Unknown Threats Research Articles

Adaptive organizational network response in a crisis: The case of five European airports during theCOVID‐19 pandemic

AbstractThe COVID‐19 pandemic posed unprecedented challenges to the joint organizational response of private and public and here especially public health organizations. This is particularly true for airports as central connectors of global travel and trade. For five European airports, we analyzed the interorganizational response based on input from 66 of the 87 different airport partners, using two fictitious scenarios derived from public health practice. We applied organizational design theory and quantitative (network) analysis to show how the four fundamental problems of organizing have been tackled at airports, i.e., task distribution, task allocation, rewards, and information provision. This article shows how the response to COVID‐19 in the airport setting needs to be solved within broad and flexible public administrative networks. The thorough understanding of organizational network responses in emergency management following from this article supports future preparedness efforts to deal with complex known and unknown public health threats.

Enhancing Security and Reliability in Industrial IoT Networks through Machine Learning

Industrial Internet of Things (IIoT) networks are very important to modern manufacturing because they allow processes to be monitored, controlled, and improved in real time. IoT systems are linked to each other, which makes them vulnerable to cyberattacks, system breakdowns, and communication problems. This makes them less reliable and secure. To deal with these problems, we need advanced technologies that can find problems before they happen, reduce risks, and keep processes running smoothly. In this paper, we suggest a new way to make IIoT networks safer and more reliable by using machine learning methods together. Our system uses machine learning techniques to look at network traffic trends, spot strange behaviors, and find possible security holes in real time, using the huge amounts of data that IIoT devices produce. Our system can successfully find and stop a wide range of cyberattacks, such as hacking attempts, malware infections, and denial-of-service (DoS) attacks, because it is always learning from past data and changing to new threats. We also use machine learning models to predict when systems might fail or perform worse, so we can do preventative maintenance and keep downtime to a minimum.To prove that our method worked, we did a lot of tests using real-world IIoT datasets and checked how well our system worked by looking at how accurate it was at finding things, how often it gave false positives, and how fast it responded. These results show that our approach based on machine learning makes IIoT networks much safer and more reliable than standard rule-based approaches. In addition, our framework is strong against new and unknown threats, which shows that it could be used in a wide range of business settings.Overall, the paper research shows that machine learning has a lot of potential to make IIoT networks more reliable and to make sure that processes in industrial settings run smoothly and safely

Analysis of modern intelligent methods for protecting critical information infrastructure

Critical information infrastructure (CII), including the financial sector, plays a key role in ensuring the sustainable functioning of economic systems and the financial stability of States. However, the growing digitalization of the financial industry and the introduction of innovative technologies are opening up new attack vectors for attackers. Modern cyber attacks are becoming more sophisticated, and traditional defenses are proving ineffective against new, previously unknown threats. There is an urgent need for more flexible and intelligent cybersecurity systems. Thus, the subject of the study is modern intelligent methods and technologies for protecting critical information infrastructure (CII) from cyber attacks. The object of the research is methods and means of ensuring the protection of critical information infrastructure using artificial intelligence and machine learning technologies. The methodological basis of this study is a comprehensive analysis of the scientific literature on the use of intelligent methods and technologies to protect critical information infrastructure. During the review and critical analysis of relevant scientific publications, key problems and unresolved tasks requiring further scientific research and practical developments in this subject area were identified. This methodological approach allowed us to form a holistic view of the current state and prospects for the development of intelligent cybersecurity tools for critical financial systems, as well as to identify priority areas for further research. The main directions of scientific novelty of this research are: 1. A detailed review of promising approaches based on artificial intelligence and machine learning technologies to ensure effective protection of CII organizations from modern complex cyber attacks. 2. Identification and analysis of a number of key scientific and technical problems that need to be solved to increase reliability, interpretability and trust in intelligent cybersecurity systems, including issues of robustness to attacks, active online learning, federated and differential private data processing. 3. Identification of promising areas for further research and development in the field of application of specialized methods of secure and trusted AI to protect critical financial infrastructure. Thus, this research makes a significant contribution to the development of scientific and methodological apparatus and practical solutions for the use of intelligent methods to ensure cybersecurity.

