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This study addresses threat scenario identification and quantitative evaluation in the context of the information security risk assessment process for airborne networks. A method integrating an improved system-theoretic process analysis (STPA) and the technique for order preference by similarity to an ideal solution (TOPSIS) is proposed. A safety control interaction structure is first established based on the system-level loss definition to identify unsafe control actions and derive STPA-Sec threat conditions. Then, the opinion aggregation method based on the weakest t-norm is introduced to address the uncertainty from insufficient expert consensus and opinion deviation. To address the intrinsic correlations among evaluation indicators, the criteria importance through intercriteria correlation (CRITIC) method is applied to determine indicator weights. These weights are subsequently incorporated into the TOPSIS framework to assess the identified threat scenarios. Comparative analysis confirms the effectiveness of the proposed approach. The results show that the improved STPA-TOPSIS method enables the reliable identification of security threats in airborne networks and supports the prioritization of threat scenarios by severity, facilitating the implementation of targeted mitigation strategies.

Though the widespread use of multi-UAV systems offers significant tactical and operational advantages, achieving efficient and secure collaborative planning remains a critical challenge in dynamic threat environments. Traditional methods struggle to balance path optimization with threat avoidance, particularly in fluctuating environments where UAVs must adapt to changing threats. To address this, an enhanced Grey Wolf Optimization (GWO) algorithm is proposed for multi-UAV collaborative planning in dynamic threat zones. Our research integrates a priori knowledge of threat zone locations, speeds, and directions with real-time data on the UAVs position relative to the threat zones to effectively manage dynamic threat zones, allowing UAVs to dynamically decide whether to navigate around or through these zones, thus significantly reducing trajectory costs. To further improve search efficiency and solution quality, strategies such as greedy initialization and K-means clustering are incorporated, enhancing the algorithms multi-objective optimization capabilities. Experimental results demonstrate that the dynamic threat zone crossing strategy significantly reduces trajectory costs compared to the traditional bypass strategy. Furthermore, the enhanced GWO algorithm outperforms both the traditional GWO and MP-GWO algorithms in terms of trajectory cost and convergence accuracy. Our approach provides novel insights and methodologies for the advancement of multi-UAV collaborative trajectory planning, while extending the applicability of the GWO algorithm in complex environments

Insider threats pose significant challenges to network security due to their destructive and covert nature, often resulting in substantial losses for enterprises. Traditional methods mainly analyze user behavior patterns or convert behaviors into time sequences for further analysis. However, existing detection methods primarily focus on identifying abnormal users or behaviors, lacking the capability to pinpoint specific threats. Additionally, these methods struggle to accurately identify long-distance dependencies in behavior sequences, frequently increasing false positives. To address these issues, we introduce a scenario-oriented insider threat detection model. This model targets three specific threat scenarios-privilege abuse, identity theft, and data leakage-by analyzing user behavior patterns, extracting detailed behavioral characteristics, and constructing behavior sequences. Firstly, this paper serializes user behavior daily and vectorizes it using one-hot encoding. Then, it introduces contextual characteristic information and reconstructs the background of abnormal behavior through behavior vectorization, providing a comprehensive description of user behavior characteristics. This approach addresses the issue of behavior isolation, thereby improving the accuracy and robustness of anomaly detection. Subsequently, a time series analysis model based on a multi-head attention mechanism is employed to analyze long-distance dependencies in behavior sequences. The multi-head attention mechanism simultaneously attends to multiple positions in the behavior sequence, capturing potential correlations between behaviors and user behavior patterns. This mechanism can analyze local information and obtain long-distance dependencies, providing depth feature representation for anomaly detection. Ultimately, we achieve the goal of classifying abnormal behavior sequences. We conduct comprehensive tests on the CERT dataset, demonstrating that our method outperforms traditional deep learning approaches (LSTM, GNN, and GCN) in detecting abnormal sequences. Compared to the best results among the baseline methods, it shows an improvement in accuracy of approximately 2% for privilege abuse, 5% for identity theft, and 2% for data leakage.

With the advantages of long-range flight and high payload capacity, large fixed-wing UAVs are often used in anti-terrorism missions, disaster surveillance, and emergency supply delivery. In the existing research, there is little research on the 3D model design of the V-tail fixed-wing UAV and 3D flight environment modeling. The study focuses on designing a comprehensive simulation environment using Gazebo and ROS, referencing existing large fixed-wing UAVs, to design a V-tail aircraft, incorporating realistic aircraft dynamics, aerodynamics, and flight controls. Additionally, we present a simulation environment modeling approach tailored for obstacle avoidance in no-fly zones, and have created a 3D flight environment in Gazebo, generating a large-scale terrain map based on the original grayscale heightmap. This terrain map is used to simulate potential mountainous terrain threats that a fixed-wing UAV might encounter during mission execution. We have also introduced wind disturbances and other specific no-fly zones. We integrated the V-tail fixed-wing aircraft model into the 3D flight environment in Gazebo and designed PID controllers to stabilize the aircraft’s flight attitude.

