Enhanced resilience in smart grids: A neural network-based detection of data integrity attacks using improved war strategy optimization

•Design and Development of Intelligent Systems: Proposing a neural network-based approach for detecting data integrity attacks in smart grid systems.•Application in Energy Management: Addressing a significant issue in energy management by focusing on the resilience and security of smart grid infrastructure.•Innovative Optimization Technique: The introduction of the improved war strategy optimization algorithm to enhance the neural network's performance highlights genuine innovation in optimization techniques.•Practical Implementation and Cost-Effectiveness: The proposed framework is designed to be cost-effective and easily implementable into existing algorithm execution modules.•Critical Infrastructures Privacy: By employing only transmitted data, the approach preserves the privacy of modules in the multi-agent systems. Ensuring the resilience and security of Smart Grid (SG) infrastructure is critical for sustainable energy management. This paper proposes a new probabilistic approach for identifying Data Integrity Attacks (DIAs), targeting decentralized consensus-based energy management algorithms. The method uniquely combines Artificial Neural Networks (ANNs) with an Improved War Strategy Optimization Algorithm (IWSOA) to determine optimal weight and bias factors, offering superior performance compared to existing techniques. Key advantages include: 1) it functions using only transmitted information and network topology, eliminating the need for private data access; 2) it is cost-effective and can be integrated into existing algorithm execution modules; 3) enhanced detection accuracy, achieving up to 99.5 % detection rate with 10 hidden neurons. The proposed framework demonstrates robust performance across various attack scenarios, effectively identifying DIAs in both single and multiple iterations. In a case study using the Future Renewable Electric Energy Delivery and Management (FREEDM) system, the method successfully detected 99.5 % of attacks that would have resulted in a 21 % profit increase for the attacker, thereby protecting the system's integrity. This approach significantly enhances SG infrastructure's resilience against DIAs, contributing to more secure and sustainable energy management. [Display omitted]