Network Intrusion Research Articles

Predicting the Performance of Lithium Adsorption and Recovery from Unconventional Water Sources with Machine Learning

Selective lithium (Li) recovery from unconventional water sources (UWS) (e.g., shale gas waters, geothermal brines, and rejected seawater desalination brines) using inorganic lithium-ion sieve (LIS) materials can address Li supply shortages and distribution issues. However, the development of high-performance LIS materials and the optimization of recovery-related operating parameters are hampered by the variety of production methods, intricate procedures, and experimental expenses. Machine learning (ML) techniques offer potential solutions for enhancing LIS material development. We collected literature data on Li adsorption, categorizing 16 parameters into adsorbent parameters, operating parameters, and solution components. Three tree-based algorithms—Random Forest (RF), Gradient Boosting Decision Trees (GBDT), and Extreme Gradient Boosting (XGBoost)—were used to evaluate the impact of these parameters on lithium adsorption. The grouped random splitting method limited data leakage and mitigated overfitting. XGBoost demonstrated the best performance, with an R² of 0.98 and a root-mean-squared error (RMSE) of 1.72. The SHAP values highlighted that operating parameters were the most influential, followed by adsorbent parameters and coexisting ion concentrations. Therefore, focusing on optimizing operating parameters or making targeted improvements on LIS based on operating conditions will enhance LIS performances in UWS. These insights are crucial for optimizing Li adsorption processes and designing effective inorganic LIS materials.

Artificial Intelligence based Leak Detection in Blended Hydrogen and Natural Gas Pipelines

In the transition towards a hydrogen-based energy system, the strategic use of pipelines is crucial for efficient hydrogen distribution. Leveraging existing natural gas pipelines to carry a mix of hydrogen and natural gas offers a cost-effective alternative to building new infrastructure. This study explores the development of a leak detection system compatible with existing pipelines, specifically tailored for the blended hydrogen and natural gas mix. Given the scarcity of leak data for blended hydrogen-natural gas pipelines, the study introduces a Real-Time Transient Model (RTTM) for blended gases, simulating leak dynamics to generate necessary data. Additionally, a leak detection system (LDS) is developed using a fusion of Convolutional Neural Network (CNN) and Explainable Artificial Intelligence (XAI) through Adaptive Neuro-Fuzzy Inference Systems (ANFIS). This LDS framework overcomes the “black box” issue common in AI-driven systems, enabling reliable detection. The integration of Explainable and traditional AI techniques holds promising implications for blended hydrogen pipelines by enhancing the safety and efficiency of hydrogen transportation, thereby mitigating economic and environmental impacts, and addressing public concerns.

Network Intrusion Detection Using Feature Selection Techniques: Bacterial Forage Optimization Algorithm

Network Intrusion Detection Using Feature Selection Techniques: Bacterial Forage Optimization Algorithm

Leveraging blockchain for sustainable supply chain management: A data privacy and security perspective

This review examines how blockchain technology can be leveraged to enhance data privacy and security in sustainable supply chain management (SSCM). As global supply chains become increasingly complex and the demand for sustainability grows, ensuring data privacy and security has become a critical concern. Traditional supply chain systems often face challenges such as data breaches, lack of transparency, and difficulty in tracing products and materials. Blockchain technology, with its decentralized, immutable, and transparent architecture, offers a promising solution to these challenges. Blockchain can enhance data security by ensuring that data is tamper-proof, traceable, and encrypted, thus protecting sensitive information across the supply chain. It provides transparency while allowing permissioned access, ensuring that stakeholders can verify data without exposing confidential information. Furthermore, privacy-preserving technologies such as zero-knowledge proofs and homomorphic encryption allow verification of data without compromising its security. Smart contracts enable automated compliance with regulatory frameworks like GDPR, reducing the risk of human error and improving operational efficiency. The integration of blockchain in SSCM can improve traceability, transparency, and accountability, thereby promoting environmental and social sustainability. By tracking the origin and journey of goods, blockchain helps verify ethical sourcing practices and reduce carbon footprints. However, the technology also presents challenges, including scalability, integration with legacy systems, and cost considerations. Through case studies in industries such as food, textiles, and renewable energy, this review highlights the practical applications and benefits of blockchain for SSCM. It concludes that blockchain has the potential to revolutionize supply chain operations, but careful consideration must be given to overcoming its technical and financial barriers to widespread adoption.

