Security Threats Research Articles

ADVANCEMENTS IN AIRLINE SECURITY: EVALUATING MACHINE LEARNING MODELS FOR THREAT DETECTION

This study assessed the performance of four machine learning algorithms—Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Neural Network (NN)—for predicting airline security threats using a dataset of 100,000 entries with 30 features. The models were evaluated based on accuracy, precision, recall, F1-Score, and AUC-ROC. The Neural Network achieved the highest performance, with an accuracy of 88%, precision of 86%, recall of 85%, F1-Score of 85.5%, and AUC-ROC of 0.90, demonstrating superior capability in capturing complex, non-linear patterns. The Random Forest model followed, with an accuracy of 85%, precision of 83%, recall of 82%, F1-Score of 82.5%, and AUC-ROC of 0.87, offering a robust and generalizable solution. The SVM model attained an accuracy of 81%, precision of 80%, recall of 78%, F1-Score of 79%, and AUC-ROC of 0.84, showing effective binary classification but with higher computational costs. The Decision Tree model, while interpretable, had the lowest performance metrics: accuracy of 78%, precision of 76%, recall of 72%, F1-Score of 74%, and AUC-ROC of 0.79. The results indicate that Neural Networks and Random Forests are the most effective models for airline security threat detection, with Neural Networks providing the highest overall accuracy and AUC-ROC.

Machine Learning Model for Content Protection from Cyber security Threats

Cyber security is the shield that protects our digital world from unauthorized access, data breaches, and evolving threats. The former views human involvement as an extra dimension and a possible target, while the second considers it as part of the security process. However, because it centers on the ethical aspect of society as a whole, this kind of cyber security conversation has significant implications. Numerous frameworks and strategies have been proposed to handle the cyber security challenge. The ideas of cyber security are also introduced, along with information on individuals, frameworks, and protecting personal data on computers. This paper aims to emphasize the effective approach for Machine Learning Model for content from Cyber Security threats. The results show that, in this scenario, the Support Vector Machine (SVM) classifier performs better than other algorithms.

A Lightweight GCT-EEGNet for EEG-Based Individual Recognition Under Diverse Brain Conditions

A robust biometric system is essential to mitigate various security threats. Electroencephalography (EEG) brain signals present a promising alternative to other biometric traits due to their sensitivity, non-duplicability, resistance to theft, and individual-specific dynamics. However, existing EEG-based biometric systems employ deep neural networks, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), which face challenges such as high parameter complexity, limiting their practical application. Additionally, their ability to generalize across a large number of subjects remains unclear. Moreover, they have been validated on datasets collected in controlled environments, which do not accurately reflect real-world scenarios involving diverse brain conditions. To overcome these challenges, we propose a lightweight neural network model, GCT–EEGNet, which is based on the design ideas of a CNN model and incorporates an attention mechanism to pay attention to the appropriate frequency bands for extracting discriminative features relevant to the identity of a subject despite diverse brain conditions. First, a raw EEG signal is decomposed into frequency bands and then passed to GCT–EEGNet for feature extraction, which utilizes a gated channel transformation (GCT) layer to selectively emphasize informative features from the relevant frequency bands. The extracted features were used for subject recognition through a cosine similarity metric that measured the similarity between feature vectors of different EEG trials to identify individuals. The proposed method was evaluated on a large dataset comprising 263 subjects. The experimental results demonstrated that the method achieved a correct recognition rate (CRR) of 99.23% and an equal error rate (EER) of 0.0014, corroborating its robustness against different brain conditions. The proposed model maintains low parameter complexity while keeping the expressiveness of representations, even with unseen subjects.

