Security Resource Allocation Research Articles

Minimizing IoT Security Deployment Costs using the Dominating Set Approach

The rise of the Internet of Things (IoT) has generated significant interest by enabling connectivity across various objects, ranging from the smallest devices to large-scale systems. Despite its benefits, IoT poses considerable security challenges due to the many interconnected devices that collect and transmit sensitive data across networks. Therefore, ensuring robust data protection and preventing unauthorized access or misuse are essential concerns. To address this issue, strategically placing security services within IoT networks is vital for safeguarding both devices and data. One promising strategy for optimizing this placement is the use of the dominating set concept derived from graph theory, which helps in the efficient allocation of security resources. This study presents an IoT network as a simple weighted graph, considering device capabilities while focusing on adopting the dominating set concept to enhance the placement of security services in IoT networks. To achieve this, an enhanced greedy heuristic is proposed for efficiently generating the dominating set. The effectiveness and performance of the proposed approach are evaluated through a comparative analysis combined with existing methods in the recent literature.

Unveiling Human Factors: Aligning Facets of Cybersecurity Leadership, Insider Threats, and Arsonist Attributes to Reduce Cyber Risk

This qualitative study is a systematic literature review (draws on literature primarily published within the last five years) addresses a comprehensive approach to a crucial but often overlooked aspect of cybersecurity: the human factors underlying insider threats. Attention is focused on the so-called “organizational arsonists” – individuals who willfully seek to adversely impact the organization by inducing anarchy aligned with their own motivations, insiders who purposefully damage their companies using digital methods, someone intentionally causing mayhem within a company, which can be criminal in cyber environments. The purpose of the research is to identify how cybersecurity leadership can effectively detect and mitigate the risks associated with insiders, particularly those exhibiting arsonist-like behaviors. Review uncovering that organizational arsonists can escalate cybersecurity risks substantially, with insider incidents costing organizations an average of $16.2 million per incident. These incidents now represent a persistent challenge, increasing in frequency by 68% over the past year according to the 2022 Insider Threat Report. The findings highlight the necessity of leadership strategies that preemptively recognize and neutralize potential insider threats to improve organizational resilience and security posture. This approach not only informs current cybersecurity practices but also aids in the development of targeted policies and refined regulatory measures. By integrating insights from psychology, criminology, and cybersecurity, the study provides a comprehensive understanding of the human elements influencing insider threats, essential for enhancing both academic knowledge and practical applications in risk management. The results showed a parallel between the motivations of arsonists who set physical fires to the characteristics and motivations of insider threats who exploit organizational vulnerabilities. The impact of this research can be helpful in assisting cybersecurity professionals, leaders who strategize against cyber threats, and risk managers and analysts who understand and mitigate human factors and insider threats. Leaders and executives may use these insights to improve security resource allocation and culture. Policymakers and regulators may use the study’s results to create more nuanced cybersecurity legislation, while academics and students in related disciplines can use it for future research.

The Role of Artificial Intelligence in Enhancing Criminal Justice: Challenges and Opportunities in the Libyan Context

This research examines the role of artificial intelligence (AI) in enhancing criminal justice systems, focusing on advanced applications such as predictive crime analysis, facial recognition, and forensic evidence analysis. Predictive analysis relies on sophisticated algorithms that use historical data to anticipate crime locations and timings, thereby improving the allocation of security resources and reducing crime rates. However, the use of these technologies raises ethical concerns related to privacy protection and algorithmic bias that may affect ethnic minorities. The study employs a qualitative methodology, including literature review and expert interviews, highlighting the situation in Libya, where the application of AI is limited due to inadequate infrastructure. The findings indicate that while AI can improve the efficiency of criminal investigations, it is crucial to establish robust legal and ethical frameworks to ensure responsible use. Therefore, cooperation between the government and civil society is essential to maximize the benefits of these technologies while minimizing their risks. Keywords: artificial intelligence, criminal problems, predictive analysis, privacy violations, organized crime.