Privacy, confidentiality and ethical concerns in audio ai assistants: A comparative study of North American, European, and Asian Markets

This study examines the ethical, privacy, and confidentiality issues with audio AI assistants in the markets of North America, Europe, and Asia. Understanding the ramifications of data collecting, storage, and usage is crucial as the use of technologies like Google Assistant and Amazon Alexa grows. The study highlights significant regulatory differences between the areas; North America offers a fragmented landscape, and many Asian nations are still developing their data protection legislation, while Europe leads with strong frameworks like the General Data Protection Regulation (GDPR). Key challenges identified include a lack of user awareness regarding privacy rights, extensive data collection practices, unauthorized listening, data breaches, and cultural differences influencing attitudes toward data protection. The findings reveal that these challenges impact user trust and hinder the responsible innovation of AI technologies. Recommendations for improvement include establishing global regulations to promote consistency in data protection, adopting best practices such as privacy-by-design principles, and enhancing user education initiatives. Additionally, the study identifies critical areas for future research, particularly the role of AI assistants in sensitive sectors like healthcare and education, where privacy concerns may differ significantly. Overall, this comparative analysis provides a foundational understanding of audio AI assistants' ethical landscape and underscores the importance of addressing these issues to foster a secure and trustworthy digital environment.

A comprehensive survey of cybercrimes in India over the last decade

Since the 1990s, the integration of technology into daily life has led to the creation of an extensive network of interconnected devices, transforming how individuals and organizations operate. However, this digital transformation has also spurred the rise of cybercrime, criminal activities perpetrated through networks or computer systems. Cybercrime has become a global concern, presenting significant challenges to security systems. Although advancements in digital technology have enhanced efficiency, they have also opened new avenues for exploitation by cybercriminals, highlighting the urgent need for advanced cybersecurity measures. The escalating number of cyberattacks and associated risks in the past decade highlights the critical importance of protecting sensitive data and safeguarding information systems. Cybercrimes range from financial fraud and phishing scams to identity theft and online harassment, posing substantial risks to both individuals and organizations. In response, governments, law enforcement agencies, and cybersecurity units have intensified their efforts to address these threats. In recent years, India has experienced a significant surge in cybercrime incidents, with a notable increase in cases involving ransomware, data breaches, and social engineering attacks. The growing penetration of internet services, the expansion of e-commerce, and the rapid adoption of digital payment systems have made individuals and organizations more vulnerable to cyber threats. Key areas affected include banking, healthcare, and government sectors, which are frequently targeted due to the sensitive nature of the data they handle. To combat these risks, there is an increasing focus on public awareness, cybersecurity education, and robust regulatory frameworks. This paper provides an in-depth analysis of cybercrime, with a focus on developing innovative prevention strategies, strengthening internal security protocols, and classifying key cybercrime terminologies to better understand their implications for digital infrastructure.

Machine learning techniques for enhancing security in financial technology systems

The financial technology (fintech) industry has transformed the way financial services are delivered, offering enhanced convenience, accessibility, and efficiency. However, this rapid digitization has also increased the sector’s exposure to a wide array of security risks, including cyberattacks, fraudulent activities, data breaches, and insider threats. As traditional security measures struggle to keep pace with the growing sophistication of these threats, machine learning (ML) techniques have emerged as promising tools for reinforcing security in fintech systems. Machine learning models can analyze vast amounts of data, detect anomalous behavior, predict potential risks, and respond to security threats in real-time. This paper explores the potential of machine learning to enhance security in financial technology by addressing key challenges such as anomaly detection, fraud detection, intrusion detection systems (IDS), and risk management. We provide an overview of common machine learning algorithms—such as decision trees, neural networks, support vector machines (SVM), and clustering methods—that are applied to these security tasks. Additionally, we discuss the evaluation metrics used to measure the accuracy, precision, recall, and overall effectiveness of these models. Through real-world case studies, we highlight successful implementations of machine learning in fintech security, offering insights into best practices and lessons learned. Despite the many benefits, there are also significant challenges and limitations associated with the adoption of machine learning in fintech, including concerns over data privacy, the accuracy and reliability of models, and the computational resources required for large-scale deployment. Looking ahead, this paper identifies emerging trends and potential innovations that could further enhance security in fintech, from advancements in deep learning to the integration of artificial intelligence with blockchain technology. Finally, we propose areas for future research to address unresolved issues and support the continued development of machine learning-driven security solutions in the fintech industry.