Software security: Threats, solutions and challenges

Software security is of great concern as computers have entered almost all walks of life and people at large have become dependent on technology for not only entertainment and communication but for performing tasks involving money and a lot of stake. Software security not only involves securing the software but also user data and communication media. This paper states the several types of security threats that exist since the time networking has evolved, namely, malware, Trojans, viruses, denial of service attacks, and many more. This paper reviews several measures to address these threats. It includes logging, anti-malware, network security methods, and encryption methods. It has been identified that a lot of work has been done to deal with security threats, and it is not only limited to the protection of software but also extends to the protection of data and networks. The existing methods make extensive use of artificial intelligence, and it is identified that there is a need to develop a model that is able to identify known as well as unknown threats. There is a huge scope for research in this area.

Secure and privacy-preserving intrusion detection in wireless sensor networks: Federated learning with SCNN-Bi-LSTM for enhanced reliability

As the digital landscape expands rapidly due to technological advancements, cybersecurity concerns have become more prevalent. Intrusion Detection Systems (IDSs), which are crucial for identifying unusual network traffic indicative of malicious activity, have become a necessity. These systems can be either hardware or software-based. However, traditional IDS models often fail to adequately protect data privacy and detect complex, unique breaches, particularly within Wireless Sensor Networks (WSNs). To address these limitations, this paper proposes a novel Stacked Convolutional Neural Network and Bidirectional Long Short Term Memory (SCNN-Bi-LSTM) model for intrusion detection in WSNs. This model leverages Federated Learning (FL) to enhance intrusion detection performance and safeguard privacy. The FL-based SCNN-Bi-LSTM model is unique in its approach, allowing multiple sensor nodes to collaboratively train a central global model without revealing private data, thereby alleviating privacy concerns. The deep learning methodology of the SCNN-Bi-LSTM model effectively identifies sophisticated and previously unknown cyber threats by meticulously examining both local and temporal linkages in network patterns. The model has been specifically designed to detect and categorize different types of Denial of Service (DoS) attacks using specialized WSN-DS and CIC-IDS-2017 datasets. Compared to traditional Artificial Deep Neural Network (ADNN) models, our proposed FL-SCNN-Bi-LSTM model demonstrated superior detection rates for complex and unknown attacks, significantly improving IDS performance. The model achieved a notable classification accuracy of approximately 99.9% precision and recall on both datasets, substantially reducing false positives and negatives. Our research underscores the potential of federated learning and deep learning in enhancing the security and privacy of WSNs. The proposed FL-SCNN-Bi-LSTM architecture not only facilitates the identification of complex cyber threats but also exemplifies how deep learning techniques can be employed to bolster intrusion detection systems while preserving user data privacy.

Nucleus accumbens dopamine release reflects the selective nature of pair bonds

In monogamous species, prosocial behaviors directed toward partners are dramatically different from those directed toward unknown individuals and potential threats. Dopamine release in the nucleus accumbens has a well-established role in social reward and motivation, but how this mechanism may be engaged to drive the highly divergent social behaviors directed at a partner or unfamiliar conspecific remains unknown. Using monogamous prairie voles, we first employed receptor pharmacology in partner preference and social operant tasks to show that dopamine is critical for the appetitive drive for social interaction but not for low-effort, unconditioned consummatory behaviors. We then leveraged the subsecond temporal resolution of the fluorescent biosensor, GRABDA, to ask whether differential dopamine release might distinguish between partner and novel social access and interaction. We found that partner seeking, anticipation, and interaction resulted in more accumbal dopamine release than the same events directed toward a novel vole. Further, partner-associated dopamine release decreased after prolonged partner separation. Our results are consistent with a model in which dopamine signaling plays a prominent role in the appetitive aspects of social interactions. Within this framework, differences in partner- and novel-associated dopamine release reflect the selective nature of pair bonds and may drive the partner- and novel-directed social behaviors that reinforce and cement bonds over time. This provides a potential mechanism by which highly conserved reward systems can enable selective, species-appropriate social behaviors.

