • Security protocols
  • Governance, policy and compliance
  • Blockchain and cryptography
  • Intrusion detection/prevention systems
  • Access control, authentication and authorization
  • Vehicular and mobile ad hoc networks
  • Internet of Things (IoT)
  • Social media, mobile and web
  • Wireless and cellular communication
  • Education, awareness and training
  • Botnet and malware
  • Digital forensics and surveillance
  • Data policy privacy and fake news

Dr. Mohammed Anbar
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. Applied Sciences 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

  • secure communications
  • access control and identity management
  • cryptography
  • blockchain
  • cyber threat intelligence
  • security operations
  • virtualization security
  • IoT
  • digital forensics
  • future networks
  • big data
  • malware analysis
  • IDS/IPS
  • DoS/DDoS detection
  • risk management
  • business continuity
  • application security
  • infrastructure security
  • edge and fog computing security
  • penetration testing
  • software testing
  • Industry 4.0
  • secure and privacy-preserving health solutions
  • cyber–physical systems security
  • zero-trust network access technology

Published Papers (4 papers)

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Research

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20 pages, 610 KiB  
Article
Registered Keyword Searchable Encryption Based on SM9
by Haoyu Zhang, Baodong Qin and Dong Zheng
Appl. Sci. 2023, 13(5), 3226; https://doi.org/10.3390/app13053226 - 02 Mar 2023
Cited by 1 | Viewed by 1303
Abstract
The SM9 algorithm is an Identity-Based Encryption (IBE) algorithm independently made by China. The existing SM9 searchable encryption scheme cannot be effective against insider keyword guessing attacks and violates users’ data privacy. This article utilizes the SM9 encryption method to propose a Registered [...] Read more.
The SM9 algorithm is an Identity-Based Encryption (IBE) algorithm independently made by China. The existing SM9 searchable encryption scheme cannot be effective against insider keyword guessing attacks and violates users’ data privacy. This article utilizes the SM9 encryption method to propose a Registered Public Keyword Searchable Encryption based on SM9 (RKSE-SM9), which uses the SM9 user keys in the registration keyword algorithm. For RKSE-SM9 to generate the keyword ciphertext or trapdoor, a secure server must first register the keyword, which effectively and reasonably protects users’ data and resists honest and curious cloud servers. From there, we also utilize Beaver’s triple to construct an improved registered keyword generation algorithm, defining and proving that the improved algorithm satisfies the concept of indistinguishability against registration keywords, achieving a higher level of privacy. In addition, compared with existing SM9 searchable encryption, our scheme proved to guarantee better security while reducing the computational efficiency by only 1%; compared with the existing registered keyword searchable encryption scheme, the overall operational efficiency increases by 63%. Full article
(This article belongs to the Special Issue New Trends in Network and Information Security)
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27 pages, 1946 KiB  
Article
Machine Learning Techniques to Detect a DDoS Attack in SDN: A Systematic Review
by Tariq Emad Ali, Yung-Wey Chong and Selvakumar Manickam
Appl. Sci. 2023, 13(5), 3183; https://doi.org/10.3390/app13053183 - 02 Mar 2023
Cited by 34 | Viewed by 10293
Abstract
The recent advancements in security approaches have significantly increased the ability to identify and mitigate any type of threat or attack in any network infrastructure, such as a software-defined network (SDN), and protect the internet security architecture against a variety of threats or [...] Read more.
The recent advancements in security approaches have significantly increased the ability to identify and mitigate any type of threat or attack in any network infrastructure, such as a software-defined network (SDN), and protect the internet security architecture against a variety of threats or attacks. Machine learning (ML) and deep learning (DL) are among the most popular techniques for preventing distributed denial-of-service (DDoS) attacks on any kind of network. The objective of this systematic review is to identify, evaluate, and discuss new efforts on ML/DL-based DDoS attack detection strategies in SDN networks. To reach our objective, we conducted a systematic review in which we looked for publications that used ML/DL approaches to identify DDoS attacks in SDN networks between 2018 and the beginning of November 2022. To search the contemporary literature, we have extensively utilized a number of digital libraries (including IEEE, ACM, Springer, and other digital libraries) and one academic search engine (Google Scholar). We have analyzed the relevant studies and categorized the results of the SLR into five areas: (i) The different types of DDoS attack detection in ML/DL approaches; (ii) the methodologies, strengths, and weaknesses of existing ML/DL approaches for DDoS attacks detection; (iii) benchmarked datasets and classes of attacks in datasets used in the existing literature; (iv) the preprocessing strategies, hyperparameter values, experimental setups, and performance metrics used in the existing literature; and (v) current research gaps and promising future directions. Full article
(This article belongs to the Special Issue New Trends in Network and Information Security)
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16 pages, 876 KiB  
Article
Comparison of ML/DL Approaches for Detecting DDoS Attacks in SDN
by Tariq Emad Ali, Yung-Wey Chong and Selvakumar Manickam
Appl. Sci. 2023, 13(5), 3033; https://doi.org/10.3390/app13053033 - 27 Feb 2023
Cited by 9 | Viewed by 2693
Abstract
Software-defined networking (SDN) presents novel security and privacy risks, including distributed denial-of-service (DDoS) attacks. In response to these threats, machine learning (ML) and deep learning (DL) have emerged as effective approaches for quickly identifying and mitigating anomalies. To this end, this research employs [...] Read more.
Software-defined networking (SDN) presents novel security and privacy risks, including distributed denial-of-service (DDoS) attacks. In response to these threats, machine learning (ML) and deep learning (DL) have emerged as effective approaches for quickly identifying and mitigating anomalies. To this end, this research employs various classification methods, including support vector machines (SVMs), K-nearest neighbors (KNNs), decision trees (DTs), multiple layer perceptron (MLP), and convolutional neural networks (CNNs), and compares their performance. CNN exhibits the highest train accuracy at 97.808%, yet the lowest prediction accuracy at 90.08%. In contrast, SVM demonstrates the highest prediction accuracy of 95.5%. As such, an SVM-based DDoS detection model shows superior performance. This comparative analysis offers a valuable insight into the development of efficient and accurate techniques for detecting DDoS attacks in SDN environments with less complexity and time. Full article
(This article belongs to the Special Issue New Trends in Network and Information Security)
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Review

