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Recent Advances on Deep Learning for Safety and Security of Multimedia Data in the Critical Infrastructure

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A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 30335

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Dr. Brij Gupta

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Department of Computer Science and Information Engineering, Asia University, Taichung 41354, Taiwan
Interests: artificial intelligence; cloud security, cyber security; information security; internet-of-things; intrusion detection; mobile and web security; network performance evaluation
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Prof. Dr. Dharma P. Agrawal †

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Ohio Board of Regents Distinguished Professor, Department of Electrical Engineering and Computing Systems, 819D Old Chemistry, University of Cincinnati, Cincinnati, OH 45221-0030, USA
Interests: bio-medical applications of wireless sensor networks; secured communication in WSN, IoT framework; vehicular systems
Special Issues, Collections and Topics in MDPI journals

Dr. Sangbing Tsai

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University ofElectronic Science and Technology of China Zhongshan Institute, China &Wuyi University

Dr. Deepak Gupta

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Founder and CEO, LoginRadius Inc., Vancouver, Canada

Special Issue Information

Dear Colleagues,

There are some systems and networks that make up the infrastructure of society. Some of these infrastructures are of utmost importance and are related to each other. If one of these is critically damaged, then they can cause huge disturbances and losses for a nation. These are known as critical infrastructures. Particularly, the security and privacy of critical infrastructures (a nation’s strategic national assets, i.e., banking and finance, communications, emergency services, energy, food chain, health, water, mass gatherings, transport, etc.), which is an essential part of our daily life, in accessing different systems, services, and applications are serious issues. However, it is challenging to achieve, as technology is changing at rapid speed and our systems are ever more complex. The explosion of multimedia data has created unprecedented opportunities and fundamental security challenges as they are not just large in volume, but also unstructured and multi-modal. Deep learning can be used to provide a robust defense mechanism for critical infrastructures. There is always the possibility of cyber-attacks against these infrastructures, which can be predicted and detected with the help of deep learning. Deep learning can be used to identify the direct and indirect connections between these infrastructures so that in case of attack, appropriate security measures can be enforced. Deep learning can also be used to identify the weaknesses present in the current security mechanisms so that the vulnerabilities can be patched before they can be exploited. Deep learning can also be used for device layers of security mechanisms that can efficiently withstand such attacks. These defense mechanisms could be of autonomous nature and thus will require almost no human intervention.

This Special Issue mainly focuses on deep learning for the safety and security of multimedia data in critical infrastructure, addressing both original algorithmic development and new applications. We are soliciting original contributions, of leading researchers and practitioners from academia as well as industry, which address a wide range of theoretical and application issues in this domain. Please note that all the submitted papers must be within the general scope of the Symmetry journal.

The topics relevant to this Special Issue include but are not limited to the following:

Security and privacy of multimedia data in telecommunication systems
Security and privacy of multimedia data in communication systems
Security and privacy of multimedia data in eCommerce
Security and privacy of multimedia data in emergency services, energy, food chain
Security, privacy and forensics of multimedia data in critical infrastructure
Security and privacy of multimedia data in mobile cloud computing
Security and privacy management of data in cloud computing
Security and privacy of Industrial control systems
Mobile cloud computing intrusion detection systems
Cryptography, authentication, authorization, and usage control for data in cloud
Security and privacy of multimedia data in smartphone devices
Security of mobile, peer-to-peer and pervasive services in clouds
Security of data in mobile commerce and mobile internet of things
Security and privacy of multimedia data in sensor networks
Big data-enabling social networks on clouds
Resource management for multimedia data on clouds
Cryptography, authentication, and authorization for data in mobile devices
Security and privacy of multimedia data in web service
Evolutionary algorithms for mining social networks for decision support

Artificial neural network and neural system applied to social media and mitigating the privacy risks in critical infrastructure

Dr. Brij Gupta
Prof. Dr. Dharma P. Agrawal
Dr. Deepak Gupta
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. Symmetry 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 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.