Fortifying cloud environments against data breaches: A novel AI-driven security framework

As cloud computing continues to dominate the modern technological landscape, organizations face growing challenges in preventing data breaches and sophisticated cyber threats. The increasing complexity and scale of cloud environments require advanced security mechanisms to address evolving threats. This paper introduces "SecureCloudAI," a cutting-edge AI-driven security framework designed to fortify sensitive data within cloud infrastructures. SecureCloudAI leverages a hybrid approach that combines machine learning models like Random Forest and deep learning techniques, including Long Short-Term Memory (LSTM) networks, to detect, classify, and respond to potential security breaches in real-time. The system offers robust malware detection, network traffic analysis, and web intrusion detection while maintaining scalability and efficiency across large cloud environments. Experimental results demonstrate SecureCloudAI's high accuracy, with malware detection at 94.78% and network traffic classification at 90.92%, ensuring the system can handle both complex and emerging threats. This AI-driven solution marks a significant advancement in cloud security, providing organizations with an adaptive and scalable tool to safeguard their data against the ever-growing cyber threat landscape.

AI techniques applied to network intrusion detection system by using genetic approaches

The ability to identify and assess potential security risks to a communication network is a critical function of network intrusion detection systems. The data it provides regarding the frequency and type of assaults is a valuable addition to other network security measures, including firewalls. Typically, an NIDS will have a sensor that scans all incoming and outgoing packets on the monitored network, flags those that seem suspicious, and then sends the flagged packets and an alert message to a server system, which will then store them and analyse them in conjunction with other events. First, we standardised the protocol structure. Then, we used a genetic approach to generate mutation and cross-over values for device identification. Finally, we used a modified J48 decision tree algorithm to conduct our search. The developed algorithms/ones outperform the existing methods in terms of performance. As an initial step in detecting an intrusion, the 64-byte structured protocol standardisation technique is created. The wire shark tool is used to monitor the communication process network, taking into account all potential transactions. An array is created from the recorded packets. Included in the array are details about the frames, as well as the protocol type, hardware device type, source and destination IP addresses, MAC addresses, and data. This information is transformed by the 64-byte protocol structured standardisation. Because all of the necessary attributes are determined in the same place by this common protocol structure procedure, finding the MAC and IP addresses in packets should take as little time as possible. As a second step, the product and device details are supplied by the least and most important 16 bits of the MAC address. Using the cross-over and mutation functions, the genetic iv method compares the invader device to the Current Active Directory List (CADL).

Intellectual Property Risks and Countermeasures in Cross-Border E-Commerce

Cross-border e-commerce has experienced rapid development driven by globalization trends, emerging as a significant force propelling international trade and economic growth. However, with the intensification of legal, cultural differences, and technological changes across nations, intellectual property issues in the cross-border e-commerce sector have become increasingly prominent. Intellectual property serves as the core of business innovation and market competition; any infringement thereof can lead to substantial financial losses for enterprises and erode market trust and brand reputation. Cross-border e-commerce enterprises face multifaceted intellectual property risks, including infringement, brand theft, and data leakage, particularly within differing legal systems, complicating these issues further. The protection of intellectual property is not only crucial for safeguarding corporate interests but also a necessary prerequisite for ensuring the healthy development of the global market. In response, cross-border e-commerce enterprises must adopt proactive measures, ranging from institutional construction to enhancing user awareness, to comprehensively address intellectual property risks and ensure their competitiveness and sustainable development in the international market.

