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The African Vulture Optimization Algorithm (AVOA) is inspired by African vultures' feeding and orienting behaviors. It comprises powerful operators while maintaining the balance of exploration and efficiency in solving optimization problems. To be used in discrete applications, this algorithm needs to be discretized. This paper introduces two versions based on the S-shaped and V-shaped transfer functions of AVOA and BAOVAH. Moreover, the increase in computational complexity is avoided. Disruption operator and Bitwise strategy have also been used to maximize this model's performance. A multi-strategy version of the AVOA called BAVOA-v1 is presented. In the proposed approach, i.e., BAVOA-v1, different strategies such as IPRS, mutation neighborhood search strategy (MNSS) (balance between exploration and exploitation), multi-parent crossover (increasing exploitation), and Bitwise (increasing diversity and exploration) are used to provide solutions with greater variety and to assure the quality of solutions. The proposed methods are evaluated on 30 UCI datasets with different dimensions. The simulation results showed that the proposed BAOVAH algorithm performed better than other binary meta-heuristic algorithms. So that the proposed BAOVAH algorithm set is the most accurate in 67% of the data set, and 93% of the data set is the best value of the fitness functions. In terms of feature selection, it has shown high performance. Finally, the proposed method in a case study to determine the number of neurons and the activator function to improve deep learning results was used in the sentiment analysis of movie viewers. In this paper, the CNNEM model is designed. The results of experiments on three datasets of sentiment analysis—IMDB, Amazon, and Yelp—show that the BAOVAH algorithm increases the accuracy of the CNNEM network in the IMDB dataset by 6%, the Amazon dataset by 33%, and the Yelp dataset by 30%. [ABSTRACT FROM AUTHOR]

Summary: Along with expansion in using of Internet and computer networks, the privacy, integrity, and access to digital resources have been faced with permanent risks. Due to the unpredictable behavior of network, the nonlinear nature of intrusion attempts, and the vast number of features in the problem environment, intrusion detection system (IDS) is regarded as the main problem in the security of computer networks. A feature selection technique helps to reduce complexity in terms of both the executive load and the storage by selecting the optimal subset of features. The purpose of this study is to identify important and key features in building an IDS. To improve the performance of IDS, this paper proposes an IDS that its features are optimally selected using a new hybrid method based on fruit fly algorithm (FFA) and ant lion optimizer (ALO) algorithm. The simulation results on the dataset KDD Cup99, NSL‐KDD, and UNSW‐NB15 have shown that the FFA–ALO has an acceptable performance according to the evaluation criteria such as accuracy and sensitivity than previous approaches. [ABSTRACT FROM AUTHOR]

Nowadays, the growth and pervasiveness of Internet of Things (IoT) devices have led to increased attacks by hackers and attackers. On the other hand, using IoT infrastructure in various fields has increased the number of node security breaches, attacks, and anomalies. Therefore, detecting anomalies in IoT devices is vital to reduce attacks and strengthen security. Over the past few years, various research has been conducted in anomaly-based intrusion detection using machine learning and deep learning methods. The biggest challenge in machine learning methods is the inability to extract new features. To do this, researchers use deep learning methods to extract new features that lead to increased accuracy in intrusion detection. There are important unsolved challenges in research, including determining important features in detecting malicious attacks, extracting features from raw network traffic data using deep networks, and insufficient accuracy in detecting attacks against IoT devices. Convolutional neural networks are considered a powerful and reliable method in this field due to the ability to automatically extract features from data and perform faster calculations. This study has designed and implemented the IoT features extraction convolutional neural network called IoTFECNN with hybrid layers for better anomaly detection in the IoT. Moreover, a binary multi-objective enhanced Capuchin Search Algorithm (CSA) called BMECapSA is developed for efficient feature selection. The combination of the IoTFECNN and BMECapSA methods has led to the introduction of a new hybrid method called CNN-BMECapSA-RF. Finally, the proposed method is implemented and tested on two data sets, NSL-KDD and TON-IoT. The results of various experiments exhibit that the proposed method has better results regarding classification criteria compared to existing deep learning and machine learning-based anomaly detection systems. The proposed method has reached 99.99% and 99.85% accuracy by identifying 27% and 44% of the effective features on the TON-IoT and NSL-KDD datasets, respectively. • Designing a convolutional neural network for feature extraction in IoT. • Extracting high-level and low-level features with hybrid convolution layers. • Combining the RF algorithm with the CNN algorithm. • Introducing an improved version of the capuchin search algorithm. • Introducing a binary multi-objective enhanced capuchin search algorithm. [ABSTRACT FROM AUTHOR]