Botnets Unveiled: A Comprehensive Survey on Evolving Threats and Defense Strategies

ABSTRACTBotnets have emerged as a significant internet security threat, comprising networks of compromised computers under the control of command and control (C&C) servers. These malevolent entities enable a range of malicious activities, from denial of service (DoS) attacks to spam distribution and phishing. Each bot operates as a malicious binary code on vulnerable hosts, granting remote control to attackers who can harness the combined processing power of these compromised hosts for synchronized, highly destructive attacks while maintaining anonymity. This survey explores botnets and their evolution, covering aspects such as their life cycles, C&C models, botnet communication protocols, detection methods, the unique environments botnets operate in, and strategies to evade detection tools. It analyzes research challenges and future directions related to botnets, with a particular focus on evasion and detection techniques, including methods like encryption and the use of covert channels for detection and the reinforcement of botnets. By reviewing existing research, the survey provides a comprehensive overview of botnets, from their origins to their evolving tactics, and evaluates how botnets evade detection and how to counteract their activities. Its primary goal is to inform the research community about the changing landscape of botnets and the challenges in combating these threats, offering guidance on addressing security concerns effectively through the highlighting of evasion and detection methods. The survey concludes by presenting future research directions, including using encryption and covert channels for detection and strategies to strengthen botnets. This aims to guide researchers in developing more robust security measures to combat botnets effectively.

Advancements and Security Challenges in the Industrial Internet of Things (IIoT)

This paper explores the significant advancements in IIoT, focusing on the increased efficiency, productivity, and innovation brought about by interconnected industrial systems. Despite the numerous benefits, the paper also delves into the critical security challenges inherent in IIoT deployments. These include vulnerabilities in network infrastructure, potential cyber- attacks targeting industrial control systems, and data privacy concerns stemming from the vast amounts of sensitive information generated. The analysis highlights the necessity for robust cybersecurity measures, including advanced encryption, intrusion detection systems, and secure communication protocols. This report offers a thorough assessment of the current status of IIoT technology by looking at both the opportunities and threats related to it. It offers insights into best practices and emerging solutions aimed at mitigating security threats, ensuring a secure and resilient industrial ecosystem. The paper concludes by discussing future directions for research and development, emphasizing the importance of a multidisciplinary approach to addressing the complex challenges and unlocking the full potential of IIoT.

Undersea cable security in the Indo‐Pacific: Enhancing the Quad’s collaborative approach

Undersea communication cables carry the bulk of global communications, intelligence, and financial transactions daily. They are increasingly threatened, and Australia, Japan, India, and the US have begun coordinating cable security activities in the Indo-Pacific as the Quad. This article investigates the Quad’s role in undersea cable communication security, and analyzes its motivations, strengths, and the challenges it faces thereby illuminating the Quad’s evolving position in international security and its role in global governance. In doing so, it evaluates the Quad’s significance in addressing emerging security threats in the Indo-Pacific, attempts to fill critical gaps in policy formulation, and contributes to theories of informality and global governance. It begins by outlining threats to undersea communication cables. Second, it details the Quad Partnership for Cable Connectivity and Resilience within the context of an informal intergovernmental organization literature. Third, it provides recommendations for policy formulation in undersea cable security and explores the Quad's potential for improving connectivity, resilience, and security. Finally, the article emphasizes the need to tailor policies to India’s unique position within the Quad and explores proactive measures like floating Quad-supported cable repair ships. These recommendations offer insights for policymakers to bolster global communication infrastructure, resilience, and provide public goods amidst multipolarity and great power rivalry.

Performance Evaluation of HTTP, DHCP and DNS Protocols of Data Packets for Vulnerabilities Using the Isolation Forest Algorithm

In the contemporary digital landscape, network security is paramount to safeguard data integrity and prevent unauthorized access as data have been structured in the network through protocols. In term of data structure IPv6 protocol has extended data size due to the robust addresses of 128 bits as compare to 32 bits of IPv4. Due to the improved security data structure of IPv6, the study focuses on identifying vulnerabilities in HTTP, DHCP, and DNS packets using the Isolation Forest algorithm approach, a machine-learning technique designed for anomaly detection. By analyzing packet lengths, size, payload and addressing, the study visualizes normal and anomalous behavior, providing insights into potential security threats in IPv4 network structure. The results highlight the effectiveness of Goodput, Quality of service and risk as essential factors in the network, using Little's theorem analysis and the Isolation Forest in detecting anomalies across these different network protocols, offering valuable implications for network security structures, due to IoT in recent networks. The time response determination in this paper explained details information on the time the treats entered the network, the duration of the vulnerabilities within the network, leading to a certain threshold, and traffic delay factors due to deviation of packet length and other social engineering activities. Sensitive multipurpose security devices are involved; MikroTik routers were configured and installed in the network under evaluation. Which the normal DPI technique was unable to effectively and efficiently addressed. ADPI principles and operations where the needed security measures adopted to detect those vulnerabilities which were eventually addressed and have contributed to recent measures of network security.

