Defense Resource Allocation Research Articles

AI in the Sky: Developing Real-Time UAV Recognition Systems to Enhance Military Security

In an era where Unmanned Aerial Vehicles (UAVs) have become crucial in military surveillance and operations, the need for real-time and accurate UAV recognition is increasingly critical. The widespread use of UAVs presents various security threats, requiring systems that can differentiate between UAVs and benign objects, such as birds. This study conducts a comparative analysis of advanced machine learning models to address the challenge of aerial classification in diverse environmental conditions without system redesign. Large datasets were used to train and validate models, including Neural Networks, Support Vector Machines, ensemble methods, and Random Forest Gradient Boosting Machines. These models were evaluated based on accuracy and computational efficiency, key factors for real-time application. The results indicate that Neural Networks provide the best performance, demonstrating high accuracy in distinguishing UAVs from birds. The findings emphasize that Neural Networks have significant potential to enhance operational security and improve the allocation of defense resources. Overall, this research highlights the effectiveness of machine learning in real-time UAV recognition and advocates for the integration of Neural Networks into military defense systems to strengthen decision-making and security operations. Regular updates to these models are recommended to keep pace with advancements in UAV technology, including more agile and stealthier designs

Defensive resource allocation in terrorism conflict management based on graph model with relative preferences

In real-world counterterrorism activities, it is usually difficult for the defender and the attacker to accurately know the private information of the each other such as valuations of targets. Instead, players may only know the relative preference on the target valuations from the adversary. In the conflict analysis, graph model is a powerful tool for dealing with relative preferences. This paper studies the defensive resource allocation in terrorism conflict management with incomplete information by establishing a graph model. To solve the model, we divide the conflict states into two types and discuss the conditions under which these two types of states are at equilibrium. Furthermore, we study how the defender should optimally allocate the resource to achieve two goals: (i) achieving a certain Nash equilibrium state desired by the defender; and (ii) minimizing the total loss from an attack in equilibrium. Subsequently, we conduct several numerical analyses: (i) analyzing the effects of both players' investment effectiveness on the optimal defense loss; (ii) comparing our model's results with those obtained using three classical decision methods, revealing that the defense loss in our model is lower; and (iii) presenting a case study to illustrate the applicability of the proposed model. This paper provides novel insights on how to efficiently allocate defensive resource when the defender and attacker know only the relative preference of the adversary on target valuations.

Modeling defensive resource allocation in multilayered systems under probabilistic and strategic risks.

Confronting the continuing risk of an attack, security systems have adopted target-hardening strategies through the allocation of security measures. Most previous work on defensive resource allocation considers the security system as a monolithic architecture. However, systems such as schools are typically characterized by multiple layers, where each layer is interconnected to help prevent single points of failure. In this paper, we study the defensive resource allocation problem in a multilayered system. We develop two new resource allocation models accounting for probabilistic and strategic risks, and provide analytical solutions and illustrative examples. We use real data for school shootings to illustrate the performance of the models, where the optimal investment strategies and sensitivity analysis are presented. We show that the defender would invest more in defending outer layers over inner layers in the face of probabilistic risks. While countering strategic risks, the defender would split resources in each layer to make the attacker feel indifferent between any individual layer. This paper provides new insights on resource allocation in layered systems to better enhance the overall security of thesystem.

Robustness evaluation method and defense strategy of electricity-heat integrated energy system

Even though electricity-heat integrated energy systems (IESs) can improve energy utilization, the faults generated by extreme events can induce more complex and wider impacts. Therefore, it is urgent to study the effective defense strategies associated with electricity-heat IES. Considering the theory of complex networks, a dynamic model was established in this paper which comprehensively considered the actual operating characteristics and cascading failure process of the electricity-heat IES. The structural and functional robustness of the electricity-heat IES after failure was evaluated by link survivability, capacity survivability and power survivability. According to the post-disaster system robustness evaluation results and system characteristics without adopting the defense strategy, the critical links of the system were identified. Also, seven defense strategies were formulated based on the cascading failure mechanism of the electricity-heat IES. The testing network coupled with an improved IEEE118-node power grid and 23-node heat supply network was used to verify the effectiveness of the proposed defense strategy and to provide a theoretical basis for the resilience enhancement strategy and defense resource allocation of the electricity-heat IES.

Transcriptome-wide N6-methyladenosine profiling reveals growth-defense trade-offs in the response of rice to brown planthopper (Nilaparvata lugens) infestation.