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47 pages, 3831 KiB  
Review
A Systematic Literature Review and a Conceptual Framework Proposition for Advanced Persistent Threats (APT) Detection for Mobile Devices Using Artificial Intelligence Techniques
by Amjed Ahmed Al-Kadhimi, Manmeet Mahinderjit Singh and Mohd Nor Akmal Khalid
Appl. Sci. 2023, 13(14), 8056; https://doi.org/10.3390/app13148056 - 10 Jul 2023
Cited by 1 | Viewed by 3189
Abstract
Advanced persistent threat (APT) refers to a specific form of targeted attack used by a well-organized and skilled adversary to remain undetected while systematically and continuously exfiltrating sensitive data. Various APT attack vectors exist, including social engineering techniques such as spear phishing, watering [...] Read more.
Advanced persistent threat (APT) refers to a specific form of targeted attack used by a well-organized and skilled adversary to remain undetected while systematically and continuously exfiltrating sensitive data. Various APT attack vectors exist, including social engineering techniques such as spear phishing, watering holes, SQL injection, and application repackaging. Various sensors and services are essential for a smartphone to assist in user behavior that involves sensitive information. Resultantly, smartphones have become the main target of APT attacks. Due to the vulnerability of smartphone sensors, several challenges have emerged, including the inadequacy of current methods for detecting APTs. Nevertheless, several existing APT solutions, strategies, and implementations have failed to provide comprehensive solutions. Detecting APT attacks remains challenging due to the lack of attention given to human behavioral factors contributing to APTs, the ambiguity of APT attack trails, and the absence of a clear attack fingerprint. In addition, there is a lack of studies using game theory or fuzzy logic as an artificial intelligence (AI) strategy for detecting APT attacks on smartphone sensors, besides the limited understanding of the attack that may be employed due to the complex nature of APT attacks. Accordingly, this study aimed to deliver a systematic review to report on the extant research concerning APT detection for mobile sensors, applications, and user behavior. The study presents an overview of works performed between 2012 and 2023. In total, 1351 papers were reviewed during the primary search. Subsequently, these papers were processed according to their titles, abstracts, and contents. The resulting papers were selected to address the research questions. A conceptual framework is proposed to incorporate the situational awareness model in line with adopting game theory as an AI technique used to generate APT-based tactics, techniques, and procedures (TTPs) and normal TTPs and cognitive decision making. This framework enhances security awareness and facilitates the detection of APT attacks on smartphone sensors, applications, and user behavior. It supports researchers in exploring the most significant papers on APTs related to mobile sensors, services, applications, and detection techniques using AI. Full article
(This article belongs to the Special Issue New Trends in Network and Information Security)
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