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A Systematic Literature Review of Cyber security Monitoring in Maritime

IEEE Access 2025年 13卷 85307-85329页

作者： Vaarandi, Risto Tsiopoulos, Leonidas Visky, Gabor Rehman, Muaan Ur Bahsi, Hayretdin Tallinn University of Technology Centre for Digital Forensics and Cyber Security Department of Software Science Tallinn19086 Estonia Northern Arizona University School of Informatics Computing and Cyber Systems FlagstaffAZ86011 United States

In recent years, many cyber incidents have occurred in the maritime sector, targeting the information technology (IT) and operational technology (OT) infrastructure. Although several literature review papers have been published in the maritime field, none of the previous studies has focused on cyber security monitoring, which aims at timely detection of cyber attacks with automated methods. The current article addresses this research gap and surveys the methods, algorithms, tools and architectures used for cyber security monitoring in the maritime sector. For the survey, a systematic literature review of cyber security monitoring studies is conducted. The first contribution of this article is the bibliometric analysis of related literature and the identification of the main research themes in previous works. For that purpose, our article presents a taxonomy for existing studies which highlights the main properties of maritime cyber security monitoring research. The second contribution of this article is an in-depth analysis of previous works and the identification of research gaps and limitations in existing literature. Based on our findings, we outline future research directions for cyber security monitoring in the maritime field. © 2013 IEEE.

关键词： Network intrusion

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ReACT_OCRS: An AI-Driven Anonymous Online Reporting System Using Synergized Reasoning and Acting in Language Models

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IEEE Access 2025年 13卷 92800-92815页

作者： Shaker Mahmoud, Amir A. Shishah, Wesam Mistry, Nilay R. National Forensic Sciences University School of Digital Forensics and Cyber Security Gandhinagar India Saudi Electronic University College of Computing and Informatics Riyadh Saudi Arabia

Victims and witnesses of cybercrime often hesitate to report incidents due to concerns over privacy, complexity, and fear of retaliation. Traditional reporting mechanisms require manual data entry, creating accessibility barriers and delaying response times. To address these challenges, this paper introduces ReACT_OCRS, an AI-driven voice-based cybercrime reporting system that allows victims and witnesses to anonymously submit complaints through audio recordings. Leveraging speech recognition transformers, a recent language model, and encryption, the system processes real-time multilingual voice inputs, extracts meaningful content, and classifies reports with high precision using a hybrid voting mechanism. Experimental evaluations on synthetically generated and human-validated datasets confirm the system’s ability to accurately transcribe, classify, and securely process audio complaints while preserving user anonymity. This work improves cybercrime reporting by making it more accessible, efficient, and secure, fostering greater participation from victims and witnesses. © 2013 IEEE.

关键词： Modeling languages

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Single Qubit Quantum Logistic-Sine XYZ-Rotation Maps:An Ultra-Wide Range Dynamics for Image Encryption

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Computers, Materials & Continua 2025年第5期83卷 2161-2188页

作者： De Rosal Ignatius Moses Setiadi T.Sutojo Supriadi Rustad Muhamad Akrom Sudipta Kr Ghosal Minh T.Nguyen Arnold Adimabua Ojugo Research Center for Quantum Computing and Materials Informatics Faculty of Computer ScienceDian Nuswantoro UniversitySemarang50131Indonesia Department of Cyber Forensics and Information Security Behala Government Polytechnic756Upendra Nath Banerjee RoadParnasreeBehalaKolkata700060India Thai Nguyen University of Technology Thai Nguyen UniversityThai Nguyen240000Vietnam Department of Computer Science Federal University of Petroleum ResourcesEffurun330102Nigeria

Data security has become a growing priority due to the increasing frequency of cyber-attacks,necessitating the development of more advanced encryption *** paper introduces Single Qubit Quantum Logistic-Sine XYZ-Rotation Maps(SQQLSR),a quantum-based chaos map designed to generate one-dimensional chaotic sequences with an ultra-wide parameter *** proposed model leverages quantum superposition using Hadamard gates and quantum rotations along the X,Y,and Z axes to enhance *** numerical experiments validate the effectiveness of *** proposed method achieves a maximum Lyapunov exponent(LE)of≈55.265,surpassing traditional chaotic maps in *** bifurcation analysis confirms a uniform chaotic distribution,eliminating periodic windows and ensuring higher *** system also generates an expanded key space exceeding 10^(40),enhancing security against brute-force ***,SQQLSR is applied to image encryption using a simple three-layer encryption scheme combining permutation and substitution *** approach is intentionally designed to highlight the impact of SQQLSR-generated chaotic sequences rather than relying on a complex encryption *** method achieves an average entropy of 7.9994,NPCR above 99.6%,and UACI within 32.8%–33.8%,confirming its strong randomness and sensitivity to minor *** robustness tests against noise,cropping,and JPEG compression demonstrate its resistance to statistical and differential ***,the decryption process ensures perfect image reconstruction with an infinite PSNR value,proving the algorithm’s *** results highlight SQQLSR’s potential as a lightweight yet highly secure encryption mechanism suitable for quantum cryptography and secure communications.