Influence of social media and the digital environment on international migration of health workforce from low- and middle-income countries post COVID-19 pandemic: a scoping review protocol

IntroductionMigration of the health workforce from low- and middle-income countries (LMCIs) is increasingly becoming a phenomenon of interest within migration governance systems. The COVID-19 pandemic aggravated health workforce shortages that...

A Survey of Joint Security-Safety for Function, Information and Human in Industry 5.0

Industry 5.0 blows the whistle on a new industrial revolution, aiming to refocus industrial development by reintegrating the human factor into the technological equation. On the eve of the revolution, the comprehensive surveys for Industry 5.0 would provide important support for future development. However, current surveys for Industry 5.0 are still in their infancy and some gaps remain. (i) Current work lacks a comprehensive technical architecture for Industry 5.0 and an in-depth analysis of the enabling technologies that will drive Industry 5.0; (ii) There is no comprehensive survey on security issues of Industry 5.0, which will directly hinder its development; (iii) As Industry 5.0 introduces people into the technological equation, then it will also further consider the broader human interests in its security equation. It is an open issue that traditional security classifications cannot summarize these new security threats in Industry 5.0. Therefore, this survey starts by reviewing the latest key enabling technologies and proposing an overall technology hierarchical structure for Industry 5.0. Second, we investigate the triad of security issues in Industry 5.0, which includes threats and countermeasures for functional safety, information security, and humanized security. Among them, we define the third security issue in Industry 5.0, humanized security, which includes safeguarding the broader interests and rights of individuals, machines, and society. Finally, we summarize future challenges and research trends. To the best of our knowledge, this is the first comprehensive overview of security in Industry 5.0, in which humanized security is defined for the first time.

Catalytic Hydrolysis of Paraoxon by Immobilized Copper(II) Complexes of 1,4,7-Triazacyclononane Derivatives.

Organophosphate neuroactive agents represent severe security threats in various scenarios, including military conflicts, terrorist activities and industrial accidents. Addressing these threats necessitates effective protective measures, with a focus on decontamination strategies. Adsorbents such as bentonite have been explored as a preliminary method for chemical warfare agent immobilization, albeit lacking chemical destruction capabilities. Chemical decontamination, on the other hand, involves converting these agents into non-toxic or less toxic forms. In this study, we investigated the hydrolytic activity of a Cu(II) complex, previously studied for phosphate ester hydrolysis, as a potential agent for chemical warfare decontamination. Specifically, we focused on a ligand featuring a thiophene anchor bound through an aliphatic spacer, which exhibited high hydrolytic activity in its Cu(II) complex form in our previous studies. Paraoxon, an efficient insecticide, was selected as a model substrate for hydrolytic studies due to its structural resemblance to specific chemical warfare agents and due to the presence of a chromogenic 4-nitrophenolate moiety. Our findings clearly show the hydrolytic activity of the studied Cu(II) complexes. Additionally, we demonstrate the immobilization of the studied complex onto a solid substrate of Amberlite XAD4 via copolymerization of its thiophene side group with dithiophene. The hydrolytic activity of the resultant material towards paraoxon was studied, indicating its potential utilization in organophosphate neuroactive agent decontamination under mild conditions and the key importance of surface adsorption of paraoxon on the polymer surface.

Unsupervised Security Threats Identification for Heterogeneous Events

As cyberattacks targeting industrial control systems continue to evolve, the development of sophisticated technologies to detect these security threats becomes increasingly essential. In addition, it is necessary to update adversarial information constantly. However, this process is complicated by the deployment of heterogeneous equipment, which increases the number of indicators and characteristics that must be analyzed by security administrators. Furthermore, security operation centers often struggle to respond promptly to adversaries because of the high number of false alerts caused by unreliable system labels. These challenges make it difficult to construct reliable detection systems. To address these issues, we propose a robust unsupervised threat-identification method. Our approach involves applying a preprocessing technique tailored to the various data types pertinent to alerts, followed by classifying unlabeled alerts using an autoencoder (AE) model. Despite the presence of numerous false positives, we verified that the proposed model could effectively distinguish between different attack types and identify their relationships with only one round of training in homogeneous and heterogeneous environments within industrial control systems. Moreover, our model can filter and display data classified as actual attacks and generate relational tables.

