Early Detection Of Threats Research Articles

Cyber threats in the age of artificial intelligence: Exploiting advanced technologies and strengthening cybersecurity

The rapid advancement in artificial intelligence (AI) technologies has led to the emergence of new and complex cyber threats. These threats rely on AI capabilities to target digital systems and infrastructures with greater precision and efficiency, making them more difficult to detect and counter. AI is being used to develop automated cyber-attacks, such as malware that can adapt and learn from its digital environment. Conversely, AI is also employed to enhance cyber security by enabling early threat detection, analyzing suspicious behaviors, and providing rapid responses to hacking incidents. Based on the above, the research idea we intend to present at this article on how to achieve a balance between leveraging AI technologies to protect digital systems and the growing risks associated with using these technologies to develop more sophisticated cyber-attacks. Research Methodologies: The study requires employing both the descriptive-analytical and inferential methodologies.

Towards Securing Smart Homes: A Systematic Literature Review of Malware Detection Techniques and Recommended Prevention Approach

The exponential growth of the Internet of Things (IoT) sector has resulted in a surge of interconnected gadgets in smart households, thus exposing them to new cyber-attack susceptibilities. This systematic literature review investigates machine learning methodologies for detecting malware in smart homes, with a specific emphasis on identifying common threats such as denial-of-service attacks, phishing efforts, and zero-day vulnerabilities. By examining 56 publications published from 2019 to 2023, this analysis uncovers that users are the weakest link and that there is a possibility of attackers disrupting home automation systems, stealing confidential information, or causing physical harm. Machine learning approaches, namely, deep learning and ensemble approaches, are emerging as effective tools for detecting malware. In addition, this analysis highlights prevention techniques, such as early threat detection systems, intrusion detection systems, and robust authentication procedures, as crucial measures for improving smart home security. This study offers significant insights for academics and practitioners aiming to protect smart home settings from growing cybersecurity threats by summarizing the existing knowledge.

The evolutive role of shoot apical meristems in the adaptation of angiosperms to life at sea and the jump to potential environmental biotechnology applications

Seagrasses have adapted to a submerged lifestyle in seawater through a complex set of evolutionary processes. However, they show sensitivity to increases in natural salinity levels such as those commonly found in discharges of desalination plants, which have exponentially grown due to water scarcity in highly populated temperate areas, such as the Mediterranean basin. This study assessed the effects of brine-derived hypersalinity on the Mediterranean seagrass Posidonia oceanica, focusing on the metabolic responses of shoot apical meristems (SAMs). Although most physiological and genetic studies have used leaves, SAMs are more directly correlated with plant survival and might be more responsive to salinity stress. The experiments were: a controlled mesocosm of more than six practical salinity units (psu) over natural levels using either artificial salts or desalination brine and field transplantation experiments comparing two sites following the dilution plume of the brine (+5 and + 2 psu) with control. Hydrogen peroxide (H2O2), thiobarbituric acid reactive substances (TBARS), and ascorbate were measured to determine oxidative stress and damage, as well as relative expression of genes related to osmotic regulation and oxidative responses. Overall, relative expression of genes related to osmotic regulation (SOS1, SOS3, AKT 2/3) and oxidative stress (STRK1, CAT, MnSOD, FeSOD, APX, GR) was higher in SAMs at higher brine exposures, indicating a more active metabolic response in this organ. Similarly, reactive oxygen species (ROS) production and lipid peroxidation were higher closer to brine discharge and total ascorbate lower, indicating a correlative response with stressor intensity. These findings confirm that SAMs play an essential role in P. oceanica hypersalinity responses and its adaptation to life in the marine environment. Finally, the use of P. oceanica SAMs in this study is highly recommended for an early detection of threats caused by desalination brines that may cause physiological damage and meadow regression.