Resource allocation algorithm for downlink secure transmission in wireless EH cooperative networks with idle relay-assisted jamming

In wireless Energy Harvesting (EH) cooperative networks, we investigate the problem of secure energy-saving resource allocation for downlink physical layer security transmission. Initially, we establish a model for a multi-relay cooperative network incorporating wireless energy harvesting, spectrum sharing, and system power constraints, focusing on physical layer security transmission in the presence of eavesdropping nodes. In this model, the source node transmits signals while injecting Artificial Noise (AN) to mitigate eavesdropping risks, and an idle relay can act as a jamming node to assist in this process. Based on this model, we formulate an optimization problem for maximizing system secure harvesting energy efficiency, this problem integrates constraints on total power, bandwidth, and AN allocation. We proceed by conducting a mathematical analysis of the optimization problem, deriving optimal solutions for secure energy-saving resource allocation, this includes strategies for power allocation at the source and relay nodes, bandwidth allocation among relays, and power splitting for the energy harvesting node. Thus, we propose a secure resource allocation algorithm designed to maximize secure harvesting energy efficiency. Finally, We validate the correctness of the theoretical derivation through Monte Carlo simulations, discussing the impact of parameters such as legitimate channel gain, power splitting factor, and the number of relays on secure harvesting energy efficiency of the system. The simulation results show that the proposed secure energy-saving resource allocation algorithm effectively enhances the security performance of the system.

A miscellaneous model for comprehensive attractiveness assessment of chemical plants: Integrating chemical hazards, domino effects and plant traits

The growing threat of intentional attacks on major petrochemical plants underscores the urgent need for robust security risk assessments (SRA). Such assessments are widely acknowledged as crucial for risk mitigation and are currently a focus of active research. This study introduces a methodology for assessing the attractiveness of the petrochemical sector to intentional attacks, considering chemical hazards, domino effects, and plant traits. Within this framework, we have identified 12 risk factors that capture the multifaceted nature of targeting incentives, relating to the enterprise, location, population, and other key features. We have developed a hybrid decision-making model that analyzes the interrelationships among these factors and determines their weights, integrating the Decision-Making Trial and Evaluation Laboratory (DEMATEL) and the Analytic Network Process (ANP). The applicability of this method is demonstrated through a detailed case study. The results indicate that our method can effectively rank the attractiveness of chemical plants within a specific region, serving as a preliminary screening phase in the SRA. The findings of the attractiveness assessment may assist in identifying potential targets among numerous existing plants and support the allocation of security resources and the formulation of defensive measures.

Secure resource allocation against colluding eavesdropping in a user-centric cell-free massive multiple-input multiple-output system

Secure resource allocation against colluding eavesdropping in a user-centric cell-free massive multiple-input multiple-output system

An efficient localization-based secure resource allocation using e-fso with ss-ddnn-based cm-lsgeo techniques

An efficient localization-based secure resource allocation using e-fso with ss-ddnn-based cm-lsgeo techniques

The Impact of Network Design Interventions on the Security of Interdependent Systems

We study the problem of defending a Cyber-Physical System (CPS) consisting of interdependent components with heterogeneous sensitivity to investments. In addition to the optimal allocation of limited security resources, we analyze the impact of an orthogonal set of defense strategies in the form of network design interventions in the CPS to protect it against the attacker. We first propose an algorithm to simplify the CPS attack graph to an equivalent form which reduces the computational requirements for characterizing the defender's optimal security investments. We then evaluate four types of design interventions in the network in the form of adding nodes in the attack graph, interpreted as introducing additional safeguards, introducing structural redundancies, introducing functional redundancies, and introducing new functionalities. We identify scenarios in which interventions that strengthen internal components of the CPS may be more beneficial than traditional approaches such as perimeter defense. We showcase our proposed approach in two practical use cases: a remote attack on an industrial CPS and a remote attack on an automotive system. We highlight how our results closely match recommendations made by security organizations and discuss the implications of our findings for CPS design.

