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Algorithms
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Deep Learning Techniques for Computer Security Problems
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Deep Learning Techniques for Computer Security Problems

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: closed (15 October 2023) | Viewed by 11846

Special Issue Editor

Dr. Yue Duan

E-Mail Website
Guest Editor
School of Computing and Information Systems, Singapore Management University, Singapore 188065, Singapore
Interests: mobile security; IoT security; software testing; blockchain security; AI security

Special Issue Information

Dear Colleagues,

Deep learning techniques have been widely adopted in both academia and industry to facilitate analysis and tackle problems in different domains. Security researchers utilize deep learning models to help solve and understand a variety of important computer security problems which cannot be easily addressed with traditional approaches. For instance, by applying deep learning models we can easily achieve code similarity analysis for plagiarism detection much more quickly and accurately than when using traditional bipartite graph-matching approaches. Possible applications of deep learning techniques for computer security problems can be found not only in binary analysis, but also in many other areas, such as malware detection, fuzzing, attack investigation, and measurement studies.

Because of this emerging trend, with this Special Issue of Algorithms, we aim to provide a platform for the publication of novel approaches and unpublished work related to the application of deep learning techniques for computer security problems.

Possible topics of interest include, but are not limited to:

  • Deep learning for malware detection;
  • Deep learning for program testing (e.g, fuzzing and symbolic execution);
  • Deep learning for privacy preserving;
  • Deep learning for mobile security;
  • Deep learning for solving emerging security problems in IoT/AI/blockchain domains.

Dr. Yue Duan
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. Algorithms 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

  • deep learning
  • deep neural networks
  • software security
  • network security
  • privacy
  • security measurement
  • reinforcement learning
  • graph embeddings

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

Published Papers (4 papers)