Enhancing security and risk management with predictive analytics: A proactive approach

and interconnected world. This paper explores the transformative potential of predictive analytics in enhancing security and risk management by shifting from reactive to proactive strategies. Examining the theoretical foundations and application areas of predictive analytics, the paper highlights how organizations can anticipate and mitigate risks across various domains, including cybersecurity, fraud detection, and supply chain management. The benefits of predictive analytics, such as early threat detection and optimized resource allocation, are discussed alongside data quality, privacy, and model interpretability challenges. Additionally, emerging trends, such as artificial intelligence, real-time data analytics, and blockchain technology, are key drivers shaping the future of predictive analytics in risk management. The paper concludes by emphasizing organizations' need to integrate predictive analytics into their risk management frameworks to enhance resilience and ensure long-term success in a rapidly evolving risk landscape.

Determining the Efficacy of Machine Learning Strategies in Quelling Cyber Security Threats: Evidence from Selected Literatures

The alarming security threats in the internet world continually raise critical concerns among individuals, organizations and governments alike. The sophistication of cyber-attacks makes it imperative for a paradigm shift from traditional approaches and measures for quelling the attacks to modern sophisticated, digital and strategic ones, such as those involving machine learning and other technologies of artificial intelligence (AI). This study is aimed at examining machine learning (ML) strategies for effective cyber security. ML involves using algorithms and statistical models to enable computers learn from and make decisions or predictions based on data. The study relied on secondary data, which were subjected to a systematic review. The results of its thematic and qualitative analyses prove that majority of the literatures allude to the fact that the maximal performance abilities and tactics of the ML constitute its strategies for quelling cyber security. These include its: early detection of threats that are tackled before they cause damages; ability to analyze huge quantity of data quickly and accurately; and processing of datasets in real-time. The study argues that the noted abilities and tactics constitute ML strategies for quelling cyber security, regardless of its challenges like data quality, security vulnerabilities and possible incidences of bias. The study concludes that ML can indeed be used to detect and respond to threats in real-time, ascertain patterns of malicious behavior, and improve on internet security, which thereby prove it to be a viable tool for quelling cyber security.

Implementasi Strategi Pertahanan Laut Nusantara Dalam Rangka Deteksi Dini Pelanggaran Di Pesisir Garut Jawa Barat Guna Mendukung Pertahanan Negara Di Laut

Indonesia, with its vast maritime territory and abundant water resources, requires substantial efforts to safeguard defense and security. The effective defense strategy of the Indonesian Navy (TNI AL) involves strategically placing naval bases throughout the entire region, demonstrating a deterrent effect to potential adversaries and affirming the presence of TNI AL in the waters of the Republic of Indonesia. The aim of this research is to analyze the implementation of the Maritime Defense Strategy in early detection of threats along the coast of Garut, West Java. This qualitative research adopts a phenomenological approach, utilizing interview, observation, and documentation methods. The findings indicate that the placement of Binpotmar personnel in Cikelet Garut under the jurisdiction of Posal Pangandaran plays a crucial role in responding to the dynamics of local maritime issues. The flexibility and responsibility of Binpotmar demonstrate vital adaptability in responding to incidents at the border. Functioning as the forefront of intelligence and executing maritime operations, the presence of Binpotmar is a strategic step to enhance self-reliance in carrying out operational tasks. The research also emphasizes the optimal utilization of Cikelet Garut's potential, including the well-maintained Dutch legacy docks, through infrastructural improvements and advanced technology. The naval facility in the area can effectively respond to maritime threats, fulfill the 5R criteria in the Southern Region of Java Island, and create a deterrent effect against adversaries. The importance of collaboration with the Garut Regency Government through forkopimda is highlighted as a crucial consideration for TNI AL. Overall, the research underscores the significance of a comprehensive and collaborative approach in maintaining maritime defense and security in the region.

