With an increase in the number and types of network attacks, traditional firewalls and data encryption methods can no longer meet the needs of current network security. As a result, intrusion detection systems have been proposed to deal with network threats. The current mainstream intrusion detection algorithms are aided with machine learning but have problems of low detection rates and the need for extensive feature engineering. To address the issue of low detection accuracy, this paper proposes a model for traffic anomaly detection named a deep learning model for network intrusion detection (DLNID), which combines an attention mechanism and the bidirectional long short-term memory (Bi-LSTM) network, first extracting sequence features of data traffic through a convolutional neural network (CNN) network, then reassigning the weights of each channel through the attention mechanism, and finally using Bi-LSTM to learn the network of sequence features. In intrusion detection public data sets, there are serious imbalance data generally. To address data imbalance issues, this paper employs the method of adaptive synthetic sampling (ADASYN) for sample expansion of minority class samples, to eventually form a relatively symmetric dataset, and uses a modified stacked autoencoder for data dimensionality reduction with the objective of enhancing information fusion. DLNID is an end-to-end model, so it does not need to undergo the process of manual feature extraction. After being tested on the public benchmark dataset on network intrusion detection NSL-KDD, experimental results show that the accuracy and F1 score of this model are better than those of other comparison methods, reaching 90.73% and 89.65%, respectively.

Although a software defined network (SDN) realizes the flexible configuration and centralized control of network resources, there are potential security risks and challenges. Network security situation awareness (NSSA) technology associates and integrates multi-source heterogeneous information to analyze the impact of the information on the whole network, and network security situation assessment can grasp the network security situation information in real time. However, the existing situation assessment methods have low assessment accuracy, and most of the studies focus on traditional networks, while there are few situation assessment studies in the SDN environment. In this paper, by summarizing the important index parameters of SDN, a network security situation assessment model based on the improved back propagation (BP) neural network (based on the cuckoo search algorithm) is proposed, and the step factor of the cuckoo search algorithm (CS) was improved to improve the search accuracy. The model maps the situation elements to the layers of the neural network, and optimizes the weights and thresholds of the BP neural network through the cuckoo search algorithm to obtain the global optimal solution; it finally realizes the purpose of situation assessment and the comprehensive rating of the SDN environment. In this paper, the evaluation model was verified on the network set up in Mininet. The experimental results show that the situation assessment curve of this model is closer to the real situation value, and the accuracy rate is 97.61%, with good situation assessment results.

Cooperative Coevolution (CC) was introduced into evolutionary algorithms as a promising framework for tackling large scale optimization problems through a divide-and-conquer strategy. A number of decomposition methods to identify interacting variables have been proposed to construct subcomponents of a large scale problem, but if the variables are all non-separable, all the CC-based algorithms of decomposition will lose the functionality, therefore, classical CC-based algorithms are inefficient in processing non-separable problems that have many interacting variables. In this paper, a new CC framework which integrates global and local search algorithms is proposed for solving large scale optimization problems. In the stage of global cooperative coevolution, we introduce a new interacting variables grouping method named Sequential Sliding Window. When the performance of global search reaches a deviation tolerance or the variables are fully non-separable, we then use a more effective local search algorithm to subsequently search the solution space of the large scale optimization problem. The integration of global and local algorithms into CC framework can efficiently improve the capability in processing large scale non-separable problems. Experimental results on large scale optimization benchmarks show that the proposed framework is more effective than other existing CC frameworks.

Numerous surrogate-assisted evolutionary algorithms have been proposed for expensive optimization problems. However, each surrogate model has its own characteristics and different applicable situations, which caused a serious challenge for model selection. To alleviate this challenge, this paper proposes an adaptive surrogate-assisted particle swarm optimization (ASAPSO) algorithm by effectively combining global and local surrogate models, which utilizes the uncertainty level of the current population state to evaluate the approximation ability of the surrogate model in its predictions. In ASAPSO, the transformation between local and global surrogate models is controlled by an adaptive Gaussian distribution parameter with a gauge of the advisability to improve the search process with better local exploration and diversity in uncertain solutions. Four expensive optimization benchmark functions and an airfoil aerodynamic real-world engineering optimization problem are utilized to validate the effectiveness and performance of ASAPSO. Experimental results demonstrate that ASAPSO has superiority in terms of solution accuracy compared with state-of-the-art algorithms.