Sentiment classification for insider threat identification using metaheuristic optimized machine learning classifiers

This study examines the formidable and complex challenge of insider threats to organizational security, addressing risks such as ransomware incidents, data breaches, and extortion attempts. The research involves six experiments utilizing email, HTTP, and file content data. To combat insider threats, emerging Natural Language Processing techniques are employed in conjunction with powerful Machine Learning classifiers, specifically XGBoost and AdaBoost. The focus is on recognizing the sentiment and context of malicious actions, which are considered less prone to change compared to commonly tracked metrics like location and time of access. To enhance detection, a term frequency-inverse document frequency-based approach is introduced, providing a more robust, adaptable, and maintainable method. Moreover, the study acknowledges the significant impact of hyperparameter selection on classifier performance and employs various contemporary optimizers, including a modified version of the red fox optimization algorithm. The proposed approach undergoes testing in three simulated scenarios using a public dataset, showcasing commendable outcomes.

Caregiver and Youth Characteristics That Influence Trust in Digital Health Platforms in Pediatric Care: Mixed Methods Study.

Combining patient-generated health data and digital health platforms may improve patient experience and population health, mitigate rising health care costs, reduce clinician burnout, and enable health equity. However, lack of trust may be a notable barrier to the data-sharing required by such platforms. Understanding sociodemographic, health, and personal characteristics will enable developers and implementers of such technologies to consider these in their technical design requirements. This study aims to understand relationships between sociodemographic characteristics of caregivers of children or adolescents and trust in and willingness to use digital platforms to store and share personal health information for clinical care and research. This study used a mixed methods approach, including surveys of caregivers of youth aged <18 years living in Canada or the United States and youth aged 16 to 17 years living in Canada, as well as web-based bulletin board discussions to further explore topics of trust in data sharing. Sociodemographic and survey data were tabulated and explored using proportional odds ordinal regression models. Comments from web-based group discussions were analyzed thematically using a coding approach to identify issues important to the participants. Survey data from 1128 caregivers (female participants: n=549, 48.7%; 36-50 years old: n=660, 58.5%; Canadian: n=603, 53.5%; urban population: n=494, 43.8%) were collected, of which 685 (60.7%) completed all questions. Data from 173 youth (female participants: n=73, 42.2%; urban population: n=94, 54.3%) were collected, of which 129 (74.6%) completed all questions, and data were available for analysis. Furthermore, among 40 participants, 23 (58%) caregivers contributed to the web-based discussion boards. Related to trust, living in a rural area (vs urban; odds ratio [OR] 0.66, 95% CI 0.46-0.95) resulted in lower concern for data privacy and security, while having an undergraduate (OR 1.82, 95% CI 1.30-2.55) or graduate degree (vs secondary or trade school; OR 2.50, 95% CI 1.68-3.73) resulted in higher levels of concern. Living with a chronic disease (OR 1.81, 95% CI 1.35-2.44) increased levels of concern regarding data privacy and security. Interestingly, those with chronic disease were more willing to use digital platforms for clinical care and share personal health information for not-for-profit research. Caregivers were most concerned about data breaches involving data from their children but also highlighted that digital platforms would allow for better coordination of care for their children. Our research confirms the willingness of caregivers and youth to use digital platforms for both clinical care delivery and research and suggests that the value of a digital platform may outweigh the risks of its use. Engagement of end users in co-designing such platforms has the potential to enhance digital trust. However, digital trust varies across sociodemographic groups; therefore, diverse end user engagement is necessary when designing digital applications.