Deep Learning for Smart Grid Intrusion Detection: A Hybrid CNN-LSTM-Based Model

As digital technology becomes more deeply embedded in power systems, protecting the communication networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3) represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities. Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network (CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to train and test our model. The results of our experiments show that our CNN-LSTM method is much better at finding smart grid intrusions than other deep learning algorithms used for classification. In addition, our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection accuracy rate of 99.50%.

A Bayesian network-based tool for crisis classification in piracy or armed robbery incidents on passenger ships

Piracy and armed robbery continue to pose significant security threats to the shipping industry. This paper presents a real-time threat assessment and crisis classification tool for piracy or armed robbery incidents. The tool is part of a crisis classification module that addresses various categories of security threats. This module is currently being developed as part of the EU-funded research project ISOLA, which aims to introduce an intelligent security superintendence ecosystem. The ecosystem is designed to complement the existing ship security processes and measures applied onboard passenger ships. The tool operates by providing real-time threat classification and subsequent warnings by analysing data collected from the ship’s legacy systems and installed sensors with the utilisation of Bayesian probabilistic techniques, particularly Bayesian Networks. The BN model developed for this purpose is thoroughly examined, and its validation is presented through indicative case studies involving piracy and armed robbery. The main objective is to improve situational awareness, enhance vigilance and early threat detection, and support the decision-making process for the Master and crew, especially under time-sensitive circumstances and stressful conditions.

Enhancing Forest Security through Advanced Surveillance Applications

Forests established through afforestation are one of the most precious natural resources, especially in harsh and desert-biased conditions. Trees are often exposed to various threats that need to be addressed. Some of the threats are igniting fires, illegal lumberjacking, hunting, using, and crossing prohibited areas, etc. This article delves into the combination of advanced technologies, such as radars, thermal imaging, remote sensing, artificial intelligence, and biomass monitoring systems, in the field of forestry and natural resource security. By examining the parametric assurance technologies described in this paper, the potentials of real-time monitoring, early detection of threats, and rapid response capabilities are examined, which significantly improves the efficiency of forest protection efforts. This article deals with the presentation of advanced algorithms that include radar, thermal cameras, and artificial intelligence, which enable the automatic identification and classification of potential threats with a false alarm rate (FAR) as low as possible. The article presents a systemic solution that optimizes the answer for a parametric security system that is required to work in a complex environment with multiple triggers that can cause false alarms. In addition to this, a presented system is required to be easy to assemble and have the ability to integrate into natural areas and serve as a vulnerable aid in nature as much as possible. In conclusion, this study highlights the transformative potential of security applications in improving forest and natural reserve security while taking into account the complexity of the environment.

Методика антикризисного управления региональными экономическими системами в условиях санкций недружественных государств

The article presents the results of developing a methodology for anti-crisis management of regional economic systems under the conditions of sanctions from unfriendly states. The preventive diagnostics of threats to the economic security of the region in the context of sanctions from unfriendly states within the framework of a bifurcation model of development is presented schematically. An algorithm for the operation of a system for early detection of threats to the economic security of the region from sanctions of unfriendly states has been formed.

Real-Time Incident Response and Remediation-A Review Paper

The real-time correlation of IRR to effective cyber defense plays a crucial role. Regular answers usually take more time to be provided and permit devastations to occur, thus becoming more severe. The swiftest IRR of automation and real-time analytics is characterized by early threat detection, instant response, and quick incident resolution. The very needed capabilities include machine detection of abnormalities and threats, quick investigation through correlation and AI, and fast response through preinstalled playbooks. SIEM, EDR, and SOAR operationalization make active IRR possible in real-time. The positive aspects of introducing such a solution are pronounced - early threat containment, speedy recovery, and higher efficiency levels for the security team. On the other hand, actual-time IRR has limitations: false positives, integration of faiths, people and procedures dependence, and effectiveness against advanced threats. However, inundation by the real-time IRR signifies a cybersecurity revolution. The real-time IRR is an opportunity for innovation in analytics and automation that partially or transforms the enterprise security system. However, the dissemination faces technical and coordination-related barriers. The real-time IRR capability is a clear sign of progress in eliminating the cyber resiliency gap, but there is still room for improvement to achieve the best.