Cloud Computing Security via Intelligent Intrusion Detection Mechanisms

New challenges of data breaches, unauthorized access, and insider threats have emerged with the rise of Cloud Computing. Attackers find cloud systems attractive due to their common infrastructure. To combat these security threats, strong security procedures must be integrated. An intrusion detection system (IDS) is a vital instrument for safeguarding networks and cloud environments. It tracks system activity and network traffic. This study compares the efficacy of various machine learning models for intrusion detection. The Random-Forest-Classifier model performed the best with a test accuracy of 99.88% in recognizing known and unknown threats. Furthermore, various benefits have been identified when contrasting this intelligent intrusion detection system with conventional rule-based and signature-based methods. The use of machine learning in intrusion detection systems offers enhanced security measures and improved threat detection capabilities, highlighting the importance of leveraging advanced technologies to secure cloud environments. This research illuminates the necessity to employ cutting-edge techniques to protect cloud computing systems from malicious actions in the era of cybersecurity. More than that, it stresses the necessity to invest in enrichment mechanisms in machine learning-based intrusion detection systems to stay abreast with the evolving security problems.

Data-Driven Cyber Threat Intelligence: Leveraging Behavioral Analytics for Proactive Defense Mechanisms

As the cyber threat landscape becomes increasingly sophisticated, traditional defense mechanisms often fall short in anticipating and mitigating advanced attacks. The paper explores the critical role of behavioral analytics in transforming cyber threat intelligence (CTI) into a proactive defense strategy. This study underscores the importance of analyzing user behavior patterns, network activity, and system interactions to detect anomalies that may indicate potential threats. By leveraging large datasets and advanced analytical techniques, organizations can move beyond reactive cybersecurity measures, instead anticipating and preventing attacks before they fully manifest. The integration of behavioral analytics with CTI provides a comprehensive understanding of both external threats and internal vulnerabilities, enabling the development of dynamic defense mechanisms that adapt to the evolving threat environment. The research also discusses the benefits of automated threat intelligence platforms, which use machine learning algorithms to continuously analyze behavioral data and refine threat detection models in real-time. This approach reduces the reliance on manual analysis, enhances the speed and accuracy of threat detection, and minimizes false positives. Moreover, the study highlights the importance of cross-sector collaboration and data sharing in building a robust CTI framework that leverages collective intelligence to combat cyber threats. Case studies of successful implementations in various industries demonstrate the practical applications and efficacy of behavioral analytics in enhancing cybersecurity resilience. The findings suggest that organizations that adopt data-driven, behavior-focused CTI are better equipped to defend against both known and unknown threats, ensuring a more secure digital environment. This proactive approach to cybersecurity not only strengthens organizational defenses but also contributes to the broader goal of creating a safer cyber ecosystem by identifying and neutralizing threats at their inception. Keywords: Data-Driven, Cyber Threat, Intelligence, Bahavioral Analytics, Proactive Defense Mechanism.

Small Microplastics: A yet Unknown Threat in the Svalbard (Norway) Region

The Arctic Ocean is undergoing several transformations because of global climate change. Small microplastics (SMPs) or nanoplastics (NPs) carried by marine aerosols may settle in the land ice and be released to the waters, produced following its melting. As sea ice extent reduces and shipping and fishing activities increase, microplastics (MPs) may enter the region following ocean and maritime transports, with implications on Arctic biota, human health, and socioeconomic issues related to the exploitation of marine resources. First analyses on amphipods collected in Ny-Ålesund confirmed the presence of SMPs. Nevertheless, the threat posed by SMPs/NPs to polar biota and regional human health is not fully understood. This article addresses this issue and the need for organisms as potential bioindicators of plastic pollution, which is currently being carried out in the Svalbard region under the framework of the MICROTRACER project funded by the Italian Arctic Research Program (PRA, Call 2021). The outputs of this research are expected to contribute to deepening the current knowledge of SMPs in Svalbard, providing new insights on their occurrence, distribution, and transfer through the marine trophic web, to realize effective control and regulatory framework measures to implement an integrated multidisciplinary approach for monitoring and to reduce MPs pollution in this fragile polar environment.