N6-Methyladenosine (m6A) is a common messenger RNA (mRNA) modification that affects various physiological processes in stress responses. However, the role of m6A modifications in plants responses to herbivore stress remains unclear. Here, we found that an infestation of brown planthopper (Nilaparvata lugens) female adults enhanced the resistance of rice to N. lugens. The m6A methylome analysis of N. lugens-infested and uninfested rice samples was performed to explore the interaction between rice and N. lugens. The m6A methylation mainly occurred in genes that were actively expressed in rice following N. lugens infestation, while an analysis of the whole-genomic mRNA distribution of m6A showed that N. lugens infestation caused an overall decrease in the number of m6A methylation sites across the chromosomes. The m6A methylation of genes involved in the m6A modification machinery and several defense-related phytohormones (jasmonic acid and salicylic acid) pathways was increased in N. lugens-infested rice compared to that in uninfested rice. In contrast, m6A modification levels of growth-related phytohormone (auxin and gibberellin) biosynthesis-related genes were significantly attenuated during N. lugens infestation, accompanied by the down-regulated expression of these transcripts, indicating that rice growth was restricted during N. lugens attack to rapidly optimize resource allocation for plant defense. Integrative analysis of the differential patterns of m6A methylation and the corresponding transcripts showed a positive correlation between m6A methylation and transcriptional regulation. The m6A modification is an important strategy for regulating the expression of genes involved in rice defense and growth during rice-N. lugens interactions. These findings provide new ideas for formulating strategies to control herbivorous pests. © 2024 Society of Chemical Industry.

Knowledge-guided Evolutionary Optimization for Large-Scale Air Defense Resource Allocation

Knowledge-guided Evolutionary Optimization for Large-Scale Air Defense Resource Allocation

Optimal Defense Resource Allocation Considering Nonlinear Attack Cost in Power Systems

Optimal Defense Resource Allocation Considering Nonlinear Attack Cost in Power Systems

A Risk Assessment Model for Distribution Systems Considering Optimal Defense Resource Allocation

A Risk Assessment Model for Distribution Systems Considering Optimal Defense Resource Allocation

Game-theoretical Model for Dynamic Defense Resource Allocation in Cyber-physical Power Systems Under Distributed Denial of Service Attacks

Game-theoretical Model for Dynamic Defense Resource Allocation in Cyber-physical Power Systems Under Distributed Denial of Service Attacks

Optimal defense resource allocation against cyber-attacks in distributed generation systems

The deployment of advanced information and communication technologies necessitates considering new security threats, such as distributed denial of service attacks and malware, which can fault power generators and feeders and exacerbate power outages in distributed generation systems (DGS). Existing cyber-security studies fail to validate the attacker–defender game model between operators and hackers or provide a DGS model that accounts for realistic characteristics and operations. Furthermore, current game models may be infeasible for large-scale systems and are not robust against uncertainties owing to the use of metaheuristic algorithms. To overcome these gaps, this study quantified the result of a game using the contest success function and estimated the parameters of this function based on real-world evidence: the dataset of cyber crime incidents from Advisen, US. The DGS management was optimized using the power flow model considering the scenario-based uncertainty stemming from cyber-attacks. A three-stage attack+defend–defend–attack framework is proposed to optimize attack–defense resource allocation using the cooperative game and [Formula: see text]-subgradient method. The results for IEEE 4, 13, 34, 123 and 342 test node feeders show that the proposed framework is applicable to large-scale systems and robust to various types of cyber-attacks. The proposed model and algorithms further enhance the DGS performance under uncertainties by protecting the entire grid or only critical nodes according to the defenders’ objectives.

Security defense strategy algorithm for Internet of Things based on deep reinforcement learning

Currently, important privacy data of the Internet of Things (IoT) face extremely high risks of leakage. Attackers persistently engage in continuous attacks on terminal devices to obtain private data of crucial importance. Although significant progress has been made in recent years in deep reinforcement learning defense strategies, most defense methods still face problems such as low defense resource allocation efficiency and insufficient defense coordination capabilities. To solve the above problems, this paper constructs a novel adversarial security scenario and proposes a security game model that integrates defense resource allocation and patrol inspection. Regarding the above game model, this paper designs a deep reinforcement learning algorithm named SDSA to calculate its security defense strategy. SDSA calculates the allocation strategy of the best patrolling strategy that is most suitable for the defender by searching the policy on a multi-dimensional discrete action space, and enables multiple defense agents to cooperate efficiently by training a multi-intelligent Dueling Double Deep Q-Network (D3QN) with prioritized experience replay. Finally, the experimental results show that the SDSA-learned security defense strategy can provide a feasible and effective security protection strategy for defenders against attacks compared to the MADDPG and OptGradFP methods.