Year-Round Testing of Coastal Waters of the Gulf of Gdańsk in the Baltic Sea for Detecting Oil in a Seawater Column Using the Fluorescence Method

Progressive climate changes and the increase in the occurrence of extreme weather phenomena indicate the need to take action to mitigate the negative effects of climate change. One of the main factors affecting climate change is the state of waters that transport heat. Oil pollution present in the water contributes to the absorption of radiation and physico-chemical changes in the sea, which has an impact on the marine ecosystem. This indicates the need to develop methods for effective oil spill detection. This study aimed to improve the methods of early detection of threats related to oil spills in the marine environment, especially when the source of oil may be invisible in the depths of the sea. Therefore, the method based on the fluorometric index is proposed, and its effectiveness for oil detection in seawater is studied. The study has answered the question of how biological activity during a whole year influences the effectiveness of oil detection by the proposed fluorometric index method. Therefore, for the calculation of the fluorometric index, the changes in the seawater fluorescence spectrum in the ultraviolet range were determined, which occurred under the influence of diffusion of some oil components in the sea. The principle of detection of oil contaminants based on the excitation-emission fluorescence spectrum is described. For the measurements, natural seawater samples used in the laboratory were exposed to a mixture of crude oil and oils commonly found in navigation. The effectiveness of oil substance detection using the fluorometric index in the biologically productive and unproductive seasons was analyzed for seawater in the vicinity of Gdynia and Gdansk ports in Poland in northern Europe. The results of excitation-emission spectra and fluorometric index indicate that the changes in the biological activity during the year do not affect the detectability of oil present in seawater for the considered oil-to-water ratio. Summarize the sensitivity analysis of the method indicates the possibility of detection of oil contamination regardless of the season. The obtained results pave the way for the construction of an underwater device to detect oil in the vicinity of such a detector.

ENSURING THE SUSTAINABILITY OF PUBLIC FINANCES IN THE CONDITIONS OF SOCIAL AND ECONOMIC INSTABILITY

The purpose of the article is research and assessment of the stability of public finances as a component of the financial system of the state in conditions of instability. Early detection of threats to the stability of economic development and timely prevention of crisis situations is one of the most important long-term goals of the economic policy of the government of any country. The theoretical aspects of the public finance system are considered, namely: their essence, functions and structure, as well as the modern public finance system of Ukraine, its structure and component links. Public finance is a key component of the financial system of Ukraine, which owns more than half of all financial resources and includes various financial institutions that implement the functions of the state. Ensuring public finances is an institutional process, which consists in the implementation of structural transformations of their components to ensure the effective accumulation and distribution of public financial resources in accordance with the priorities of the country’s socio-economic development. The practical aspects of the application of modern tools for assessing the sustainability of public finances in the conditions of Ukraine are revealed. The evolution of approaches to defining the concept of “sustainability” is presented. The multi-functionality of public finances determines their extensive structure, the main component of which is the State budget. The article analyzes the implementation of the State Budget of Ukraine for nine years (2013-2021), as a key component of public finances and one of the criteria for their sustainability, and determines the level of the budget deficit and its share in GDP. It is shown that the budget indicators almost correspond to the European criteria for the sustainability of the public finance system. A manifestation of the instability of the state’s financial position is, as a rule, an unlimited increase in the relative value of the state debt (no more than 60% of GDP). An inevitable consequence of the instability of public finances is hyperinflation and/or the state’s refusal to service its obligations (default). Based on calculations, it was found that the level of the state debt met these requirements only in 2019 and 2021. The need to maintain the ratio of public debt to GDP at a constant level to ensure financial stability is argued by the fact that the amount of public debt, which is constantly growing, may soon reach a certain limit, which will be perceived as the solvency threshold.