A Hybrid Secure Resource Allocation and Trajectory Optimization Approach for Mobile Edge Computing Using Federated Learning Based on WEB 3.0

A Hybrid Secure Resource Allocation and Trajectory Optimization Approach for Mobile Edge Computing Using Federated Learning Based on WEB 3.0

Jointly Active/Passive Beamforming Optimization for Intelligent-Reflecting Surface-Assisted Cognitive-IoT Networks

To overcome challenges such as limited energy availability for terminal devices, constrained network coverage, and suboptimal spectrum resource utilization, with the overarching objective of establishing a sustainable and efficient interconnection infrastructure, we introduce an innovative Intelligent Reflective Surface (IRS) technology. This cutting-edge IRS technology is employed to architect a wireless and energy-efficient cognitive secure communication network assisted by IRS. To further optimize the overall energy harvesting of this network, we present a cognitive secure resource allocation scheme, aiming to maximize the system’s total collected energy. This scheme carefully considers various constraints, including transmission power constraints for cognitive base stations, power constraints for jammer devices, interference limitations for all primary users, minimum security rate constraints for all cognitive Internet of Things (IoT) devices, and phase shift constraints for IRS. We establish a comprehensive hybrid cognitive secure resource allocation model, encompassing joint cognitive transmission beam design, jammer device transmission beam design, and phase shift design. Given the non-convex nature of the formulated problem and the intricate coupling relationships among variables, we devise an effective block coordinate descent (BCD) iterative algorithm. The realization of joint cognitive/jammer base station transmission beam design and phase shift design employs sophisticated techniques such as continuous convex approximation methods and semi-definite programming. Simulation results underscore the superior performance of the proposed scheme compared to existing resource allocation approaches, particularly in terms of total harvested energy and other critical metrics.

Secure Resource Allocation and Trajectory Design for UAV-Assisted IoT With Double Cluster Head

Secure Resource Allocation and Trajectory Design for UAV-Assisted IoT With Double Cluster Head

Assessment of Extension Workers’ Nutrition Education Competency Level and Training Needs in Katsina State, Nigeria

This research was carried out to assess the extension workers’ nutrition education competency level and training needs in Katsina state, Nigeria. A multistage sampling technique was used to sample 90 extension workers from the three agricultural zones in the state. Seven categories of 49 specific nutrition education competency items were adapted for the study. A structured questionnaire was used to collect data from the sampled respondents. The mean competency rating for each competency item was computed using SPSS and Microsoft Excel. A Mean scale to interpret the computed mean values. While mean weight discrepancy score was used to determine the areas of training needed by the extension workers. The findings showed that 85.6% of the respondents were males with a mean age of 44 years. It also revealed that the extension workers had a high level of literacy and vast years of working experience of 20-25 years. In terms of their nutrition education competency level, the findings showed that 71.42% of the extension workers have an average competency level on basic nutrition knowledge, nutritional needs of different household members, Hygiene and sanitation, Post-harvest handling and food safety, as well as on Gender and nutrition. However, the extension workers were found to have a low competency level in planning and resource allocation for household food security which also featured to be the prioritized area of training needed by the extension workers. It was finally recommended that nutrition education should be integrated into the extension worker's professional training in the study area.

Protecting critical infrastructure with game theory, optimization techniques, and AI algorithms

In light of recent geopolitical developments, Europe and Poland are acutely aware of the urgent importance of infrastructure security. Despite heightened interest and increased investments, our security resources remain severely limited, rendering continuous protection for every potential target unattainable. Consequently, the strategic allocation of security resources becomes an ongoing imperative. This paper presents a short introduction to the core principles behind advanced methods that facilitate automated decision-making in security resource allocation. These methods leverage artificial intelligence (AI), game theory, and optimization techniques, and have demonstrated their effectiveness through multiple real-life deployments in the USA. We also provide a concise overview of this exciting body of research and discuss the solutions and software developed by our team, “AI for Security” at the IDEAS NCBR research institute to protect critical infrastructure in Poland and in Europe.

