Security Without Detection: Economic Denial as a Primitive for Edge and IoT Defense

Detection-based security fails against sophisticated attackers using encryption, stealth, and low-rate techniques, particularly in IoT/edge environments where resource constraints preclude ML-based intrusion detection. We present Economic Denial Security (EDS), a detection-independent framework that makes attacks economically infeasible by exploiting a fundamental asymmetry: defenders control their environment while attackers cannot. EDS composes four mechanisms adaptive computational puzzles, decoy-driven interaction entropy, temporal stretching, and bandwidth taxation achieving provably superlinear cost amplification. We formalize EDS as a Stackelberg game, deriving closed-form equilibria for optimal parameter selection (Theorem 1) and proving that mechanism composition yields 2.1x greater costs than the sum of individual mechanisms (Theorem 2). EDS requires < 12KB memory, enabling deployment on ESP32 class microcontrollers. Evaluation on a 20-device heterogeneous IoT testbed across four attack scenarios (n = 30 trials, p < 0.001) demonstrates: 32-560x attack slowdown, 85-520:1 cost asymmetry, 8-62% attack success reduction, < 20ms latency overhead, and close to 0% false positives. Validation against IoT-23 malware (Mirai, Torii, Hajime) shows 88% standalone mitigation; combined with ML-IDS, EDS achieves 94% mitigation versus 67% for IDS alone a 27% improvement. EDS provides detection-independent protection suitable for resource-constrained environments where traditional approaches fail. The ability to detect and mitigate the malware samples tested was enhanced; however, the benefits provided by EDS were realized even without the inclusion of an IDS. Overall, the implementation of EDS serves to shift the economic balance in favor of the defender and provides a viable method to protect IoT and edge systems methodologies.

Zero-Trust Agentic Federated Learning for Secure IIoT Defense Systems

Recent attacks on critical infrastructure, including the 2021 Oldsmar water treatment breach and 2023 Danish energy sector compromises, highlight urgent security gaps in Industrial IoT (IIoT) deployments. While Federated Learning (FL) enables privacy-preserving collaborative intrusion detection, existing frameworks remain vulnerable to Byzantine poisoning attacks and lack robust agent authentication. We propose Zero-Trust Agentic Federated Learning (ZTA-FL), a defense in depth framework combining: (1) TPM-based cryptographic attestation achieving less than 0.0000001 false acceptance rate, (2) a novel SHAP-weighted aggregation algorithm providing explainable Byzantine detection under non-IID conditions with theoretical guarantees, and (3) privacy-preserving on-device adversarial training. Comprehensive experiments across three IDS benchmarks (Edge-IIoTset, CIC-IDS2017, UNSW-NB15) demonstrate that ZTA-FL achieves 97.8 percent detection accuracy, 93.2 percent accuracy under 30 percent Byzantine attacks (outperforming FLAME by 3.1 percent, p less than 0.01), and 89.3 percent adversarial robustness while reducing communication overhead by 34 percent. We provide theoretical analysis, failure mode characterization, and release code for reproducibility.

Comparative Evaluation of Embedding Representations for Financial News Sentiment Analysis

Financial sentiment analysis enhances market understanding; however, standard natural language processing approaches encounter significant challenges when applied to small datasets. This study provides a comparative evaluation of embedding-based methods for financial news sentiment classification in resource-constrained environments. Word2Vec, GloVe, and sentence transformer representations are evaluated in combination with gradient boosting on manually labeled headlines. Experimental results identify a substantial gap between validation and test performance, with models performing worse than trivial baselines despite strong validation metrics. The analysis demonstrates that pretrained embeddings yield diminishing returns below a critical data sufficiency threshold, and that small validation sets contribute to overfitting during model selection. Practical application is illustrated through weekly sentiment aggregation and narrative summarization for market monitoring workflows. The findings offer empirical evidence that embedding quality alone cannot address fundamental data scarcity in sentiment classification. For practitioners operating with limited resources, the results indicate the need to consider alternative approaches such as few-shot learning, data augmentation, or lexicon-enhanced hybrid methods when labeled samples are scarce.