An Integrated Fusion Engine for Early Threat Detection Demonstrated in Public-Space Trials.

Counter terrorism is a huge challenge for public spaces. Therefore, it is essential to support early detection of threats, such as weapons or explosives. An integrated fusion engine was developed for the management of a plurality of sensors to detect threats without disrupting the flow of commuters. The system improves security of soft targets (such as airports, undergrounds and railway stations) by providing security operators with real-time information of the threat combined with image and position data of each person passing the monitored area. This paper describes the results of the fusion engine in a public-space trial in a metro station in Rome. The system consists of 2D-video tracking, person re-identification, 3D-video tracking, and command and control (C&C) formulating two co-existing data pipelines: one for visualization on smart glasses and another for hand-over to another sensor. Over multiple days, 586 commuters participated in the trial. The results of the trial show overall accuracy scores of 97.4% and 97.6% for the visualization and hand-over pipelines, respectively, and each component reached high accuracy values (2D Video = 98.0%, Re-identification = 100.0%, 3D Video = 99.7% and C&C = 99.5%).

Anomaly Detection in Network Traffic using Machine Learning for Early Threat Detection

Due to advances in network technologies, the amount of people using networks is rising rapidly. This has resulted in a large amount of transmission information being generated and moved across the network. However, this data is vulnerable to attacks and intrusions. To prevent network intrusions, security measures must be implemented, which can detect anomalies and identify potential threats. Network security researchers and labs have done extensive research in network security. The purpose of this study was to perform a noninvasive inspection to give a large general mechanism on recent advances in abnormality detection. The study reviewed recent research published in the past five years, which examined new technologies and potential future opportunities in anomaly detection. The literature review focused specifically on anomaly detection systems used in network traffic. This included various applications such as Wireless Sensor Networks (WSN), Internet of Things (IoT), High Performance Computing, Industrial Control Systems (ICS), and Software Defined Networking (SDN) environments. The review concludes by highlighting several unresolved issues that need to be addressed in order to improve anomaly detection systems.

Visual Monitoring Strategies of Sentinels in a Cooperative Breeder.

Vigilance is important for early detection of threats. Previous studies have focused on the allocation of time to vigilance but neglected how animals monitor their surroundings during vigilance. Where animals look and how long each look lasts can affect the quality of visual monitoring and thus the ability to detect threats during vigilance. We examined visual monitoring strategies in the Florida scrub-jay (Aphelocoma coerulescens), a cooperative breeder with sentinel behaviour. Sentinels in this species make head turns from vantage points to detect the arrival of predators and intruding neighbours. We found that sentinels initiated head turns at regular intervals and also returned their gaze to areas previously monitored at regular intervals, which is predicted when predators and intruders rely on surprise rather than stealth to approach. Sentinels made head turns in several directions, but often more frequently on one side of the body than the other, which was not predicted for regular vigilance. Average look duration during sentinel bouts was shorter in smaller groups and in juveniles. We argue that shorter looks are beneficial to increase visual coverage in more threatening situations. Our study highlights how visual monitoring strategies during vigilance reflect the risk posed by predators and intruders.

A Feature-driven Method for Automating the Assessment of OSINT Cyber Threat Sources

Global malware campaigns and large-scale data breaches show how everyday life can be impacted when the defensive measures fail to protect computer systems from cyber threats. Understanding the threat landscape and the adversaries’ attack tactics to perform it represent key factors for enabling an efficient defense against threats over the time. Of particular importance is the acquisition of timely and accurate information from threats intelligence sources available on the web which can provide additional intelligence on emerging threats even before they can be observed as actual attacks. Currently, specific indicators of compromise (e.g. IP addresses, domains, hashsums of malicious files) are shared in a semi-automated and structured way via so-called threat feeds. Unfortunately, current systems have to deal with the trade-off between the timeliness of such an alert (i.e. warning at the first mention of a threat) and the need to wait for verification by other sources (i.e. warning after multiple sources have verified the threat). In addition, due to the increasing number of open sources, it is challenging to find the right balance between feasibility and costs in order to identify a relatively small subset of valuable sources. In this paper, a method to automate the assessment of cyber threat intelligence sources and predict a relevance score for each source is proposed. Specifically, a model based on meta-data and word embedding is defined and experimented by training regression models to predict the relevance score of sources on Twitter. The results evaluation show that the assigned score allows to reduce the waiting time for intelligence verification, on the basis of its relevance, thus improving the time advantage of early threat detection.