Published Papers (8 papers)

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Research

15 pages, 11385 KiB

Open AccessArticle

An Approach on Image Processing of Deep Learning Based on Improved SSD

by Liang Jin and Guodong Liu

Symmetry 2021, 13(3), 495; https://doi.org/10.3390/sym13030495 - 17 Mar 2021

Cited by 28 | Viewed by 2628

Abstract

Compared with ordinary images, each of the remote sensing images contains many kinds of objects with large scale changes, providing more details. As a typical object of remote sensing image, ship detection has been playing an essential role in the field of remote sensing. With the rapid development of deep learning, remote sensing image detection method based on convolutional neural network (CNN) has occupied a key position. In remote sensing images, the objects of which small scale objects account for a large proportion are closely arranged. In addition, the convolution layer in CNN lacks ample context information, leading to low detection accuracy for remote sensing image detection. To improve detection accuracy and keep the speed of real-time detection, this paper proposed an efficient object detection algorithm for ship detection of remote sensing image based on improved SSD. Firstly, we add a feature fusion module to shallow feature layers to refine feature extraction ability of small object. Then, we add Squeeze-and-Excitation Network (SE) module to each feature layers, introducing attention mechanism to network. The experimental results based on Synthetic Aperture Radar ship detection dataset (SSDD) show that the mAP reaches 94.41%, and the average detection speed is 31FPS. Compared with SSD and other representative object detection algorithms, this improved algorithm has a better performance in detection accuracy and can realize real-time detection. Full article

(This article belongs to the Special Issue Recent Advances on Deep Learning for Safety and Security of Multimedia Data in the Critical Infrastructure)

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19 pages, 2718 KiB

Open AccessArticle

Ship Detection and Tracking in Inland Waterways Using Improved YOLOv3 and Deep SORT

by Yang Jie, LilianAsimwe Leonidas, Farhan Mumtaz and Munsif Ali

Symmetry 2021, 13(2), 308; https://doi.org/10.3390/sym13020308 - 12 Feb 2021

Cited by 28 | Viewed by 4835

Abstract

Ship detection and tracking is an important task in video surveillance in inland waterways. However, ships in inland navigation are faced with accidents such as collisions. For collision avoidance, we should strengthen the monitoring of navigation and the robustness of the entire system. Hence, this paper presents ship detection and tracking of ships using the improved You Only Look Once version 3 (YOLOv3) detection algorithm and Deep Simple Online and Real-time Tracking (Deep SORT) tracking algorithm. Three improvements are made to the YOLOv3 target detection algorithm. Firstly, the Kmeans clustering algorithm is used to optimize the initial value of the anchor frame to make it more suitable for ship application scenarios. Secondly, the output classifier is modified to a single Softmax classifier to suit our ship dataset which has three ship categories and mutual exclusion. Finally, Soft Non-Maximum Suppression (Soft-NMS) is introduced to solve the deficiencies of the Non-Maximum Suppression (NMS) algorithm when screening candidate frames. Results showed the mean Average Precision (mAP) and Frame Per Second (FPS) of the improved algorithm are increased by about 5% and 2, respectively, compared with the existing YOLOv3 detecting Algorithm. Then the improved YOLOv3 is applied in Deep Sort and the performance result of Deep Sort showed that, it has greater performance in complex scenes, and is robust to interference such as occlusion and camera movement, compared to state of art algorithms such as KCF, MIL, MOSSE, TLD, and Median Flow. With this improvement, it will help in the safety of inland navigation and protection from collisions and accidents. Full article

(This article belongs to the Special Issue Recent Advances on Deep Learning for Safety and Security of Multimedia Data in the Critical Infrastructure)

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11 pages, 3083 KiB

Open AccessArticle

Automatic Malicious Code Classification System through Static Analysis Using Machine Learning

by Sungjoong Kim, Seongkyu Yeom, Haengrok Oh, Dongil Shin and Dongkyoo Shin

Symmetry 2021, 13(1), 35; https://doi.org/10.3390/sym13010035 - 28 Dec 2020

Cited by 8 | Viewed by 3382

Abstract

The development of information and communication technology (ICT) is making daily life more convenient by allowing access to information at anytime and anywhere and by improving the efficiency of organizations. Unfortunately, malicious code is also proliferating and becoming increasingly complex and sophisticated. In fact, even novices can now easily create it using hacking tools, which is causing it to increase and spread exponentially. It has become difficult for humans to respond to such a surge. As a result, many studies have pursued methods to automatically analyze and classify malicious code. There are currently two methods for analyzing it: a dynamic analysis method that executes the program directly and confirms the execution result, and a static analysis method that analyzes the program without executing it. This paper proposes a static analysis automation technique for malicious code that uses machine learning. This classification system was designed by combining a method for classifying malicious code using a portable executable (PE) structure and a method for classifying it using a PE structure. The system has 98.77% accuracy when classifying normal and malicious files. The proposed system can be used to classify various types of malware from PE files to shell code. Full article