Comparison of Data Fluctuations that Lead to Cyber Security Attacks: A Difference between Surface, Deep and Dark Net

The term "darknet" refers to the address space on the internet that is not being used, and users do not anticipate that this area will interact with their machines. Darknet is a source of cyber intelligence. In order to develop network security, it is necessary to conduct studies of the many dangers that comprise the network. In this research, we offer brand new machine learning classifiers that go by the name stacking ensemble learning. Their purpose is to evaluate and categorize darknet traffic. This novel approach employs predictions created by three different base learning techniques in order to deal with the issues relating to darknet attacks. The software was validated using a dataset that had more than 141,000 records and was derived from the CIC-Darknet 2020 database. The findings of the experiment indicated that the classifiers used in the investigation were able to easily differentiate between benign and malignant traffic. The classifiers have the ability to efficiently recognize known as well as unknown threats with a high degree of precision and accuracy that is greater than 99% in the training and 97% in the testing phases, with increments ranging from 4 to 64% based on the algorithms that are currently in use. As a consequence of this, the suggested system will become more reliable and accurate as more data is collected. Additionally, in comparison to other AI algorithms already available, the suggested system has the lowest standard deviation.

INTRUSION DETECTION SYSTEM USING CLUSTERING ALGORITHMS OF NEURAL NETWORKS

This research paper explores the application of clustering algorithms in neural networks for enhancing Intrusion Detection Systems (IDS). Intrusion Detection Systems are critical in safeguarding information systems from unauthorized access, misuse, or damage. The dynamic nature of cyber threats necessitates advanced approaches for detection and prevention. Neural networks, with their ability to learn and adapt, offer significant potential in identifying and classifying network intrusions. This paper reviews various neural network architectures and clustering algorithms, their integration in IDS, and evaluates their effectiveness in detecting known and unknown threats.

A hybrid methodology for anomaly detection in Cyber–Physical Systems

The rapid adoption of Industry 4.0 has seen Information Technology (IT) networks increasingly merged with Operational Technology (OT) networks, which have traditionally been isolated on air-gapped and fully trusted networks. This increased attack surface has resulted in compromises of Cyber–Physical Systems (CPS) with significant economic and life safety consequences. This paper proposes a hybrid model of anomaly detection of security threats to CPS by blending the signature-based and threshold-based Intrusion Detection Systems (IDS) commonly used in IT networks, with a Machine Learning (ML) model designed to detect behaviour-based anomalies in OT networks. This hybrid model achieves more rapid detection of known threats through signature-based and threshold-based detection strategies, and more accurate detection of unknown threats via behaviour-based anomaly detection using ML algorithms.

A Deep CNN Framework for UAV Intrusion Detection in Intelligent Systems

Unmanned Ariel Vehicle (UAV) s are dealing with several safety and protection issues including internal hardware/software and potential attacks. In addition, detecting UAV anomalies will be a crucial responsibility to defend against hostile enemies and prevent accidents. In this research, we present a UAV and an Automatic Dependent (AD) system using surveillance and Machine Learning (ML) algorithms to analyze data from their detectors in real-time. Proposed Improved Region based Convolutional Neural Network (IRCNN) model used to generate and acquire the characteristics of untreated sensor information and characteristics to facilitate AD. The proposed model creating an Inertial Measurement Unit (IMU) & UAV sensors dataset using cyber security simulation system and Active Learning (AL) identifies aggressions based on the least probable interrogation method. This proposed model enables the identification to efficiently improve the occurrences of unexplained aggressions discovered of IRCNN at reduced labeling cost. A thorough trial showed that IRCNN-AL is effective at detecting unknown threats with frequency improvements of between 9% and 30% on comparison approaches. The AL methodology presented with as few as 1% of a labeled unexpected aggressions.

