For safe path planning of unmanned aerial vehicles (UAVs) in a three-dimensional (3D) environment with multiple threats, first, a cost function is introduced according to the terrain constraints and UAV overall performance constraints of the path planning problem. Then, improved nonlinear dynamic inertia weights (INDIW) are introduced into the particle swarm optimization (PSO) algorithm, and when the particles fall into the local optimum, the velocity is perturbed, and the velocity and improved nonlinear dynamic inertia weight PSO (VAINDIWPSO) algorithm are obtained. The algorithm improves the speed of convergence and fitness function value of the PSO algorithm. However, the impact of flyable path optimization is now not obvious. Therefore, to further enhance the overall performance of the VAINDIWPSO algorithm, the adaptive adjustment of the velocity is introduced, the chaotic initialization is carried out, and the improved logistic chaotic map is introduced into the algorithm, and an improved chaotic-VAINDIWPSO (IC-VAINDIWPSO) algorithm is obtained. Then, the corresponding relationship between the algorithm and constraints is used to efficiently search complicated environments and find paths with excessive security and small cost function. The simulation outcomes exhibit that in a complicated environment the IC-VAINDIWPSO algorithm substantially improves the speed of convergence of the algorithm, reduces the fitness function value of the algorithm and the initialization time of the algorithm, and the acquired path is additionally smoother. A near-optimal solution is obtained.

Penetration testing is an important method to evaluate the security degree of a network system. The importance of penetration testing attack path planning lies in its ability to simulate attacker behavior, identify vulnerabilities, reduce potential losses, and continuously improve security strategies. By systematically simulating various attack scenarios, it enables proactive risk assessment and the development of robust security measures. To address the problems of inaccurate path prediction and difficult convergence in the training process of attack path planning, an algorithm which combines attack graph tools (i.e., MulVAL, multi-stage vulnerability analysis language) and the double deep Q network is proposed. This algorithm first constructs an attack tree, searches paths in the attack graph, and then builds a transfer matrix based on depth-first search to obtain all reachable paths in the target system. Finally, the optimal path for target system attack path planning is obtained by using the deep double Q network (DDQN) algorithm. The MulVAL double deep Q network(MDDQN) algorithm is tested in different scale penetration testing environments. The experimental results show that, compared with the traditional deep Q network (DQN) algorithm, the MDDQN algorithm is able to reach convergence faster and more stably and improve the efficiency of attack path planning.

Insider threats are one of the most costly and difficult types of attacks to detect due to the fact that insiders have the right to access an organization’s network systems and understand its structure and security procedures, making it difficult to detect this type of behavior through traditional behavioral auditing. This paper proposes a method to leverage unsupervised outlier scores to enhance supervised insider threat detection by integrating the advantages of supervised and unsupervised learning methods and using multiple unsupervised outlier mining algorithms to extract from the underlying data useful representations, thereby enhancing the predictive power of supervised classifiers on the enhanced feature space. This novel approach provides superior performance, and our method provides better predictive power compared to other excellent abnormal detection methods. Using only 20% of the computing budget, our method achieved an accuracy of 86.12%. Compared with other anomaly detection methods, the accuracy increased by up to 12.5% under the same computing budget.

The Industrial Internet of Things (IIoT) is used in various industries to achieve industrial automation and intelligence. Therefore, it is important to assess the network security situation of the IIoT. The existing network situation assessment methods do not take into account the particularity of the IIoT’s network security requirements and cannot achieve accurate assessment. In addition, IIoT transmits a lot of heterogeneous data, which is subject to cyber attacks, and existing classification methods cannot effectively deal with unbalanced data. To solve the above problems, this paper first considers the special network security requirements of the IIoT, and proposes a quantitative evaluation method of network security based on the Analytic Hierarchy Process (AHP). Then, the average under-/oversampling (AUOS) method is proposed to solve the problem of unbalance of network attack data. Finally, an IIoT network security situation assessment classifier based on the eXtreme Gradient Boosting (XGBoost) is constructed. Experiments show that the situation assessment method proposed in this paper can more accurately characterize the network security state of the IIoT. The AUOS method can achieve data balance without generating too much data, and does not burden the training of the model. The classifier constructed in this paper is superior to the traditional classification algorithm.

Image steganalysis has been a key technology in information security in recent years. However, existing methods are mostly limited to the binary classification for detecting steganographic images used in digital watermarking, privacy protection, illicit data concealment, and security images, such as unaltered cover images or surveillance images. They cannot identify the steganography algorithms used in steganographic images, which restricts their practicality. To solve this problem, this paper proposes a general steganography algorithms recognition scheme based on image big data matching with improved ResNet50. The scheme first intercepts the image region with the highest complexity and focuses on the key features to improve the analysis efficiency; subsequently, the original image of the image to be detected is accurately located by the image big data matching technique and the steganographic difference feature image is generated; finally, the ResNet50 is improved by combining the pyramid attention mechanism and the joint loss function, which achieves the efficient recognition of the steganography algorithm. To verify the feasibility and effectiveness of the scheme, three experiments are designed in this paper: verification of the selection of the core analysis region, verification of the image similarity evaluation based on Peak Signal-to-Noise Ratio ($PSNR$), and performance verification of the improved ResNet50 model. The experimental results show that the scheme proposed in this paper outperforms the existing mainstream steganalysis models, such as ZhuNet and YeNet, with a detection accuracy of 96.11%, supports the recognition of six adaptive steganography algorithms, and adapts to the needs of analysis of multiple sizes and image formats, demonstrating excellent versatility and application value.

In the realm of urban logistics, optimizing vehicle routes for varied cargo types—including refrigerated, fragile, and standard cargo—poses significant challenges amid complex urban infrastructures and heterogeneous vehicle capacities. This research paper introduces a novel model for the multi-type capacitated vehicle routing problem (MT-CVRP) that harnesses an advanced ant colony optimization algorithm, dubbed Lévy-EGACO. This algorithm integrates Lévy flights and elitist guiding principles, enhancing search efficacy and pheromone update processes. The primary objective of this study is to minimize overall transportation costs while optimizing the efficiency of intricate route planning for vehicles with diverse load capacities. Through rigorous simulation experiments, we corroborated the validity of the proposed model and the effectiveness of the Lévy-EGACO algorithm in optimizing urban cargo transportation routes. Lévy-EGACO demonstrated a consistent reduction in transportation costs, ranging from 1.8% to 2.5% compared to other algorithms, across different test scenarios following base data modifications. These findings reveal that Lévy-EGACO substantially improves route optimization, presenting a robust solution to the challenges of MT-CVRP within urban logistics frameworks.