Deep learning enables the fully automated quantification of high-resolution cardiac CT images: human in loop for model development

Abstract Introduction Cardiac computed tomography (CCT) images provide vast spatial and temporal information. Segmentation of the structure of interest is necessary to provide quantitative information. However, manual segmentation of cardiac substructures is highly time-consuming. Objective This study aims to develop a robust deep-learning segmentation model for cardiac substructure segmentation in 3D CCT images. Method We initially developed a 3D U-Net architecture, incorporating extensive augmentation, for the segmentation of the Left Ventricle Myocardium (LVM), the blood cavities of the Left and Right Ventricles (LV, RV), and the Left and Right Atria (LA, RA). This model utilized CT images from 20 patients who had available manual segmentation. Subsequently, we applied this initial model to datasets from two centers, including different generations of scanners. We then visually inspected the segmentation, selected the highest-quality segmentations, and modify it if necessary, and excluded the low-quality data. We continued fine-tuning the model with the new dataset until we reached a count of 456 patients. As there were multiple images from one patient during this model's development, we generated datasets to develop a new model using the entire new dataset to avoid any data leakage in the reported metrics. The model was developed using data from Center 1 (5-fold cross-validation) and then evaluated on an external test set from Center 2 (126 patients) and also 4D images from Center 1. Various metrics were calculated to assess the model's performance. Results In the internal test set, utilizing 5-fold CV, the model demonstrated high segmentation accuracy, achieving an overall Dice coefficient of 0.98, with individual scores of 0.96 for LVM, 0.98 for LV, 0.98 for RV, 0.99 for RA, and 0.98 for LA. We assessed the automatic quantification capability of the segmentation model from 256 patients, resulting in an R2 value greater than 0.92 for both LV end-diastolic and systolic volumes. In contrast, the model maintained robust performance on the external dataset, achieving an overall Dice coefficient of 0.96, with individual scores of 0.95 for LVM, 0.98 for LV, 0.97 for RV, 0.98 for RA, and 0.94 for LA. Conclusion We provided a large cohort of ground truth segmentations for different cardiac CT substructures using a human-in-the-loop strategy. Subsequently, we developed a deep learning model for highly accurate automated segmentation of these substructures. This model allows for fast and fully automatic segmentation and quantification of 3D and 4D cardiac CT images, from which various quantitative metrics can be extracted from different cardiac substructures.

To Study on Cyber Crime Burgeoning in India and Crucial Actions Need for Every Cybercrime Victim

India is facing a great deal of challenges due to the rapid digitalization and broad adoption of technology in many different areas. One such concern is cybercrime. In this paper explores the nature, scope, and impact of cybercrime in the Indian context. The rising ubiquity of internet usage has rendered people, companies, and governmental organizations more susceptible to a range of cyber threats, such as identity theft, financial fraud, data breaches, and cyber harassment. Despite developments in cyber security protection, the lack of awareness and poor regulatory frameworks has aggravated the situation. To address these issues, the Indian government has put in place a number of efforts, including the National Cyber Security Policy and the Information Technology Act, 2000. But enforcement is still patchy, and a lot of crimes go unreported or ignored. The problem has been made worse by insufficient regulatory frameworks and a lack of knowledge, even in spite of advances in cyber security measures. But enforcement is still patchy, and a lot of crimes go unreported or ignored. This research paper emphasizes India's cybercrime victims have several legal options, the need for a multimodal strategy that includes improved cyber security infrastructure, better legislative frameworks and greater public awareness in order to tackle cybercrime. In light of the transnational character of cyber threats, it also highlights the significance of international cooperation. In the end, a thorough plan is necessary to protect India's digital environment and guarantee the security and safety of its people in a world that is becoming more interconnected by the day

Research on the Impact and Solution Strategies of Blockchain Technology on Data Security and Transparency in Enterprise Digital Transformation