Cybersecurity Enhancement in IoT Wireless Sensor Networks using Machine Learning

In the context of the Internet of Things, this paper presents approaches in order to enhancing the security in Wireless Sensor Networks. It addresses the challenges arising from the lack of standardization in IoT. On the other hand, this paper proposes a machine learning and AI algorithms to detect the intrusion detection. WSNs, which are crucial for data collection across various applications, face several security threats like eavesdropping and Denial of Service (DoS) attacks. The proposed approach in this paper present accuracy rates of 0.98 for Random Forest, 0.90 for SVM, and 0.95 for KNN. It demonstrates the effectiveness of machine learning in identifying various types of attacks. This method not only improves authentication efficiency but also significantly enhances the detection and classification of diverse security threats, paving the way for substantial advancements in cybersecurity within IoT environments.

Railway security checks at the border: between intrusive security technologies and fundamental traveller rights.

European railway borders are facing a particular exposure to security threats and need a delicate balance between securitization and free movement, especially amid globalisation, the current geopolitical landscape and increased migrant flows. For example, the war in Ukraine illustrated the challenges experienced at the Eastern EU borders by the refugee migration surge in early 2022. This exploratory focuses on the European border security control process from the rail border perspective. It encompasses a descriptive synthesis of the lessons learned from the UIC Refugee Task Force as well as insights from the ongoing EU-funded Horizon Europe project ODYSSEUS (Unobtrusive Technologies for Secure and Seamless Border Crossing for Travel Facilitation). Project ODYSSEUS aims to support the security and integrity of the European space, reduce illegal movements of people and goods across EU borders, facilitate travelling for citizens all while protecting fundamental rights of travellers. The project will test a combination of multi-behavioural and General Data Protection Regulation (GDPR) compliant biometric user identity verification tools, allowing, when possible, citizens to cross EU border without any interruption or queue. Further, novel luggage and baggage checks will allow citizens' vehicles and cargos to be remotely checked at land borders to speed up the border check processes in a secure and reliable manner. The project will run three pilot tests at road, rail and water borders. In this paper, we analyse the implementation of project's technologies in the rail border crossing pilot test and discuss the implications for the actors involved in the process of railway border crossing (e.g., border authorities, railway operators and railway travellers).

Digital Image Security Analysis using Hill Cipher and AES Algorithm

In today's digital era, digital image exchange has become very common in various industries, but this also increases security risks such as counterfeiting, image manipulation, and information theft. To protect the confidentiality of information in digital images, encryption is a fairly effective method. Hill Cipher, as a classic cryptographic method, offers matrix-based encryption, while Advanced Encryption Standard (AES) is known for its high level of security and efficiency. By combining Hill Cipher and AES, encryption systems can leverage the strengths of classic and modern cryptography together, providing an additional layer of protection that strengthens the security of digital data and reduces vulnerabilities that may exist in each method separately. This approach provides a more comprehensive solution for maintaining the confidentiality of digital images in the context of evolving security threats.

Integrating Security Information and Event Management (SIEM) with Data Lakes and AI: Enhancing Threat Detection and Response

The evolving threat landscape in cybersecurity necessitates more advanced and efficient solutions for threat detection and response. Traditional Security Information and Event Management (SIEM) systems have limitations in handling large volumes of data and identifying sophisticated threats. This research explores the integration of SIEM solutions with data lakes, offering a scalable and flexible approach to managing security data. By leveraging artificial intelligence (AI) and machine learning (ML) algorithms, SIEM platforms can enhance their capabilities in real-time threat detection, automated response, and advanced analytics. This integration enables organizations to process vast amounts of structured and unstructured data from various sources, improving both the speed and accuracy of identifying security threats. The article examines the architecture, benefits, and challenges of combining SIEM with data lakes and AI, providing insights into how these technologies can collectively strengthen organizational security postures.

Factors associated with COVID-19 vaccine hesitancy and uptake among populations in the West department of Haiti.