A game theory based optimal allocation strategy for defense resources of smart grid under cyber-attack

A deep integration of cyber and physical systems will lead to severe risk and security challenges in the new power system. To address this problem, an optimal defense resource allocation method based on game theory is proposed to against potential cyber-attacks in smart grid proactively. Based on this method, a two-layer game model is designed to optimal resource allocation. One layer is consisted by an evolutionary game between defense nodes and attackers, and the anther layer is consisted by a noncooperative game between multiple defense nodes. Further up, the offensive and defensive evolution results for all scenarios have been discussed, then the solution to resource allocation problem among multiple nodes is formulated. Different from previous work, characteristics of the bounded rationality of the attacker are considered according to the indexes of integrity, usability and confidentiality. Meanwhile, quantum response equalization is introduced to quantify player gains. Finally, specific algorithms are employed to demonstrate the feasibility and effectiveness of the method proposed in this paper.

Starlet: Network defense resource allocation with multi-armed bandits for cloud-edge crowd sensing in IoT

The cloud platform has limited defense resources to fully protect the edge servers used to process crowd sensing data in Internet of Things. To guarantee the network's overall security, we present a network defense resource allocation with multi-armed bandits to maximize the network's overall benefit. Firstly, we propose the method for dynamic setting of node defense resource thresholds to obtain the defender (attacker) benefit function of edge servers (nodes) and distribution. Secondly, we design a defense resource sharing mechanism for neighboring nodes to obtain the defense capability of nodes. Subsequently, we use the decomposability and Lipschitz continuity of the defender's total expected utility to reduce the difference between the utility's discrete and continuous arms and analyze the difference theoretically. Finally, experimental results show that the method maximizes the defender's total expected utility and reduces the difference between the discrete and continuous arms of the utility.

Industrial Cyber–Physical System Defense Resource Allocation Using Distributed Anomaly Detection

An industrial cyber–physical system (ICPS) tightly integrating both physical processes and information and communication technologies (ICTs) leads to increasing cyberspace threats and attacks for the critical electrical infrastructure. With the limited defense resources availability, the efficient threat perception and mitigation of potential impacts of cyber attacks are essential to enhance the ICPS operational security. This article proposes an optimal defense resource allocation solution to prioritize the ICPS asset protection based on the distributed network traffic anomaly detection. The traffic anomalies and attack paths can be timely detected simultaneously over multiple security zones of the electrical infrastructure through local computing devices. The defense resource allocation is formulated as a multiobjective optimization (MOO) problem considering the tradeoff among the asset vulnerability, cost, and criticality, and solved by the Pareto optimal solution generation approach. The proposed solution is extensively evaluated using a realistic electrical CPS (ECPS) testbed for a range of cyber-attack scenarios. The numerical results confirm the effectiveness of the proposed distributed anomaly detection model and defense resource allocation strategy for varying defense resource availabilities.

Novel improved chaotic elephant herding optimization algorithm-based optimal defense resource allocation in cyber-physical systems

Novel improved chaotic elephant herding optimization algorithm-based optimal defense resource allocation in cyber-physical systems

Game Attack–Defense Graph Approach for Modeling and Analysis of Cyberattacks and Defenses in Local Metering System

