Future Internet

AI-driven network security relies increasingly on Large Language Models (LLMs) to detect sophisticated threats; however, their deployment on resource-constrained edge devices is severely hindered by immense parameter scales. While unstructured pruning offers a theoretical reduction in model size, commodity Graphics Processing Unit (GPU) architectures fail to efficiently leverage element-wise sparsity due to the mismatch between fine-grained pruning patterns and the coarse-grained parallelism of Tensor Cores, leading to latency bottlenecks that compromise real-time analysis of high-volume security telemetry. To bridge this gap, we propose SPARTA (Sparse Parallel Architecture for Real-Time Threat Analysis), an algorithm–architecture co-design framework. Specifically, we integrate a hardware-based address remapping interface to enable flexible row-offset access. This mechanism facilitates a novel graph-based column vector merging strategy that aligns sparse data with Tensor Core parallelism, complemented by a pipelined execution scheme to mask decoding latencies. Evaluations on Llama2-7B and Llama2-13B benchmarks demonstrate that SPARTA achieves an average speedup of 2.35× compared to Flash-LLM, with peak speedups reaching 5.05×. These findings indicate that hardware-aware microarchitectural adaptations can effectively mitigate the penalties of unstructured sparsity, providing a viable pathway for efficient deployment in resource-constrained edge security.

Integrating Software-Defined Networking (SDN) to enhance mobility management in Vehicular Ad Hoc Networks (VANETs) comes with an additional critical risk. Because centralized controllers are single points of failure, they create the risk that the network will be subject to denial-of-service (DoS) attacks during handovers. Most Intrusion Detection and Prevention systems (IDPSs) do not adequately address these risks because they are topology-blind and have excessive processing layers. This article presents a novel Location-Aware SDN-IDPS Framework that employs a hierarchical defense approach to protect vehicular networks against volumetric attacks. This two-plane system operates with the first tier, which uses dynamic host-location mappings to drop spoofed traffic at the switch level (data plane). In contrast, the second tier analyzes confirmed traffic through a Suricata-based engine to identify and respond to complex flood attack patterns. The experimental results from the Mininet-WiFi testbed show that the system provides a significant improvement over the unprotected state, with controller CPU utilization reduced by up to 18 times (from 9.0% to below 0.5%). In addition, the system provides a 2.3 s guaranteed recovery time, service continuity, successful microsecond-level mitigation time, and a packet delivery ratio (PDR) of 99.73% for legitimate safety messages. In control-plane stress testing, the proposed location-aware logic improved throughput stability by approximately 76.26% compared to the baseline. These findings confirm that offloading anti-spoofing logic to the network edge significantly enhances resilience without compromising performance in safety-critical vehicular environments.

Efficient retrieval of mathematical and structural similarities in System Dynamics models remains a significant challenge for traditional lexical systems, which often fail to capture the contextual dependencies of simulation processes. This paper presents an architectural approach and implementation of a semantic search module integrated into an existing cloud-based modeling and simulation system. The proposed method employs a strategy for serializing graph structures into textual descriptions, followed by the generation of vector embeddings via local ONNX inference and indexing within a vector database (Qdrant). Experimental validation performed on a diverse corpus of complex dynamic models, compares the proposed approach against traditional information retrieval methods (Full-Text Search, Keyword Search in PostgreSQL, and Apache Lucene with Standard and BM25 scoring). The results demonstrate the distinct advantage of semantic search, achieving high precision (over 90%) within the scope of the evaluated corpus and effectively eliminating information noise. In comparison, keyword search exhibited only 24.8% precision with a significant rate of false positives, while standard full-text analysis failed to identify relevant models for complex conceptual queries (0 results). Despite a recorded increase in latency (~2 s), the study proves that the vector-based approach is a significantly more robust solution for detecting hidden semantic connections in mathematical model databases, providing a foundation for future developments toward multi-vector indexing strategies.