A grounded theory-based qualitative approach for examining local implementation of public health policies during crises

BackgroundCOVID-19′s emergence questions the agility of health policy deployment in a context of urgency. This exceptional pandemic offers a unique Implementation Science study opportunity. It reveals how actors adapt, coordinate, and mitigate an unknown global threat to safeguard populations from an initially mysterious virus. Limited research has explored how involved players act and adapt their practices to fulfil health protection missions during a global health crisis. Bridging the gap between public policy expectations and achievements requires a methodology for stakeholder identification and implementation practice description. ObjectiveFocusing on COVID-19 management in France's second-largest region, we investigate ministerial recommendation implementation and the emergence of new links, coordination modes, and practices. MethodsDue to the novel subject, we adopted grounded theory. Initial documentary data collection identifies stakeholders for subsequent interviews. Open-ended coding of collected discourse enables content analysis. ResultsFindings reveal a crisis-driven re-evaluation of stakeholder relationships. This research identifies three levels of implementation of health policies at the local level (administrative, organizational and operational) and reveals different types of coordination specific to each of these levels. Our results provide insights on how to better coordinate and implement healthcare policies in a period of crisis. Recommendations include real-life simulations of large-scale crises. ConclusionOur work establishes a methodological foundation for analysing coordination dynamics. Future research could compare these findings with other unpredictable health emergencies, such as episiotomic veterinary health crises.•The first step of the method is to analyse the guidelines of health policy implementation during the Covid-19 crisis and to identify the main stakeholders in charge of the local health policy implementation.•The second step consists of interviewing these stakeholders using a co-constructed sample and structural coding of their speech to reveal the forms of coordination between stakeholders.

NeuPot: A Neural Network-Based Honeypot for Detecting Cyber Threats in Industrial Control Systems

Honeypots have proven to be an effective defense method for industrial control systems (ICSs). However, as attacker skills become more sophisticated, it becomes increasingly difficult to develop honeypots that can effectively recognize and respond to such attacks. In this paper, we propose a neural network-based ICS honeypot scheme named Neupot that improves security from two aspects: honeypot interaction and cyber threats detection capability. Neupot can respond to attacker requests depending on a specific industrial scenario without constant communication with the ICS and detect malicious traffic. To create this honeypot scheme, a new seq2seq time-series forecast model guided by Huber loss is designed to simulate the long-term changes in actual ICS physical processes. Second, a Modbus honeypot framework is created to react to changes in these ICS physical processes in their interactions with attackers and to capture various cyber threats against the ICS. Further, a novel loss function for industrial protocol-level malicious traffic detection is devised to identify known and unknown threats. According to our experiments, the proposed honeypot scheme is highly effective and outperforms state-of-the-art schemes in terms of interactivity and in detecting cyber threats.

Developing Transportation Resilience Adaptively to Climate Change

Although definitions of resilience are wide-ranging, they ultimately aim to preserve functionality and performance during and after anticipated and unanticipated disruptions. Common themes for transportation system resilience include preparedness, robustness, response, and recovery. Adaptation is increasingly considered an important resilience ability for deeply uncertain conditions, and transformation an essential ability for sustainability. This paper discusses three factors that contribute to the development of resilience adaptively: (1) development of adaptive capabilities in an organization and its infrastructure systems to face known and unknown threats; (2) application of dynamic, adaptive, and robustness approaches in deeply uncertain conditions; and (3) mitigation of factors that cause or exacerbate unwanted disruptions to system function and performance—together with risk-based approaches to manage specific threats with well-understood likelihoods and consequences of occurrence. These factors enable an entity to adapt under deeply uncertain conditions—developing resilience as conditions change—and to adapt to curb the causes of disruptions. The benefits of developing resilience adaptively are to: enable the development of both threats-based and opportunities-based resilience; address deep uncertainties where it is impossible to quantify the chances and consequences of disruptions; and transition from merely managing symptoms to addressing the factors that cause, influence, or perpetuate them. These concepts are applied to develop an adaptive resilience improvement process for transportation agencies. This paper is potentially useful to practitioners interested in developing resilience adaptively and to researchers interested in risk-, adaptive and mitigation-based approaches to address known and unknown threats, for short- and long-term resilience gains.