We propose a game attack–defense graph (GADG) approach that integrates the attack–defense graph and the game theory to model and analyze cyberattacks and defenses in the local metering system (LMS). Different from previous studies concentrating on static analyses of cybervulnerabilities, the GADG method considers correlations among these vulnerabilities. Besides, to avoid the uncertainty brought by unilateral analysis, we introduce the game theory for the interaction analysis between the attacker and the defender. The mixed-strategy Nash equilibrium that they finally reach can serve as the input for the inference of system states. We also propose two eliminating algorithms to reduce the complexity of solving mixed-strategy Nash equilibrium on large-scale LMS. In the case study, our GADG model has been conducted on a real LMS, and the results prove its efficiency. This research aims to assist the power company in optimizing the allocation of limited defensive resources, especially specific in attacking steps. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article is motivated by the practical demand of protecting the cybersecurity of the local metering system (LMS), which is a critical subsystem of the smart grid and has been widely deployed to collect and transmit time-stamped information. Different from typical research works on local metering cybersecurity, which focuses on the analysis and identification of cybervulnerabilities, in this study, real experiments considering the interactive scenarios between the attacker and the defender are conducted on the LMS. Based on this, we developed the attack graph to clearly illustrate various cyberattack paths and their corresponding effects. We then applied the game theory model on the attack graph to obtain the optimal attack and defense strategy under each attack scenario. Based on this, the defender, i.e., power company, can optimally allocate limited defense resources among various attacking steps according to our model. The proposed model and method are demonstrated on one exemplar LMS used by our industrial partner China Southern Grid (CSG). Given the generalizability of the proposed model and method, they can be applied to other similar cyber–physical systems.

Risk assessment and defense resource allocation of cyber-physical distribution system under denial of service attack

Risk assessment and defense resource allocation of cyber-physical distribution system under denial of service attack

Tabu search with simulated annealing for solving a location–protection–disruption in hub network

Nowadays, designing a reliable hub network has become a critical issue in the process of transporting goods from an origin to a destination. Due to intentional disasters, both location and protection of hub play a key role in satisfying the demands and ensuring network reliability. This study tries to model the impact of the number of hubs opened, allocation of defensive resources, and the risk of disruptions on the configuration of the hub network. This model aims is to minimize the total transportation cost via the primary and backup hubs subject to the installed hubs, allocation of defensive resources, and the risk of disruptions. The formulation with the location of hubs, allocation of protection budget, flow routing between two nodes in origin–destination via the primary and backup hubs, and hub failure probabilities have not been remarked in the literature.As the problem is an NP-hard, the performance of some metaheuristic algorithms called tabu search (TS), simulated annealing (SA), variable neighborhood search (VNS), imperialist competitive algorithm (ICA) and genetic algorithm (GA) is investigated to solve instances of problem with 50 nodes and 5, 10, and 15 hubs. Computational results show that the SA and TS algorithms are superior to existing metaheuristic methods to the novel problem based on solution accuracy and computational time, respectively. Additionally, this study presents a fast and robust hybrid approach that combines the advantages of TS and SA. The proposed algorithm has been successfully used to solve a large number of instances of this problem via sensitivity analysis and instances from the Turkish Postal data set. Several experimental results indicate the applicability of the new model and the advantage of the new hybrid method compared to other metaheuristic algorithms concerning decision and execution time.

Optimal allocation of defensive resources to defend urban power networks against different types of attackers

As an important part of urban lifeline networks, the urban power network is threatened by multiple types of intentional attackers, such as criminals, terrorists, illegal employees, etc. Each type of attackers differs remarkably in purposes, strategies and valuations of targets. In order to improve the defense capability of urban power network under the threat of different types of attackers, this study proposed an optimal allocation method of defensive resources based on Bayesian game model to reduce the expected loss of the defender. In the case study of IEEE 30-bus network, the reliability and applicability of the resource optimization allocation method are verified through the comparison of the expected loss under different strategy combinations. It is proved that the Bayesian Nash equilibrium defense strategy is the best choice for the defender when there are different types of attackers.

Defense resource allocation in road dangerous goods transportation network: A Self-Contained Girvan-Newman Algorithm and Mean Variance Model combined approach

A Self-Contained Girvan-Newman Algorithm and Mean Variance Model combined approach is proposed to allocate the defense resource in road dangerous goods transportation network. Firstly, the weighted physical network without direction and its weighted service network with direction are established. The Self-Contained Girvan-Newman Algorithm is applied to separate the whole weighted physical network into several communities. Next, based on the service network, the covariance matrix of each separated community is established, the Mean Variance Model is used to allocate the defense resource for each community, which focuses on selecting the option with the lowest probability of loss caused by the dangerous goods transportation accident/risk. The case study is conducted by using the road network and the dangerous goods transportation volume of Dalian, China as the background. When the whole network is separated into 6 communities, the defense resource capability in the whole network is the best. The Power Function Allocation Model (PFAM) is applied as the comparison approach, the overall rescue capability of the network is defined and applied to evaluate the defense resource allocation schemes. The results show that, the approach proposed in this paper has better effectiveness than PFAM, especially when the whole network is separated into 6 communities.