Blockchain-Empowered Resource Allocation in Multi-UAV-Enabled 5G-RAN: A Multi-Agent Deep Reinforcement Learning Approach

In 5G and B5G networks, real-time and secure resource allocation with the common telecom infrastructure is challenging. This problem may be more severe when mobile users are growing and connectivity is interrupted by natural disasters or other emergencies. To address the resource allocation problem, the network slicing technique has been applied to assign virtualized resources to multiple network slices, guaranteeing the 5G-RAN quality of service. Moreover, to tackle connectivity interruptions during emergencies, UAVs have been deployed as airborne base stations, providing various services to ground networks. However, this increases the complexity of resource allocation in the shared infrastructure of 5G-RAN. Therefore, this paper proposes a dynamic resource allocation framework that synergies blockchain and multi-agent deep reinforcement learning for multi-UAV-enabled 5G-RAN to allocate resources to smart mobile user equipment (SMUE) with optimal costs. The blockchain ensures the security of virtual resource transactions between SMUEs, infrastructure providers (InPs), and virtual network operators (VNOs). We formulate a virtualized resource allocation problem as a hierarchical Stackelberg game containing InPs, VNOs, and SMUEs, and then transform it into a stochastic game model. Then, we adopt a Multi-agent Deep Deterministic Policy Gradient (MADDPG) algorithm to solve the formulated problem and obtain the optimal resource allocation policies that maximize the utility function. The simulation results show that the MADDPG method outperforms the state-of-the-art methods in terms of utility optimization and quality of service satisfaction.

Secure Resource Allocations for Polarization-Enabled Multiple-Access Cooperative Cognitive Radio Networks With Energy Harvesting Capability

We address secure communications over energy-harvesting based orthogonal frequency-division multiple-access (OFDMA) cooperative cognitive radio networks, where one primary user (PU) cooperates with several size-limited secondary users (SUs) in terms of both information transmission and energy harvesting. To improve spectrum utilization and ensure that SU transmitters can harvest as much energy as possible, we let the size-limited SUs be equipped with orthogonally dual-polarized antennas (ODPAs). Based on these setting-ups, we propose the polarization-enabled two-phase cooperative framework, where SU transmitters first apply the power splitting technique to harvest energy from radio frequency (RF) signals radiated by the PU, and then use the harvested energy to concurrently transmit their own and the PU’s data over the same subcarriers. Under our proposed framework, we develop three secure resource allocation schemes for scenarios when SUs are untrusted users, which implies that each SU may overhear the PU’s and the other SUs’ confidential information. For these scenarios, which has hardly been studied, we jointly optimize the allocation of relays, subcarriers, power splitting ratios, and powers when SUs adopt the decode-and-forward (DF) strategy or the amplify-and-forward (AF) strategy to relay the PU’s information, with the objective to maximize the total secrecy rate of all SUs while guaranteeing the PU’s minimum secrecy rate. Finally, we validate and evaluate our proposed cooperative framework and secure resource allocation schemes through numerical analyses.

Fusion of artificial intelligence and game theory for resource allocation in non‐orthogonal multiple access‐assisted device‐to‐device cooperative communication

SummaryDevice‐to‐device (D2D) communication offers a low‐cost paradigm where two devices in close proximity can communicate without needing a base station (BS). It significantly improves radio resource allocation, channel gain, communication latency, and energy efficiency and offers cooperative communication to enhance the weak user's network coverage. The cellular mobile users (CMUs) share the spectral resources (e.g., power, channel, and spectrum) with D2D mobile users (DMUs), improving spectral efficiency. However, the reuse of radio resources causes various interferences, such as intercell and intracell interference, that degrade the performance of overall D2D communication. To overcome the aforementioned issues, this paper presents a fusion of AI and coalition game for secure resource allocation in non‐orthogonal multiple access (NOMA)‐based cooperative D2D communication. Here, NOMA uses the successive interference cancellation (SIC) technique to reduce the severe impact of interference from the D2D systems. Further, we utilized a coalition game theoretic model that efficiently and securely allocates the resources between CMUs and DMUs. However, in the coalition game, all DMUs participate in obtaining resources from CMUs, which increases the computational overhead of the overall system. For that, we employ artificial intelligence (AI) classifiers that bifurcate the DMUs based on their channel quality parameters, such as reference signal received power (RSRP), received signal strength indicator (RSSI), signal‐to‐noise ratio (SNR), and channel quality indicator (CQI). It only forwards the DMUs that have better channel quality parameters into the coalition game, thus reducing the computational overhead of the overall D2D communication. The performance of the proposed scheme is evaluated using various statistical metrics, for example, precision score, accuracy, recall, F1 score, overall sum rate, and secrecy capacity, where an accuracy of 99.38% is achieved while selecting DMUs for D2D communication.