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Research

22 pages, 4259 KiB  
Article
Predicting the Impact of Data Poisoning Attacks in Blockchain-Enabled Supply Chain Networks
by Usman Javed Butt, Osama Hussien, Krison Hasanaj, Khaled Shaalan, Bilal Hassan and Haider al-Khateeb
Algorithms 2023, 16(12), 549; https://doi.org/10.3390/a16120549 - 29 Nov 2023
Viewed by 2332
Abstract
As computer networks become increasingly important in various domains, the need for secure and reliable networks becomes more pressing, particularly in the context of blockchain-enabled supply chain networks. One way to ensure network security is by using intrusion detection systems (IDSs), which are [...] Read more.
As computer networks become increasingly important in various domains, the need for secure and reliable networks becomes more pressing, particularly in the context of blockchain-enabled supply chain networks. One way to ensure network security is by using intrusion detection systems (IDSs), which are specialised devices that detect anomalies and attacks in the network. However, these systems are vulnerable to data poisoning attacks, such as label and distance-based flipping, which can undermine their effectiveness within blockchain-enabled supply chain networks. In this research paper, we investigate the effect of these attacks on a network intrusion detection system using several machine learning models, including logistic regression, random forest, SVC, and XGB Classifier, and evaluate each model via their F1 Score, confusion matrix, and accuracy. We run each model three times: once without any attack, once with random label flipping with a randomness of 20%, and once with distance-based label flipping attacks with a distance threshold of 0.5. Additionally, this research tests an eight-layer neural network using accuracy metrics and a classification report library. The primary goal of this research is to provide insights into the effect of data poisoning attacks on machine learning models within the context of blockchain-enabled supply chain networks. By doing so, we aim to contribute to developing more robust intrusion detection systems tailored to the specific challenges of securing blockchain-based supply chain networks. Full article
(This article belongs to the Special Issue Deep Learning Techniques for Computer Security Problems)
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35 pages, 824 KiB  
Article
An Information Theoretic Approach to Privacy-Preserving Interpretable and Transferable Learning
by Mohit Kumar, Bernhard A. Moser, Lukas Fischer and Bernhard Freudenthaler
Algorithms 2023, 16(9), 450; https://doi.org/10.3390/a16090450 - 20 Sep 2023
Cited by 1 | Viewed by 1519
Abstract
In order to develop machine learning and deep learning models that take into account the guidelines and principles of trustworthy AI, a novel information theoretic approach is introduced in this article. A unified approach to privacy-preserving interpretable and transferable learning is considered for [...] Read more.
In order to develop machine learning and deep learning models that take into account the guidelines and principles of trustworthy AI, a novel information theoretic approach is introduced in this article. A unified approach to privacy-preserving interpretable and transferable learning is considered for studying and optimizing the trade-offs between the privacy, interpretability, and transferability aspects of trustworthy AI. A variational membership-mapping Bayesian model is used for the analytical approximation of the defined information theoretic measures for privacy leakage, interpretability, and transferability. The approach consists of approximating the information theoretic measures by maximizing a lower-bound using variational optimization. The approach is demonstrated through numerous experiments on benchmark datasets and a real-world biomedical application concerned with the detection of mental stress in individuals using heart rate variability analysis. Full article
(This article belongs to the Special Issue Deep Learning Techniques for Computer Security Problems)
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12 pages, 1932 KiB  
Article
Machine-Learning Techniques for Predicting Phishing Attacks in Blockchain Networks: A Comparative Study
by Kunj Joshi, Chintan Bhatt, Kaushal Shah, Dwireph Parmar, Juan M. Corchado, Alessandro Bruno and Pier Luigi Mazzeo
Algorithms 2023, 16(8), 366; https://doi.org/10.3390/a16080366 - 29 Jul 2023
Cited by 10 | Viewed by 4875
Abstract
Security in the blockchain has become a topic of concern because of the recent developments in the field. One of the most common cyberattacks is the so-called phishing attack, wherein the attacker tricks the miner into adding a malicious block to the chain [...] Read more.
Security in the blockchain has become a topic of concern because of the recent developments in the field. One of the most common cyberattacks is the so-called phishing attack, wherein the attacker tricks the miner into adding a malicious block to the chain under genuine conditions to avoid detection and potentially destroy the entire blockchain. The current attempts at detection include the consensus protocol; however, it fails when a genuine miner tries to add a new block to the blockchain. Zero-trust policies have started making the rounds in the field as they ensure the complete detection of phishing attempts; however, they are still in the process of deployment, which may take a significant amount of time. A more accurate measure of phishing detection involves machine-learning models that use specific features to automate the entire process of classifying an attempt as either a phishing attempt or a safe attempt. This paper highlights several models that may give safe results and help eradicate blockchain phishing attempts. Full article
(This article belongs to the Special Issue Deep Learning Techniques for Computer Security Problems)
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13 pages, 1431 KiB  
Article
Audio Anti-Spoofing Based on Audio Feature Fusion
by Jiachen Zhang, Guoqing Tu, Shubo Liu and Zhaohui Cai
Algorithms 2023, 16(7), 317; https://doi.org/10.3390/a16070317 - 28 Jun 2023
Cited by 3 | Viewed by 2157
Abstract
The rapid development of speech synthesis technology has significantly improved the naturalness and human-likeness of synthetic speech. As the technical barriers for speech synthesis are rapidly lowering, the number of illegal activities such as fraud and extortion is increasing, posing a significant threat [...] Read more.
The rapid development of speech synthesis technology has significantly improved the naturalness and human-likeness of synthetic speech. As the technical barriers for speech synthesis are rapidly lowering, the number of illegal activities such as fraud and extortion is increasing, posing a significant threat to authentication systems, such as automatic speaker verification. This paper proposes an end-to-end speech synthesis detection model based on audio feature fusion in response to the constantly evolving synthesis techniques and to improve the accuracy of detecting synthetic speech. The model uses a pre-trained wav2vec2 model to extract features from raw waveforms and utilizes an audio feature fusion module for back-end classification. The audio feature fusion module aims to improve the model accuracy by adequately utilizing the audio features extracted from the front end and fusing the information from timeframes and feature dimensions. Data augmentation techniques are also used to enhance the performance generalization of the model. The model is trained on the training and development sets of the logical access (LA) dataset of the ASVspoof 2019 Challenge, an international standard, and is tested on the logical access (LA) and deep-fake (DF) evaluation datasets of the ASVspoof 2021 Challenge. The equal error rate (EER) on ASVspoof 2021 LA and ASVspoof 2021 DF are 1.18% and 2.62%, respectively, achieving the best results on the DF dataset. Full article
(This article belongs to the Special Issue Deep Learning Techniques for Computer Security Problems)
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