Debabrata Samanta

Network intrusion detection systems (IDS) are still a staple of the contemporary cybersecurity infrastructure, but the traditional methods based on fixed signatures and theoretical rule sets are still not able to overcome the high false positive rates, slow detection and a general inability to follow the fast changing tactics used by attacks. These are especially acute in settings where encrypted traffic streams, heterogeneous IoT devices and advanced adversarial interactions that strategically bypass signature-based defenses are present. In this paper, the author presents the Maintainable Adaptive AI (MAI) model, a new two-stage ensemble architecture, which combines gradient boosting (XGBoost) with a lightweight feedforward neural network to provide real-time continuously adaptive intrusion detection. The MAI model also uses dynamic feature selection of the lexical, host-based, traffic-based, and statistical attributes in addition to an incremental learning process directed by the KL-divergence-based drift detection. In contrast to earlier, ad hoc proposals of adaptive IDS, the MAI framework is specifically maintainable by a selective retraining of components that only increases the weight of the neural networks when in standard operation and initiates a complete ensemble recalibration when distribution drift is beyond predefined thresholds. Thorough testing using four different sets of data, which are the Malicious URLs Dataset (651,191 entries), ISCX VPN-NonVPN Traffic Dataset (200,000 samples), TON IoT Dataset (400, 000 samples), and a newly curated Custom Scraped Dataset (cumulative of approximately 200,000 real-world attack logs across open-source threat intelligence feeds) prove precision of 93–96, recall of 92–95 and overall Formal complexity analysis The computational tractability of the model is defined and inference latencies of 1.8 ms per sample with batched computation on a GPU, which can be deployed at network links with throughput as high as 4.3 Gbps. Initial adversarial robustness evaluation shows that the model still achieves more than 83% accuracy with combined URL perturbation attacks, and the competitiveness and even better performance are proved by the comparison to other recent state-of-the-art solutions such as federated boosting algorithms, collaborative SRU networks, and concept-drift-sensitive random forests. The experimental code and model settings are accessible in the public to reproduce and research them at the repository where Data Availability section is provided.

The growth of high-velocity data flows in security-important fields, such as network cybersecurity or big-data scientific computing, has generated an acute demand among smart systems that can handle data within strict operational deadline limits. Although deep learning (DL) models provide unprecedented levels of analysis power, their computational intensity also makes them unsuitable in applications where any break in the decision deadline can result in disastrous outcomes. The current literature has either concentrated on soft real-time systems or fixed-point optimisation of models, and a large gap in the literature that provides dynamic, adaptive frameworks to ensure performance within strict latency constraints remains open. The paper presents the Dynamic Gated Temporal Convolutional Network (DG-TCN), a new integrative architecture that combines an efficient feature extractor, a lightweight Temporal Convolutional Network (TCN), with a Deep Reinforcement Learning (DRL) agent to dynamically modulate the architecture in real time in a manner that is both practical and scalable. The DRL agent dynamically adjusts the computational graph of the TCN—selectively activating or bypassing convolutional blocks—to actively manage the trade-off between inference accuracy and latency on a per-batch basis. We empirically validate the DG-TCN on two disparate, high-volume public datasets: the CSE-CIC-IDS2018 for cybersecurity threat detection and the HIGGS dataset for scientific particle classification. Our results demonstrate that the DG-TCN outperforms, across evaluated conditions, both high-accuracy static models and low-latency static models, reducing deadline miss rates by up to 78% while maintaining an F1-score within 3% of the much larger, non-real-time model. The theoretical contributions include a new paradigm for deadline-constrained, adaptive-depth deep learning, while the practical benefits manifest in a practically deployable solution for latency-sensitive, security-critical decision-making systems.

Technology Strategy (TechStrat) Fusion aligns technology investments with core business objectives, positioning technology as a strategic asset in the digital revolution. This approach merges TechStrat with Business Strategy, which often operates under separate frameworks for embracing technology and achieving significant business goals. Its inherent complexity demands flexibility for effective implementation. This paper examines whether Agile methodologies offer a successful pathway for delivering iterative and user-centric solutions. Agile principles, including feedback loops, address the challenges of TechStrat Fusion, enabling organisations to manage technology adoption complexities and deliver value rapidly. This study presents two distinct case studies – one from the financial services sector and the other from the manufacturing industry – ­demonstrating how Agile methodologies and the Design Science Research (DSR) model facilitate strategic alignment and measurable improvements in operational performance. Key benefits include generating revenue growth by identifying hidden streams and accelerating time-to-market, enhancing customer satisfaction through a deeper understanding of customer needs, and increasing efficiency by streamlining processes and eliminating bottlenecks. The focus extends to improving patient care quality through IoT technologies and reducing operational costs by aligning technology initiatives with ­business goals. Future research should explore integrating emerging technologies like artificial intelligence (AI) and Blockchain, expanding applications across various sectors, and establishing advanced metrics for evaluating implementations. Conducting longitudinal studies will provide deeper insights into the framework’s long-term impact, ensuring that TechStrat Fusion remains effective in the evolving landscape of strategic technology adoption.