The future combat scenario will undergo a sea change as compared to the conventional and un-conventional warfare employed by the traditional armies and non-state actors. In such a scenario, the main battle tank which serves as a game changer during these conflicts has to face the dilemma whether its design should be either evolutionary or revolutionary. To determine the basis of selecting the right type of design based on the above, the broad parameters that define the configuration namely number of crew, weight, armament system, survivability, operating range, transportability, tactical mobility, trafficability, intelligence - surveillance - target acquisition - reconnaissance (ISTAR), system modularity and theatre of operation have been considered. Taking these parameters into account, this study evaluates both the evolutionary and revolutionary design configurations for a generation next main battle tank. Finally, from the outcome of this study it is observed that the revolutionary design approach not only fares better compared to the evolutionary approach, but also possess ease of adaptiveness as an universal combat weapon platform.

On the basis of analytical investigations, the current article has carried out an analysis of information and propaganda component of the Russian Federation hybrid aggression against Ukraine at the present historical stage. The key components of Russia’s ‘information weapon’ have been identified, and it has been emphasized that Russia’s information and propaganda campaign over the past decades has targeted both the specific states of its aggression and the entire consortium of developed democratic countries in the Western world, especially their major associations – the EU and NATO. The experience of a number of countries, in particular Latvia, Estonia, Georgia, in counteracting Russian propaganda has been investigated.

The twelfth-century 'Ludus de Antichristo' already contains a number of the threatening scenarios (Ottoman Expansion, Heresy, Antichrist, etc.) that maintain a presence in the theatre up until the sixteenth century. This essay aims to investigate which scenarios of religious threat are dominant in the dramas of the later Middle Ages and Reformation, and what kinds of dramatic and production techniques are used in order to perform these scenarios on stage. Three levels of dramatic staging may be distinguished (Latency, Presence, Topicality), and these will be analysed here on the basis of three exemplary plays published before and after the Reformation (Hans Folz, 'Der Herzog von Burgund' / 'The Jewish Messiah'; Niklaus Manuel, 'Vom Papst und seiner Priesterschaft' / 'Of the Pope and his Priesthood'; Thomas Naogeorg, 'Pammachius' / 'Pammachius').

Through interdiction models, we infer the vulnerabilities inherent in an operational system. This chapter presents four applications of interdiction modeling: (a) to delay an adversary’s development of a first nuclear weapon; (b) to understand vulnerabilities in an electric power system; (c) to locate sensors in a municipal water network; and (d) to secure a border against a nuclear smuggler. In each case, we detail and interpret the mathematical model and characterize insights gained from solving instances of the model. We point to special structures that sometimes arise in interdiction models and the associated implications for analyses. From these examples, themes emerge on how one should model, and defend against, an intelligent adversary.

Cyber situational awareness has been proven to be of value in forming a comprehensive understanding of threats and vulnerabilities within organisations, as the degree of exposure is governed by the prevailing levels of cyber-hygiene and established processes. A more accurate assessment of the security provision informs on the most vulnerable environments that necessitate more diligent management. The rapid proliferation in the automation of cyber-attacks is reducing the gap between information and operational technologies and the need to review the current levels of robustness against new sophisticated cyber-attacks, trends, technologies and mitigation countermeasures has become pressing. A deeper characterisation is also the basis with which to predict future vulnerabilities in turn guiding the most appropriate deployment technologies. Thus, refreshing established practices and the scope of the training to support the decision making of users and operators. The foundation of the training provision is the use of Cyber-Ranges (CRs) and Test-Beds (TBs), platforms/tools that help inculcate a deeper understanding of the evolution of an attack and the methodology to deploy the most impactful countermeasures to arrest breaches. In this paper, an evaluation of documented CRs and TBs platforms is evaluated. CRs and TBs are segmented by type, technology, threat scenarios, applications and the scope of attainable training. To enrich the analysis of documented CRs and TBs research and cap the study, a taxonomy is developed to provide a broader comprehension of the future of CRs and TBs. The taxonomy elaborates on the CRs/TBs dimensions, as well as, highlighting a diminishing differentiation between application areas.

The escalating complexity and impact of cyber threats require organisations to rehearse responses to cyber-attacks by routinely conducting cyber security exercises. However, the effectiveness of these exercises is limited by the exercise planners’ ability to replicate real-world scenarios in a timely manner that is, most importantly, tailored to the training audience and sector impacted. To address this issue, we propose the integration of AI-driven sectorial threat intelligence and forecasting to identify emerging and relevant threats and anticipate their impact in different industries. By incorporating such automated analysis and forecasting into the design of cyber security exercises, organisations can simulate real-world scenarios more accurately and assess their ability to respond to emerging threats. Fundamentally, our approach enhances the effectiveness of cyber security exercises by tailoring the scenarios to reflect the threats that are more relevant and imminent to the sector of the targeted organisation, thereby enhancing its preparedness for cyber attacks. To assess the efficacy of our forecasting methodology, we conducted a survey with domain experts and report their feedback and evaluation of the proposed methodology.