<![CDATA[The increasing complexity of cyber threats has strengthened the need for adaptive network intrusion detection systems (IDS). This systematic literature review (SLR) synthesizes nine peer-reviewed studies published from 2024 to 2026 on deep learning (DL)-based network anomaly detection. The review follows a PRISMA 2020-aligned process covering database selection, eligibility screening, quality assessment, and structured data extraction. Five research questions examine DL architectures, preprocessing and class-imbalance handling, comparative performance, deployment challenges, and the basis for a conceptual framework. The synthesis compares preprocessing, classification strategy, Accuracy, Precision, Recall, and F1-Score where reported. The evidence shows that hybrid architectures can achieve very high benchmark performance, but the results are dataset- and experimental-setting dependent; therefore, the claim that hybrid models universally exceed 95% is not supported. Across the included studies, the recurring technical pattern is: representative data collection, preprocessing and imbalance mitigation, architecture selection according to spatial or temporal characteristics, and multi-metric evaluation. The revised framework extends this four-layer pipeline by explicitly integrating Explainable AI (XAI) and edge-oriented deployment as cross-cutting operational requirements. The review also identifies limitations in dataset realism, cross-dataset validation, reporting consistency, computational efficiency, and explainability.]]>
