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Algorithms
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Deep Learning for Anomaly Detection
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Deep Learning for Anomaly Detection

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 May 2024) | Viewed by 4726

Special Issue Editors

Dr. Alessio Martino

E-Mail Website
Guest Editor
Department of Business and Management, LUISS University, Viale Romania 32, 00197 Rome, Italy
Interests: machine learning; computational intelligence; big data analysis; bioinformatics; computational biology
Special Issues, Collections and Topics in MDPI journals
Dr. Indro Spinelli

E-Mail Website
Guest Editor
ISPAMM Lab of Sapienza, University of Rome, 00185 Rome, RM, Italy
Interests: graph neural networks; trustworthy machine learning

Special Issue Information

Dear Colleagues,

Anomaly detection is an important task that tackles the problem of discovering data points or patterns in data that do not conform to normal behavior. Recognizing anomalies is critical for numerous high-impact applications in cyber-security, predictive maintenance, and rare disease diagnosis. Unfortunately, despite the recent developments in deep learning approaches, deep anomaly detection is significantly less explored than many other data mining tasks.

Transformer-based architectures are a brilliant example. They have topped the state-of-the-art charts in computer vision and natural language processing, but they are still under-explored for anomaly detection. This issue is due to the characteristics of anomalies (rarity, heterogeneity, unbounded nature, and absence of large data) that poorly fit the strengths of these algorithms in their standard configuration.

Furthermore, modern society also requires transparent decision processes. Therefore, the explanation of the anomaly has the same importance as its prediction. This fact is especially true for deep detection models applied in sensitive domains such as healthcare.

In this Special Issue, we welcome high-quality research papers addressing and reviewing theoretical and practical issues of deep learning systems focusing on anomaly detection tasks. We encourage solutions based on transformer architectures with explainable predictions or, in the case of graph-structured data, solutions that rely on graph neural networks.

Similarly, we welcome research papers on cutting-edge applications, including (but not limited to) cyber-security, predictive maintenance and defect detection, fraud detection, and rare disease/symptoms diagnosis.

Dr. Alessio Martino
Dr. Indro Spinelli
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. 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

  • data mining
  • machine learning
  • deep learning
  • anomaly detection
  • explainable AI

Published Papers (3 papers)