Blockchain-Aided Network Resource Orchestration in Intelligent Internet of Things

The proliferation of users and data traffic poses substantial pressure on resource management in the Internet of Things (IoT). In addition to beneficially allocating scarce network resources, it also needs to meet differentiated users’ Quality-of-Service (QoS) requirements, such as low delay, high security, etc. The distributed management architecture of blockchain and its inherent security features bring inspiration to resource management in the IoT. In this article, we propose a blockchain-enabled resource orchestration scheme for IoT by deep reinforcement learning (DRL), where the IoT edge server and the end user can reach a consensus on the allocation of network resources based on blockchain theory. Moreover, relying on the policy network, the intelligent agent can be trained by these resource attributes to fully perceive the change of the network’s state and hence make dynamic resource allocation decisions. Finally, simulation results show that the proposed resource orchestration scheme has good performance in comparison to other security resource allocation algorithms. The average revenue, the user request acceptance rate, and the profitability are increased by an average of 8.5%, 1.8%, and 11.9%, respectively, compared with other algorithms.

Intelligent resource allocation for transmission security on IRS-assisted spectrum sharing systems with OFDM

In this paper, intelligent reflecting surface (IRS) technology is employed to enhance physical layer security (PLS) for spectrum sharing communication systems with orthogonal frequency division multiplexing (OFDM). Aiming to improve the secondary users’ secrecy rates, a design problem for jointly optimizing the transmission beamforming of secondary base station (SBS), the IRS’s reflecting coefficient and the channel allocation is formulated under the constraints of the requirements of minimum data rates of primary users and the interference between users. As the scenario is highly complex, it is quite challenging to address the non-convexity of the optimization problem. Thus, a deep reinforcement learning (DRL) based approach is taken into consideration. Specifically, we use dueling double deep Q networks (D3QN) and soft Actor–Critic (SAC) to solve the discrete and continuous action space optimization problems, respectively, taking full advantage of the maximum entropy RL algorithm to explore all possible optimal paths. Finally, simulation results show that our proposed approach has a great improvement in security transmission rate compared with the scheme without IRS and OFDM, and our proposed D3QN-SAC approach is more effective than other approaches in terms of maximum security transmission rate.

Information Design for Multiple Interdependent Defenders: Work Less, Pay Off More

This paper studies the problem of information design in a general security game setting in which multiple self-interested defenders attempt to provide protection simultaneously for the same set of important targets against an unknown attacker. A principal, who can be one of the defenders, has access to certain private information (i.e., attacker type), whereas other defenders do not. We investigate the question of how that principal, with additional private information, can influence the decisions of the defenders by partially and strategically revealing her information. In particular, we develop a polynomial time ellipsoid algorithm to compute an optimal private signaling scheme. Our key finding is that the separation oracle in the ellipsoid approach can be carefully reduced to bipartite matching. Furthermore, we introduce a compact representation of any ex ante persuasive signaling schemes by exploiting intrinsic security resource allocation structures, enabling us to compute an optimal scheme significantly faster. Our experiment results show that by strategically revealing private information, the principal can significantly enhance the protection effectiveness for the targets.

Evaluating Criticality of Nodes in Consensus Network Under False Data Injection Attack

In this paper, a finite-duration, magnitude-bounded false data injection (FDI) attack on consensus network is considered. The aim of the attacker is to induce disagreement between nodes and consequently, influence the convergence of consensus algorithm. In order to measure the induced disagreement, a metric, namely induced terminal disagreement (ITD), is defined. The objective of this study is to determine the criticality of individual nodes in terms of the worst-case ITD resulting from attack on them. To achieve that, for every node, the closed-form expressions for the optimal attack input which results in the maximum ITD and the corresponding value of ITD, are obtained. Based on that, criticality ranks are assigned to all nodes. These ranks are beneficial in allocating security resources and designing resilient architecture. Further, the effect of varying attack duration on the worst-case ITDs and criticality ranks, is analyzed. Finally, it is shown that the criticality ranks of nodes have strong negative correlation with their degrees. A numerical example and simulations are presented to illustrate the proposed results.