关键词： Single qubit quantumchaotic quantumchaotic map quantumimage encryption quantumlogistic map quantum sine map

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Comparative Evaluation of Deep Learning Models in Alzheimer’s Disease Diagnosis

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Procedia Computer Science 2025年 258卷 2352-2361页

作者： Leena Arya Yogesh Kumar Sharma Smitha Nayak Janaki Sivakumar Assistant Professor Computing Department Muscat College Oman Associate Professor Programme leader Computer security and forensics Department of Computer Science and Creative Technologies Global College of Engineering and Technology Muscat Oman

Alzheimer’s Disease (AD) is an ever-evolving neurodegenerative confusion that influences the neurons of the mind in a manner that hinders the patients’ ability to solve problems and remember issues and that requires timely and proper identification to enable appropriate management and treatment. Based on MRI medical images, deep learning models have become one of the most effective approaches for analyzing the signs of the early stages of AD. This research, therefore, carries out a comparative analysis of five deep learning models such as Convolutional Neural Networks (CNNs) like AlexNet, VGGNet, and ResNet, as well as Long Short-Term Memory (LSTM) networks and 3D Convolutional Neural Networks (3D-CNNs) in the finding of Alzheimer’s Sickness from medical imaging data. Each model’s arrangement parameters, for example, layer configurations, learning rate, batch size, optimizer, number of epochs, loss function, applying techniques for regularization, activation functions, and the input dimensions, using pre-trained weights, were carefully initialized to yield a proper comparison between the models. Diagnostic performances were evaluated utilizing precision, responsiveness(recall), specificity, and region under the receiver operating characteristic curve (AUC-ROC). ResNet outperformed other profound learning models in diagnosing Alzheimer’s Disease from MRI scans with a precision of 0.91 and AUC-ROC of 0.95. At the same time, VGGNet, AlexNet, LSTM, and 3D-CNNs demonstrated varying strengths and limitations in balancing accuracy, computational efficiency, and feature extraction.

关键词： Artificial Intelligence (AI) Cognitive Impairment Data Augmentation Diagnosis Accuracy Medical Imaging

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Design and Implementation of an IoT-based Emergency Alert and GPS Tracking System using MQTT and GSM/GPS Module

Design and Implementation of an IoT-based Emergency Alert an...

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Trends in Material Science and Inventive Materials (ICTMIM), International Conference on

作者： P. Chinnasamy Pranavdev P. S Chintha Sivakrishnaiah T. Sathiya Irfan Alam Divya Priya Degala Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamilnadu India Department of MCA Annamacharya University Rajampet India Department of CSE Sona College of Technology Salem Tamilnadu India Cyber Security and Digital Forensics Division SCAI VIT Bhopal University Sehore India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

An optimally optimized emergency response system can prove to be lifesaving by reducing response time during the state of emergency. This paper proposes an IoT-based emergency alert and GPS tracking system implemented on an ESP32 microcontroller and an A9G GSM/GPS module. The system has a continuous log of user position and device battery level and pushes data in intervals through MQTT to the Adafruit IO platform for remote monitoring. To achieve best battery life, the A9G module goes into low power mode in idle mode but keeps GPS monitoring running. One more feature highlighted is the SOS button, which makes an emergency call by sending the user's location as an SMS and calling a pre-configured contact number. The system also maintains data integrity with automatic reconnect of MQTT broker when it loses connection. Low power and battery portable, the system is applicable in personal protection, home health care for elderly, outdoor adventure, and remote area surveillance. The article explains hardware design, software process, MQTT-based data communication, and testing of the performance of the system based on precision, network, and response to emergencies. The results verify reliability and cost saving of the system in real-time monitoring and reporting of emergencies. With its energy-saving and usability design, it helps in the development of IoT-backed safety solutions for real-time tracking and emergency response in case of a need.

关键词： Safety devices Protocols Emergency services Real-time systems Batteries Time factors Older adults Usability Global Positioning System Testing

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A dark web story in-depth research and study conducted on the dark web based on forensic computing and security in Malaysia

A dark web story in-depth research and study conducted on th...

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2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering, ICPCSI 2017

作者： Rafiuddin, Mohammed Farook Bin Minhas, Hamza Dhubb, Prethpal Singh Forensics Computing and Security Asia Pacific University Malaysia

ISBN: (纸本)9781538608135

The following is a research conducted on the Dark Web to study and identify the ins and outs of the dark web, what the dark web is all about, the various methods available to access the dark web and many others. The researchers have also included the steps and precautions taken before the dark web was opened. Apart from that, the findings and the website links / URL are also included along with a description of the sites. The primary usage of the dark web and some of the researcher's experience has been further documented in this research paper. © 2017 IEEE.