Cyber-Resilience Evaluation Methods Focusing on Response Time to Cyber Infringement

Though multilevel, in-depth information protection systems are employed to defend against unknown cyber threats, vulnerabilities in these systems are frequently exploited by cyberattacks. As a result, it becomes challenging to comprehensively counter these attacks within a constrained time frame. When a cyberattack is detected, immediate measures are necessary to prevent widespread damage and maintain the system’s regular functioning. Possessing sustainable cyber-resilience capabilities, which can promptly restore the system to its pre-attack state, is crucial. In this paper, a cyber-defense activity optimization procedure is introduced, drawing on the failure recovery time of the information system, aiming to enhance both the response and recovery phases of cyber resilience. Through training, the response time for various types of cyberattack was determined. Notably, a decrease in response time by 17.8% compared to the baseline was observed. By optimizing response times and integrating them with sustainable cyber-resilience assessment activities, a robust framework is presented for evaluating an organization’s overall cyber-defense stance. Research on the cyber combat capability index, dissecting the response time for each distinct cyber-defense activity, is planned for future endeavors.

The Impact of COVID-19 Pandemic on The Construction Projects in Indonesia: Identifying the Risks from the Stakeholders’ Perspective

Covid-19, which is declared as a pandemic by the World Health Organisation (WHO) on March 11 2020 has significant impacts in many sectors around the world. Nevertheless, there is a lack of research which analyses the impact of Covid-19 in the construction projects risks in developing countries. Given the lack of research related to the construction projects risks due to Covid-19, the overall aim of the study is to identify and classify the risk in a construction project as a result of Covid-19 Pandemic in Indonesia. The methodology which is used in this study is by implementing the questionnaire approach with the targets from the related construction industry stakeholders. The findings identified five risks which have the highest possibility to occur in a construction project as a result of Covid-19, in which the highest risk is delayed funding on construction project design (Score: 0,2523) while the lowest risk is unknown threat or vandalism from the neighborhood gangster (score: 0,0136). Further study will focus on the way in which we could develop a new framework model for risk management and preventive measures of these risks adjusted to Covid-19 impact.

Beyond detection: Uncovering unknown threats

Threat management is essential for ensuring an organisation’s security, but traditional strategies often only address known threats, leaving the organisation vulnerable to unknown threats. To be well equipped against advanced cyberattacks, a proactive approach beyond detection that uncovers unknown and emerging threats is necessary. This paper proposes a comprehensive approach to threat management involving the partnership between the threat detection, threat hunting, threat intelligence and threat exposure teams. Various approaches for hunting unknown threats are explored, including simulation, forensics, threat modelling, incident pivoting, deception, and a process to hunt once and automate. Insights detailed in this paper will also help organisations make informed decisions on resources and practices around threat hunting. The proposed strategy emphasises the need for a proactive and iterative approach to threat management, allowing organisations to stay ahead of adversaries and be prepared for unknown threats.

EFEKTIVITAS TERAPI MUSIK INSTRUMENTAL TERHADAP TINGKAT KECEMASAN MAHASISWA FAKULTAS TEKNIK UNIVERSITAS RIAU KEPULAUAN DALAM MENYUSUN SKRIPSI

Background: Anxiety is a diffuse and vague concern caused by an unknown or non-existent threat. Anxiety in students can arise in the preparation of undergraduate thesis due to internal and external factors. The purpose of this study was to analyze the effectiveness of instrumental music therapy on the anxiety level of engineering students at the University of Riau Islands in preparing undergraduate thesis in 2023
 Methods: This research method uses a Quasi-Experimental methodology with a Non Equivalent Control Group Design. The sampling technique in this study was purposive sampling technique with the Slovin formula and obtained a total sample of 68 respondents. This data was collected using the Zung Self Rating Anxiety Scale questionnaire and then processed using the Wilcoxon Signed Rank Test, which is to measure the level of significance of the difference between two groups of paired data on an ordinal scales.
 Results: The results of the analysis with the Wilcoxon Signed Rank Test showed a significant difference between the control group and the experimental group, namely p = 0.000 (p <0.05). In the mean rank column of the experimental group is higher (16.50) than the control group (4.50).
 Conclusion: Based on the results of the research there is the effectiveness of music therapy on the anxiety level of Engineering Faculty students at the University of Riau Islands in preparing their undergraduate thesis in 2023.