In real-world applications such as emotion recognition from recorded brain activity, data are captured from electrodes over time. These signals constitute a multidimensional time series. In this paper, Echo State Network (ESN), a recurrent neural network with great success in time series prediction and classification, is optimized with different neural plasticity rules for classification of emotions based on electroencephalogram (EEG) time series. The developed network could automatically extract valid features from EEG signals. We use the filtered signals as the network input and do not take any feature extraction methods. Evaluated on two well-known benchmarks, the DEAP dataset, and the SEED dataset, the performance of the ESN with intrinsic plasticity greatly outperforms the feature-based methods and shows certain advantages compared with other existing methods. Thus, the proposed network can form a more complete and efficient representation, whilst retaining the advantages such as faster learning speed and more reliable performance.

The advancement of communication and internet technology has brought risks to network security. Thus, Intrusion Detection Systems (IDS) was developed to combat malicious network attacks. However, IDSs still struggle with accuracy, false alarms, and detecting new intrusions. Therefore, organizations are using Machine Learning (ML) and Deep Learning (DL) algorithms in IDS for more accurate attack detection. This paper provides an overview of IDS, including its classes and methods, the detected attacks as well as the dataset, metrics, and performance indicators used. A thorough examination of recent publications on IDS-based solutions is conducted, evaluating their strengths and weaknesses, as well as a discussion of their potential implications, research challenges, and new trends. We believe that this comprehensive review paper covers the most recent advances and developments in ML and DL-based IDS, and also facilitates future research into the potential of emerging Artificial Intelligence (AI) to address the growing complexity of cybersecurity challenges.