(This article belongs to the Special Issue Recent Advances on Deep Learning for Safety and Security of Multimedia Data in the Critical Infrastructure)

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23 pages, 681 KiB

Open AccessArticle

MADICS: A Methodology for Anomaly Detection in Industrial Control Systems

by Ángel Luis Perales Gómez, Lorenzo Fernández Maimó, Alberto Huertas Celdrán and Félix J. García Clemente

Symmetry 2020, 12(10), 1583; https://doi.org/10.3390/sym12101583 - 23 Sep 2020

Cited by 37 | Viewed by 4832

Abstract

Industrial Control Systems (ICSs) are widely used in critical infrastructures to support the essential services of society. Therefore, their protection against terrorist activities, natural disasters, and cyber threats is critical. Diverse cyber attack detection systems have been proposed over the years, in which each proposal has applied different steps and methods. However, there is a significant gap in the literature regarding methodologies to detect cyber attacks in ICS scenarios. The lack of such methodologies prevents researchers from being able to accurately compare proposals and results. In this work, we present a Methodology for Anomaly Detection in Industrial Control Systems (MADICS) to detect cyber attacks in ICS scenarios, which is intended to provide a guideline for future works in the field. MADICS is based on a semi-supervised anomaly detection paradigm and makes use of deep learning algorithms to model ICS behaviors. It consists of five main steps, focused on pre-processing the dataset to be used with the machine learning and deep learning algorithms; performing feature filtering to remove those features that do not meet the requirements; feature extraction processes to obtain higher order features; selecting, fine-tuning, and training the most appropriate model; and validating the model performance. In order to validate MADICS, we used the popular Secure Water Treatment (SWaT) dataset, which was collected from a fully operational water treatment plant. The experiments demonstrate that, using MADICS, we can achieve a state-of-the-art precision of 0.984 (as well as a recall of 0.750 and F1-score of 0.851), which is above the average of other works, proving that the proposed methodology is suitable for use in real ICS scenarios. Full article

(This article belongs to the Special Issue Recent Advances on Deep Learning for Safety and Security of Multimedia Data in the Critical Infrastructure)

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24 pages, 5439 KiB

Open AccessArticle

Deep-Learning Steganalysis for Removing Document Images on the Basis of Geometric Median Pruning

by Shangping Zhong, Wude Weng, Kaizhi Chen and Jianhua Lai

Symmetry 2020, 12(9), 1426; https://doi.org/10.3390/sym12091426 - 28 Aug 2020

Cited by 4 | Viewed by 2089

Abstract

The deep-learning steganography of current hotspots can conceal an image secret message in a cover image of the same size. While the steganography secret message is primarily removed via active steganalysis. The document image as the secret message in deep-learning steganography can deliver a considerable amount of effective information in a secret communication process. This study builds and implements deep-learning steganography removal models of document image secret messages based on the idea of adversarial perturbation removal: feed-forward denoising convolutional neural networks (DnCNN) and high-level representation guided denoiser (HGD). Further—considering the large computation cost and storage overheads of the above model—we use the document image-quality assessment (DIQA) as threshold, calculate the importance of filters using geometric median and prune redundant filters as extensively as possible through the overall iterative pruning and artificial bee colony (ABC) automatic pruning algorithms to reduce the size of the network structure of the existing vast and over-parameterized deep-learning steganography removal model, while maintaining the good removal effects of the model in the pruning process. Experiment results showed that the model generated by this method has better adaptability and scalability. Compared with the original deep-learning steganography removal model without pruning in this paper, the classic indicators params and flops are reduced by more than 75%. Full article

(This article belongs to the Special Issue Recent Advances on Deep Learning for Safety and Security of Multimedia Data in the Critical Infrastructure)