Abstract: This research delves into the profound impact of blockchain technology on enhancing data security and transparency during enterprises digital transformation. By dissecting blockchains core attributesdecentralization, immutability, and advanced securitythis study elucidates how it offers pragmatic solutions to prevalent challenges such as data breaches and opaque information processing. Leveraging an extensive literature review, meticulous case analysis, and expert insights, the article comprehensively illustrates blockchains diverse applications in ensuring data integrity and enhancing transparency across critical sectors, including financial services, supply chain management, and healthcare. Furthermore, the study identifies and deliberates on existing research gaps in blockchain technologys application within digital transformation, proposing a systematic framework to deepen comprehension of its role in this process. Moreover, it conducts an in-depth analysis of the technical, operational, and environmental hurdles hindering blockchain adoption, offering a series of action strategies to enable enterprises to harness their potential effectively amidst the swiftly evolving digital landscape. Through this comprehensive inquiry, the article underscores the strategic significance of blockchain technology in augmenting corporate operational transparency and data security. Additionally, it elucidates how blockchain can bolster corporate governance, compliance, and stakeholder trust. Overall, this synthesis provides a thorough review and profound insights into research findings, underscoring blockchains pivotal role in addressing contemporary digital challenges while furnishing invaluable strategic guidance for industry innovation and advancement. The research outcomes hold substantial implications for practitioners and researchers, signaling the transformative potential of blockchain technology in reshaping enterprise operations and charting a course for future exploration in this pivotal domain.

The application and challenges of different face recognition technologies in the three major fields of security, social media, and medical care

Abstract. Face recognition technology is a very important part of modern technology, which can not only be used to ensure security, but also can be used in the field of information organization and content division. However, with the popularization and application of face recognition technology, many problems that need to be solved urgently have emerged: excessive computing resources are consumed in order to pursue high-precision recognition, which brings computing pressure; The basis for improving recall is the need for a lot of power and memory; If security is not guaranteed, it can cause problems such as data breaches. The demand for face recognition technology is different in different use fields, so the purpose of this study is to combine the scene requirements and technical advantages more reasonably. The research results are as follows: the high accuracy and recall rate of 3D convolutional neural networks (3DCNNs) ensure that it can be used safely in high-precision and high-security scenarios. Lightweight Convolutional Neural Network (MobileNetV2) is suitable for resource-constrained environments due to its low memory consumption and low communication cost. Edge computing real-time face recognition (EC-RFERNet) is the most suitable for large-scale popularization and application among the three because of its lowest power consumption and latency. This study deeply explores the advantages and disadvantages of different facial recognition technologies and finds solutions to their shortcomings. According to their unique advantages combined with the requirements of commonly used scenarios, it provides a scientific basis for the deployment of face recognition technology in different fields. However, due to limited information, this paper cannot cover all application scenarios and the latest technologies, so it is hoped that in the future, we can combine the advantages of different technologies to develop more comprehensive face recognition technology and make more reasonable technical planning.

Digitalizations Enhancement in HR Practices: The Impact of Incorporating AI in the Process of Recruitment and Selection

Many organizations desires to attract the most value-adding talent has pushed their increased adoption of artificial intelligence in the processes of recruitment and selection. As AI can be generated into tools to conduct an analysis of applicants and provide an overview, it reduces the recruiters time allocation on administrative tasks to focus on making more advanced decisions to execute the businesss desired strategic directions and developments. This paper mainly examines the impact of AI in the talent acquisition processes on the three aspects of practical, ethical, and strategic impacts. The practical approach can improve the efficiency in recruitment and selection to find the best-fit candidate through AI algorithms for certain roles. The ethical approach of AI can ensure a fair talent acquisition process that reduces bias and discrimination to support final decision-making. The strategic approach allows organizations to use AI in conducting recruitment and selection practices with considerations on the future growth of the business, prospecting the required talents to achieve such growth. The attraction of using AI in the hiring process can lead to a more digitalized and trendy employer branding, making organizations appeal and expand to innovative pools of candidates. However, there are downsides to all approaches, including the lack of human involvement, the risks of data security and breaches, and the consistency of employer branding in utilizing AI technology. In exploring the benefits and limitations of AIs impact on talent acquisition, there should be a more structured approach to unleashing this technology in the HR practice.