The COVID-19 pandemic in Haiti led to increased challenges for a population concurrently dealing with natural and social disasters, poor quality health care, lack of clean running water, and inadequate housing. In 2022, the population of Haiti was 11.4 million. While half a million vaccines for COVID-19 were donated by the United States to the government of Haiti, less than 5% of the population agreed to be vaccinated. This resulted in thousands of unused doses that were diverted to other countries. The purpose of this study was to evaluate population characteristics related to vaccine uptake in order to inform future interventions to improve COVID-19 vaccine uptake as well as inform strategies to safeguard against future global health security threats. This was a mixed-methods, cross-sectional study conducted in the West Department of Haiti within peri-urban communes. Participants consisted of adults in this setting responding to a survey from June 15 to July 18, 2022. The survey assessed demographic information, household characteristics, religious beliefs, past vaccine use, and current COVID-19 vaccine status. Multivariate regression modeling was conducted to assess predictors of vaccine hesitancy. Qualitative focus group discussion participants were recruited and focus group discussions were conducted between August-September 2022 among community groups, religious leaders and health professionals to provide additional, community-level context on perceptions of the COVID-19 pandemic and vaccines. A total of 1,923 respondents completed the survey; of which a majority were male (52.7%), between the age of 18-35 (58.5%), had a medical visit within the last year (63.0%) and received the COVID-19 vaccine (46.1%). Compared to those who had been COVID-19 vaccinated, participants who had not been vaccinated were more likely to be male (57.7% vs 46.8%, p < .0001), have completed secondary education (30.5% vs 16.6%, p < .001), unemployed (20.3% vs 7.3%, p < .0001) and had a medical visit 3 or more years ago (30.2% vs 11.2%, p < .0001). Unvaccinated COVID-19 participants were also more likely to have never received any other vaccine (36.1% vs22.5%, p < .0001), have a religious leader speak out against the vaccine (20.0% vs 13.1%, p < .0001), not believe in the effectiveness of the vaccine (51.2% vs 9.1%, p < .0001) and did not trust the healthcare worker administering the vaccine (35.2% vs 3.8%, p<0.0001). These results show that targeted interventions to religious leaders and health care workers on how to engage with the community and share clearer messages around the COVID-19 vaccination may result in increased vaccine uptake. Results also shed light on how activities surrounding vaccinations can be tailored to meet client needs addressing the misinformation encountered to achieve greater health impact thereby safeguarding the population against future global health security threats.

Machine learning applications of network security enhancement: review

Machine learning (ML) is being used to improve intrusion detection mechanisms and identification in cyber security. Network data volume scaling (with the help of Machine learning) — Automated analysis and pattern recognition for large amounts of network-data, thereby detection of anomalies / potentially malicious activities that escape current rule-based techniques. By training ML models on historical data, these models can learn benign network behavior as well the anomalies in them that may result from malicious activities. The purpose of this essay/report is to begin taking a look under the hood at how ML can be used for security threat detection and analysis in-networking on an ongoing basis. This paper covers the automation of ML algorithms to enhance network security. Given the current state of electronic threats and their evolution, traditional security methods are typically insufficient. ML can analyze large volumes of data, learn patterns from it and this makes it suitable to complement network defense mechanisms. It then discusses different ML applications in network cybersecurity: intrusion detection, anomaly detection, spam and malware analysis; which the paper characterizes. It analyzes the potential, benefits and constrains of major ML methods in network security like supervised learning; unsupervised learning and reinforcement-learning. Finally, this paper represents recent progress in the use and impact of ML techniques along with case studies.The paper discusses the existing difficulties in the field such as the necessity for datasets and the vulnerability of machine learning models to adversarial attacks.T he paper also highlights avenues for exploration by focusing on developing scalable security solutions based on machine learning that are resilient and flexible.The goal of this examination is to offer both researchers and industry professionals valuable perspectives into the opportunities and obstacles linked to utilizing machine learning, in the domain of network security. ML methods have potential to improve network security by addressing the challenges posed by the increasing cyber threats that traditional security measures struggle to combat effectively over time. One key strength of ML lies in its capacity to analyze datasets and identify intricate patterns efficiently. The research paper delves into applications of ML in enhancing network security. The list covers security tools like Intrusion Detection Systems (IDS) examining malware and phishing attempts as well as anomalies in network activity and user behavior analysis (UEBA). The study explores both supervised and unsupervised learning methods. How they are used for quick threat detection and response in real time scenarios.You will find case studies and recent developments that showcase the implementation and effectiveness of these strategies.In addition the article delves into the obstacles linked to using machine learning techniques in network security including the necessity, for datasets, The paper's goal is to give an enlightening summary to scholars and practitioners about how machine learning can be applied to network security in order to provide solutions that are robust, adaptive, and scalable. To this end, it touches on several relevant aspects. One is the threat posed by adversarial attacks on the sorts of models that are likely to be used in this context. Another is the imperative, deriving from both adversarial threat and model drift, that models needed in this context be available in a form usable for continuous update. Keywords: Network Security, machine learning (ML), Intrusion Detection Systems (IDS), entity behavior analytics (UEBA).