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15 pages, 4763 KiB

Open AccessArticle

A Face Image Virtualization Mechanism for Privacy Intrusion Prevention in Healthcare Video Surveillance Systems

by Jinsu Kim and Namje Park

Symmetry 2020, 12(6), 891; https://doi.org/10.3390/sym12060891 - 1 Jun 2020

Cited by 19 | Viewed by 3331

Abstract

Closed-circuit television (CCTV) and video surveillance systems (VSSs) are becoming increasingly more common each year to help prevent incidents/accidents and ensure the security of public places and facilities. The increased presence of VSS is also increasing the number of per capita exposures to CCTV cameras. To help protect the privacy of the exposed objects, attention is being drawn to technologies that utilize intelligent video surveillance systems (IVSSs). IVSSs execute a wide range of surveillance duties—from simple identification of objects in the recorded video data, to understanding and identifying the behavioral patterns of objects and the situations at the incident/accident scenes, as well as the processing of video information to protect the privacy of the recorded objects against leakage. Besides, the recorded privacy information is encrypted and recorded using blockchain technology to prevent forgery of the image. The technology herein proposed (the “proposed mechanism”) is implemented to a VSS, where the mechanism converts the original visual information recorded on a VSS into a similarly constructed image information, so that the original information can be protected against leakage. The face area extracted from the image information is recorded in a separate database, allowing the creation of a restored image that is in perfect symmetry with the original image for images with virtualized face areas. Specifically, the main section of this study proposes an image modification mechanism that inserts a virtual face image that closely matches a predetermined similarity and uses a blockchain as the storage area. Full article

(This article belongs to the Special Issue Recent Advances on Deep Learning for Safety and Security of Multimedia Data in the Critical Infrastructure)

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16 pages, 7380 KiB

Open AccessArticle

Multi-Column Atrous Convolutional Neural Network for Counting Metro Passengers

by Jun Zhang, Gaoyi Zhu and Zhizhong Wang

Symmetry 2020, 12(4), 682; https://doi.org/10.3390/sym12040682 - 24 Apr 2020

Cited by 7 | Viewed by 3537

Abstract

We propose a symmetric method of accurately estimating the number of metro passengers from an individual image. To this end, we developed a network for metro-passenger counting called MPCNet, which provides a data-driven and deep learning method of understanding highly congested scenes and accurately estimating crowds, as well as presenting high-quality density maps. The proposed MPCNet is composed of two major components: A deep convolutional neural network (CNN) as the front end, for deep feature extraction; and a multi-column atrous CNN as the back-end, with atrous spatial pyramid pooling (ASPP) to deliver multi-scale reception fields. Existing crowd-counting datasets do not adequately cover all the challenging situations considered in our work. Therefore, we collected specific subway passenger video to compile and label a large new dataset that includes 346 images with 3475 annotated heads. We conducted extensive experiments with this and other datasets to verify the effectiveness of the proposed model. Our results demonstrate the excellent performance of the proposed MPCNet. Full article

(This article belongs to the Special Issue Recent Advances on Deep Learning for Safety and Security of Multimedia Data in the Critical Infrastructure)

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23 pages, 1763 KiB

Open AccessFeature PaperArticle

AppCon: Mitigating Evasion Attacks to ML Cyber Detectors

by Giovanni Apruzzese, Mauro Andreolini, Mirco Marchetti, Vincenzo Giuseppe Colacino and Giacomo Russo

Symmetry 2020, 12(4), 653; https://doi.org/10.3390/sym12040653 - 21 Apr 2020

Cited by 14 | Viewed by 3679

Abstract

Adversarial attacks represent a critical issue that prevents the reliable integration of machine learning methods into cyber defense systems. Past work has shown that even proficient detectors are highly affected just by small perturbations to malicious samples, and that existing countermeasures are immature. We address this problem by presenting AppCon, an original approach to harden intrusion detectors against adversarial evasion attacks. Our proposal leverages the integration of ensemble learning to realistic network environments, by combining layers of detectors devoted to monitor the behavior of the applications employed by the organization. Our proposal is validated through extensive experiments performed in heterogeneous network settings simulating botnet detection scenarios, and consider detectors based on distinct machine- and deep-learning algorithms. The results demonstrate the effectiveness of AppCon in mitigating the dangerous threat of adversarial attacks in over 75% of the considered evasion attempts, while not being affected by the limitations of existing countermeasures, such as performance degradation in non-adversarial settings. For these reasons, our proposal represents a valuable contribution to the development of more secure cyber defense platforms. Full article

(This article belongs to the Special Issue Recent Advances on Deep Learning for Safety and Security of Multimedia Data in the Critical Infrastructure)

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