Numerical Fluid Dynamics Simulation for Drones’ Chemical Detection

The risk associated with chemical, biological, radiological, nuclear, and explosive (CBRNe) threats in the last two decades has grown as a result of easier access to hazardous materials and agents, potentially increasing the chance for dangerous events. Consequently, early detection of a threat following a CBRNe event is a mandatory requirement for the safety and security of human operators involved in the management of the emergency. Drones are nowadays one of the most advanced and versatile tools available, and they have proven to be successfully used in many different application fields. The use of drones equipped with inexpensive and selective detectors could be both a solution to improve the early detection of threats and, at the same time, a solution for human operators to prevent dangerous situations. To maximize the drone’s capability of detecting dangerous volatile substances, fluid dynamics numerical simulations may be used to understand the optimal configuration of the detectors positioned on the drone. This study serves as a first step to investigate how the fluid dynamics of the drone propeller flow and the different sensors position on-board could affect the conditioning and acquisition of data. The first consequence of this approach may lead to optimizing the position of the detectors on the drone based not only on the specific technology of the sensor, but also on the type of chemical agent dispersed in the environment, eventually allowing to define a technological solution to enhance the detection process and ensure the safety and security of first responders.

The impact of aversive context on early threat detection in trauma exposed individuals and associations with post-traumatic stress symptoms.

Introduction:Prolonged attentional bias to threat (AB) is associated with posttraumatic stress symptoms (PTSS). However, it is unclear whether this relationship extends to early threat detection (elicited by masked stimuli) and/or varies if AB is measured during an aversive context.Methods:Two trauma-exposed samples of either intervention-seekers (N = 50) or community members (N = 98) completed a masked dot-probe task to measure early AB to angry faces in safe vs. aversive contexts (i.e., during threat of aversive noises).Results:Linear mixed effects models showed that an aversive context increased the orienting responses in both samples; however, PTSS did not moderate these effects in either sample.Limitations:Sample size and heterogeneity of trauma-type may have impacted effect of PTSS on AB.Conclusion:These results highlight the importance of assessing AB in varying contexts and examining generalizability across populations. Given prior research, the results also suggest that increased AB in PTSS may only be present for later attentional processes rather than early threat detection, at least with behavioral methods.

Early detection of wildlife morbidity and mortality through an event-based surveillance system.

The ability to rapidly detect and respond to wildlife morbidity and mortality events is critical for reducing threats to wildlife populations. Surveillance systems that use pre-diagnostic clinical data can contribute to the early detection of wildlife morbidities caused by a multitude of threats, including disease and anthropogenic disturbances. Here, we demonstrate proof of concept for use of a wildlife disease surveillance system, the ‘Wildlife Morbidity and Mortality Event Alert System’, that integrates pre-diagnostic clinical data in near real-time from a network of wildlife rehabilitation organizations, for early and enhanced detection of unusual wildlife morbidity and mortality events. The system classifies clinical pre-diagnostic data into relevant clinical classifications based on a natural language processing algorithm, generating alerts when more than the expected number of cases is recorded across the rehabilitation network. We demonstrated the effectiveness and efficiency of the system in alerting to events associated with both common and emerging diseases. Tapping into this readily available unconventional general surveillance data stream offers added value to existing wildlife disease surveillance programmes through a relatively efficient, low-cost strategy for the early detection of threats.