Enhancing societal security: a multimodal deep learning approach for a public person identification and tracking system

In public spaces, threats to societal security are a major concern, and emerging technologies offer potential countermeasures. The proposed intelligent person identification system monitors and identifies individuals in public spaces using gait, face, and iris recognition. The system employs a multimodal approach for secure identification and utilises deep convolutional neural networks (DCNNs) that have been pretrained to predict individuals. For increased accuracy, the proposed system is implemented on a cloud server and integrated with citizen identification systems such as Aadhar/SSN. The performance of the system is determined by the rate of accuracy achieved when identifying individuals in a public space. The proposed multimodal secure identification system achieves a 94% accuracy rate, which is higher than that of existing public space person identification systems. Integration with citizen identification systems improves precision and provides immediate life-saving assistance to those in need. Utilising secure deep learning techniques for precise person identification, the proposed system offers a promising solution to security threats in public spaces. This research is necessary to investigate the efficacy and potential applications of the proposed system, including accident identification, theft identification, and intruder identification in public spaces.

Security Evaluation Framework for Virtualised Environments

With the expansion of cloud computing, virtual environments remain susceptible to sophisticated security threats, such as hypervisor vulnerabilities, VM escapes, inter-VM attacks, DoS attacks, and malware injections. Hence, ensuring that virtualised environments are secure, has become increasingly crucial. These threats are particularly affecting as many enterprises which have shifted to online services and remote work. This paper examines the existing frameworks which have been designed to address the existing challenges, their gaps and strengths; and proposes an enhanced security framework that can help to mitigate these threats. The framework was developed following an iterative process encompassing several stages. It has the hypervisor layer, virtual machine layer, network layer, management layer, and monitoring and response layer components. The framework aims to enhance detection capabilities, reduce response times, minimise system performance impact, and lower false positive rates while optimising resource utilisation by providing a practical and effective approach to securing virtualised infrastructures, thereby ensuring the resilience and reliability of cloud-based services.

Limits of India–US Relations: Balancing Through Strategic Autonomy and Multi-alignment

The India–US relationship has often been described as a defining partnership. But the nature of the friendship in actuality is yet to assume depth. Despite convergences at multiple levels, it falls short of a true strategic partnership or deep friendship. The India–US relationship is neither a trusted nor an assured economic or security partnership. It is often tested by the vagaries of real-time issues, especially involving other great powers. That is why, despite signing the civilian nuclear deal, foundational agreements, being members of the QUAD and Indo-Pacific Economic Forum, and with the common objective of containing the rise of China, they are unable to work closely together on myriad bilateral, international and regional issues. There are differences over matters of primary interests and the type of partnership that each wants to establish. The United States was insensitive to Indian concerns in its abrupt drawdown from Afghanistan in 2021, sub-continental security threats emanating from the China–Pakistan axis and its continued military support to Pakistan. India’s neutral stand (read in the United States as being pro-Russian) in the Ukraine crisis marked by its abstention from voting against Russia in the UN and purchase of Russian oil despite strong objections from the US-led Western camp are cases in point. We argue that India’s US policy is a case of limited balancing dictated by compulsions and lacks depth. For rising India, a continued desire for strategic autonomy and multi-alignment—the post-non-alignment foreign policy principles—guides its foreign policy orientation. For Washington, a deeper relationship would imply India abandoning its visions of strategic autonomy and joining more formally in Washington’s orbit. These core national objectives of the two countries limit the level of partnership they can forge.