The present paper is devoted to Bidirectional Associative Memory (BAM) Cohen–Grossberg-type impulsive neural networks with time-varying delays. Instead of impulsive discontinuities at fixed moments of time, we consider variable impulsive perturbations. The stability with respect to manifolds notion is introduced for the neural network model under consideration. By means of the Lyapunov function method sufficient conditions that guarantee the stability properties of solutions are established. Two examples are presented to show the validity of the proposed stability criteria.

The aim of this article is to develop efficient methods of expressing multilevel structured information from various modalities (images, speech, and text) in order to naturally duplicate the structure as it occurs in the human brain. A number of theoretical and practical issues, including the creation of a mathematical model with a stability point, an algorithm, and software implementation for the processing of offline information; the representation of neural networks; and long-term synchronization of the various modalities, must be resolved in order to achieve the goal. An artificial neural network (ANN) of the Cohen–Grossberg type was used to accomplish the objectives. The research techniques reported herein are based on the theory of pattern recognition, as well as speech, text, and image processing algorithms.

DoS and DDoS attacks remain a major threat to service availability in modern IP and IoT networks, yet many learning-based detectors depend on dataset-specific flow exports, feature tables, or preprocessing conventions. This article presents a unified sequence-level detection pipeline designed to process heterogeneous public datasets through the same representation. Raw PCAP/PCAPNG traces from CIC-IDS-2017, CIC-DDoS-2019, and CICIoT2023 are converted into one-second aggregates per destination host using header-only features derived from IP, TCP, UDP, and ICMP metadata, source diversity, and packet timing. Dataset-specific annotations are used only to assign binary DoS/DDoS labels to this common representation. The resulting time-ordered aggregates are grouped into fixed-length temporal windows and classified by a compact transformer encoder, TemporalDosTransformer, which produces a window-level attack probability. The study focuses on whether a clean PCAP-based aggregation and labelling flow can support consistent DoS/DDoS detection across multiple datasets without payload inspection, flow-exporter dependence, or dataset-specific feature engineering.