Research on the Influence of AI Technology and Optimization Strategy on the Chinese Manufacturing Enterprises about Digital Transformation

Abstract: The rapid evolution of the digital economy has fostered a favorable environment for the growth of the artificial intelligence (AI) industry, catalyzing a transformative shift across diverse sectors. AI technology is driving the digital transformation landscape, contributing to the emergence of a novel economic framework characterized by enhanced unity, fairness, and operational efficiency. Particularly in China's manufacturing domain, substantial governmental support has propelled the widespread adoption of AI, thereby facilitating significant strides towards industrial modernization. However, despite the promising trajectory, notable challenges persist. These include the scarcity of AI talent, concerns regarding data security such as leakage, and the ongoing refinement of compatibility with existing systems. Addressing these challenges necessitates a multifaceted approach. Efforts should prioritize bolstering personnel training initiatives in AI-related domains through collaborative engagements with industry, universities, and research institutes. This cooperative endeavor can nurture a skilled workforce adept at navigating the complexities of AI implementation. Moreover, stringent measures must be implemented to safeguard sensitive data, including robust encryption protocols, secure computing practices, and meticulous access controls to mitigate the risks of data breaches. Furthermore, promoting standardization processes and embracing data format conversion tools are critical steps towards enhancing interoperability across disparate systems. In conclusion, this discourse underscores the pivotal role of AI in driving the digital transformation journey of Chinese manufacturing enterprises. By addressing pertinent challenges and embracing strategic initiatives, these enterprises can fortify their resilience, navigate future complexities, and achieve sustainable development in the digital era.

Mitigating Maritime Cybersecurity Risks Using AI-Based Intrusion Detection Systems and Network Automation During Extreme Environmental Conditions

The maritime industry is increasingly confronted with a myriad of cybersecurity challenges exacerbated by extreme environmental conditions, technological advancements, and heightened reliance on automation. This review paper discusses the intersection of these factors, focusing on the adoption of artificial intelligence (AI)-based intrusion detection systems (IDS) and network automation as vital strategies for mitigating cybersecurity risks. The paper begins by outlining the unique cybersecurity threats faced by the maritime sector, which include data breaches, phishing attacks, and malware threats, all amplified by adverse weather and geographical isolation. In light of these challenges, the rationale for integrating AI-driven solutions into maritime operations is discussed. AI-based IDS can enhance threat detection capabilities through advanced machine learning algorithms that adapt to evolving cyber threats while minimizing false positives. Additionally, network automation can improve connectivity and data security, facilitating real-time monitoring and response to incidents. The review also addresses the critical need for collaboration between maritime and technology industries, emphasizing how partnerships can foster innovation and provide tailored solutions to the sector’s specific needs. Furthermore, the paper examines current implementations and case studies that illustrate successful applications of AI and automation in adverse maritime conditions. While recognizing the potential benefits, the review highlights the technical and operational challenges inherent in these implementations, including data integration, regulatory compliance, and cultural differences between sectors. Ultimately, this paper aims to provide a comprehensive overview of the state of maritime cybersecurity and the pivotal role of AI and automation in shaping a resilient, secure maritime future. The findings underscore the importance of ongoing research and development, collaborative efforts, and the necessity of adaptable strategies to safeguard maritime operations against the evolving landscape of cyber threats.

A novel integrated prediction method using adaptive mode decomposition, attention mechanism and deep learning for coking products prices

Accurate prediction of coking product prices is crucial for enhancing production efficiency, cost optimization, and profit maximization in smart coking facilities. To address the volatility caused by nonlinear factors such as raw material costs, substitutes, macroeconomic indicators, sudden events, policy changes, and market behaviors, we propose a novel integrated prediction method for coking product price prediction. This method combines Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) for signal decomposition, Bidirectional Encoder Representations from Transformers (BERT) for natural language processing, attention mechanisms (AT) to weigh feature importance, and an ensemble of Bidirectional Gated Recurrent Unit, Bidirectional Long Short-Term Memory, and Gated Recurrent Unit, abbreviated BBG, for robust feature extraction. We design a feature selection strategy to avoid data leakage and improve the predictive ability of the model, and describe a method to maintain textual data information integrity when combining data from different sources. Experimental results on coke and methanol datasets show that our approach retains multi-source text richness improves predictive capability, and outperforms other state-of-the-art methods, providing an effective tool for developing smart coke plants.