关键词： Malware

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An empirical study on the performance of java/.net cryptographic APIs

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Information Systems security 2007年第6期16卷 344-354页

作者： Francia, Guillermo A. Francia, Rahjima R. Computer Security and Forensics Laboratory Mathematical Computing and Information Sciences Department Jacksonville State University Jacksonville AL United States

The unprecedented growing demands on security and privacy protection ushered the proliferation of cryptographic tools. This article presents a study on the performance comparison of cryptographic Application Program Interfaces (APIs) that are implemented for the Java and the .Net frameworks. The results of the study clearly indicate the superiority of a set of commercial cryptographic APIs over its open-source counterpart. © 2007 Taylor & Francis.

关键词： JAVA (Computer program language) APPLICATION program interfaces (Computer software) MICROSOFT .NET Framework CRYPTOGRAPHY security management COMPUTER software

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Extraction of Actionable Threat Intelligence from Dark Web Data 5

Extraction of Actionable Threat Intelligence from Dark Web D...

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5th International Conference on Control, Communication and computing, ICCC 2023

作者： Varghese, Varsha Mahalakshmi, S. Senthilkumar, K.B. ER&DCI Institute of Technology Cyber Forensics and Information Security Department of Computer Science & Engineering Thiruvananthapuram India Cyber Security Group Centre for Development of Advanced Computing Thiruvananthapuram India

ISBN: (纸本)9798350334128

Darknet has emerged as an excellent platform for cybercriminals to conduct various illicit activities such as operating a fully functional crypto-currency-based marketplace or maintaining a highly anonymous communication channel. However, it also acts as a source of Cyber Threat Intelligence which is the information about the techniques, tactics, or motives of an emerging threat. The life cycle of a Darknet-based Cyber Threat Intelligence solution mainly consists of the Collection, Processing, Analysis, and Production of Data from the Darknet. The Collection and Processing stages deal with accumulating information from various dark web pages and transforming the data into some format using a big data system. The Analysis part is integral to the Threat Intelligence schema since it constitutes the extraction of named entities like Organization, Offensive Activity, Tools, etc., and the determination of relationships between these entities. The production stage is like data mining where some knowledge is extracted that can be relevant to an organization under consideration. Such knowledge can mitigate risks and disrupt any targeted cyber-attack campaigns, making Cyber Threat Intelligence actionable. In this research, an open-source-intelligence toolset is used to scan and collect data from the dark web forums through crawling and scrapping. The collected data is then ingested into a state-of-the-art NLP model that extracts actionable threat intelligence using Named Entity Recognition. The experimental results indicate that the model could identify HackerIds, tools, software, organizations, and other entities in the discussions of dark web forums with better efficiency and accuracy. This could be used to identify the source of a data leak, the release of new malware, evidence of a new exploit, and other offensive activities. © 2023 IEEE.

关键词： Extraction

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Perceptual Colour-based Geolocation of Human Trafficking Images for Digital Forensic Investigation 3

Perceptual Colour-based Geolocation of Human Trafficking Ima...

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3rd Cyber Research Conference - Ireland, Cyber-RCI 2024

作者： Herrmann, Jessica Bamigbade, Opeyemi Sheppard, John Scanlon, Mark Forensics and Security Research Group School of Computer Science Univeristy College Dublin Ireland Department of Computing and Mathematics South East Technological University Waterford Ireland

ISBN: (纸本)9798350390100

This paper investigates the effectiveness of colour-based descriptors in Content-Based Image Retrieval (CBIR) and examines the impact of various parameters on image matching accuracy. The aim is to improve image retrieval methods to support digital forensic investigators in human trafficking cases. Colour values are used as key components to describe specific image characteristics, and the technique is evaluated on the Hotels-50K dataset. The method achieved a Top-50 accuracy of over 95%, enabling efficient data triage and significantly reducing the volume of images to be examined. Using 2 colour descriptors is found to optimise the balance between information richness and dimensionality reduction. Performance is further improved by optimised image selection, reducing false-positive rates, and increasing robustness. The approach demonstrates potential in advancing image analysis tools in human trafficking investigations and other contexts, opening new avenues for using colour values in crime detection and image data analysis. Future research may refine the Euclidean distance method used in the image similarities measure by introducing weighted distance measurements to reduce the impact of common colour values, and investigate lighting and saturation effects. © 2024 IEEE.

关键词： Dimensionality reduction

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Forensic investigation of social networking applications

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Network security 2014年第11期2014卷 9-16页

作者： Taylor, Mark Haggerty, John Gresty, David Almond, Peter Berry, Tom Liverpool John Moores University United Kingdom Nottingham Trent University United Kingdom Centre for Computer Security Audit Forensics and Education in the University of Greenwich United Kingdom School of Computing and Mathematical Sciences Liverpool John Moores University United Kingdom

Social networking applications such as Facebook, LinkedIn, MySpace and Twitter provide facilities including email, blogging, instant messaging and photo sharing for social and commercial exchange.1 There has been a rapid growth in the use of social networking applications by both individuals and organisations.2,3 And an increasing number of organisations use Facebook and Twitter as part of their marketing campaigns.4,5 © 2014, Elsevier Ltd. All rights reserved.

关键词： Marketing

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