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Research

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21 pages, 5057 KiB  
Article
Particle Swarm Optimization-Based Model Abstraction and Explanation Generation for a Recurrent Neural Network
by Yang Liu, Huadong Wang and Yan Ma
Algorithms 2024, 17(5), 210; https://doi.org/10.3390/a17050210 - 13 May 2024
Viewed by 472
Abstract
In text classifier models, the complexity of recurrent neural networks (RNNs) is very high because of the vast state space and uncertainty of transitions, which makes the RNN classifier’s explainability insufficient. It is almost impossible to explain the large-scale RNN directly. A feasible [...] Read more.
In text classifier models, the complexity of recurrent neural networks (RNNs) is very high because of the vast state space and uncertainty of transitions, which makes the RNN classifier’s explainability insufficient. It is almost impossible to explain the large-scale RNN directly. A feasible method is to generalize the rules undermining it, that is, model abstraction. To deal with the low efficiency and excessive information loss in existing model abstraction for RNNs, this work proposes a PSO (Particle Swarm Optimization)-based model abstraction and explanation generation method for RNNs. Firstly, the k-means clustering is applied to preliminarily partition the RNN decision process state. Secondly, a frequency prefix tree is constructed based on the traces, and a PSO algorithm is designed to implement state merging to address the problem of vast state space. Then, a PFA (probabilistic finite automata) is constructed to explain the RNN structure with preserving the origin RNN information as much as possible. Finally, the quantitative keywords are labeled as an explanation for classification results, which are automatically generated with the abstract model PFA. We demonstrate the feasibility and effectiveness of the proposed method in some cases. Full article
(This article belongs to the Special Issue Deep Learning for Anomaly Detection)
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24 pages, 7769 KiB  
Article
Anomaly Detection for Skin Lesion Images Using Convolutional Neural Network and Injection of Handcrafted Features: A Method That Bypasses the Preprocessing of Dermoscopic Images
by Flavia Grignaffini, Maurizio Troiano, Francesco Barbuto, Patrizio Simeoni, Fabio Mangini, Gabriele D’Andrea, Lorenzo Piazzo, Carmen Cantisani, Noah Musolff, Costantino Ricciuti and Fabrizio Frezza
Algorithms 2023, 16(10), 466; https://doi.org/10.3390/a16100466 - 2 Oct 2023
Cited by 3 | Viewed by 2514
Abstract
Skin cancer (SC) is one of the most common cancers in the world and is a leading cause of death in humans. Melanoma (M) is the most aggressive form of skin cancer and has an increasing incidence rate. Early and accurate diagnosis of [...] Read more.
Skin cancer (SC) is one of the most common cancers in the world and is a leading cause of death in humans. Melanoma (M) is the most aggressive form of skin cancer and has an increasing incidence rate. Early and accurate diagnosis of M is critical to increase patient survival rates; however, its clinical evaluation is limited by the long timelines, variety of interpretations, and difficulty in distinguishing it from nevi (N) because of striking similarities. To overcome these problems and to support dermatologists, several machine-learning (ML) and deep-learning (DL) approaches have been developed. In the proposed work, melanoma detection, understood as an anomaly detection task with respect to the normal condition consisting of nevi, is performed with the help of a convolutional neural network (CNN) along with the handcrafted texture features of the dermoscopic images as additional input in the training phase. The aim is to evaluate whether the preprocessing and segmentation steps of dermoscopic images can be bypassed while maintaining high classification performance. Network training is performed on the ISIC2018 and ISIC2019 datasets, from which only melanomas and nevi are considered. The proposed network is compared with the most widely used pre-trained networks in the field of dermatology and shows better results in terms of classification and computational cost. It is also tested on the ISIC2016 dataset to provide a comparison with the literature: it achieves high performance in terms of accuracy, sensitivity, and specificity. Full article
(This article belongs to the Special Issue Deep Learning for Anomaly Detection)
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Review

Jump to: Research

41 pages, 1907 KiB  
Review
Anomaly Detection in Blockchain Networks Using Unsupervised Learning: A Survey
by Christos Cholevas, Eftychia Angeli, Zacharoula Sereti, Emmanouil Mavrikos and George E. Tsekouras
Algorithms 2024, 17(5), 201; https://doi.org/10.3390/a17050201 - 9 May 2024
Viewed by 570
Abstract
In decentralized systems, the quest for heightened security and integrity within blockchain networks becomes an issue. This survey investigates anomaly detection techniques in blockchain ecosystems through the lens of unsupervised learning, delving into the intricacies and going through the complex tapestry of abnormal [...] Read more.
In decentralized systems, the quest for heightened security and integrity within blockchain networks becomes an issue. This survey investigates anomaly detection techniques in blockchain ecosystems through the lens of unsupervised learning, delving into the intricacies and going through the complex tapestry of abnormal behaviors by examining avant-garde algorithms to discern deviations from normal patterns. By seamlessly blending technological acumen with a discerning gaze, this survey offers a perspective on the symbiotic relationship between unsupervised learning and anomaly detection by reviewing this problem with a categorization of algorithms that are applied to a variety of problems in this field. We propose that the use of unsupervised algorithms in blockchain anomaly detection should be viewed not only as an implementation procedure but also as an integration procedure, where the merits of these algorithms can effectively be combined in ways determined by the problem at hand. In that sense, the main contribution of this paper is a thorough study of the interplay between various unsupervised learning algorithms and how this can be used in facing malicious activities and behaviors within public and private blockchain networks. The result is the definition of three categories, the characteristics of which are recognized in terms of the way the respective integration takes place. When implementing unsupervised learning, the structure of the data plays a pivotal role. Therefore, this paper also provides an in-depth presentation of the data structures commonly used in unsupervised learning-based blockchain anomaly detection. The above analysis is encircled by a presentation of the typical anomalies that have occurred so far along with a description of the general machine learning frameworks developed to deal with them. Finally, the paper spotlights challenges and directions that can serve as a comprehensive compendium for future research efforts. Full article
(This article belongs to the Special Issue Deep Learning for Anomaly Detection)
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