Praveen Ravula, University of Florida, United States of America
This paper presents the U.S. AI-driven Vulnerability Assessment and Containment Framework (US-AIVAC framework), which can be used to better defend and resilient U.S. critical infrastructure against increasing cybersecurity risks. The suggested system will incorporate intelligent preprocessing, risk assessment through machine learning, automated containment systems to detect and remove vulnerabilities in the infrastructure settings. First, data preprocessing methods (e.g. data cleaning, normalisation, etc.) are done to make the data consistent and reliable to be analysed. To accomplish the vulnerability risk assessment, the Deep Neural Network (DNN) model is used, and vulnerabilities related attributes are identified, and the vulnerability severity are predicted. Moreover, a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) network is employed to accommodate spatial and sequential rates of vulnerability data and thus detect intricate cyber-attack behaviours. The suggested US-AIVAC model is expected to enhance the priority of vulnerabilities, enhance threat detection and response to cybersecurity in a timely manner.
AI-Driven Cybersecurity, Vulnerability Risk Assessment, CNN–LSTM Threat Detection, Automated Containment Framework, U.S. Critical Infrastructure Protection.
Sarra Bouzayane, Imène Sekkiou, Houssam Moumouh and Ammar Djebabla Research and Development Department, Caplogy Innovation, Paris, France
Real-time pedestrian trajectory prediction is critical for autonomous drone navigation in indoor environments. While the FORTRAJ system successfully addresses trajectory prediction in fisheye camera environments, its standard configuration requires 8 complete observations before generating predictions, introducing significant activation latency that compromises reactivity during critical early moments. This paper introduces E-FORTRAJ (Early FORTRAJ), an enhanced system that reduces activation latency by 50% through two complementary innovations: (1) enrichment of input trajectories with 12 scientific descriptors spanning kinematic, geometric, statistical, and contextual features to compensate for reduced temporal context, and (2) simple linear extrapolation that generates missing observations while respecting physical constraints. Experiments on the ETH/UCY benchmark demonstrate that E-FORTRAJ maintains competitive performance while triggering predictions from only 4 observations instead of 8. Cross-domain generalization tests on fisheye datasets (HABBOF, Caplogy) confirm the approach's robustness, with E-FORTRAJ achieving ADE of 0.32m and FDE of 0.38m on HABBOF, outperforming both standard and enriched-only configurations. Real-world validation on Caplogy videos at 25 fps shows prediction activation within 160ms (4 frames), demonstrating practical feasibility for drone deployment. These results establish E-FORTRAJ as a viable solution for latency-critical applications requiring early trajectory anticipation in fisheye environments.
Trajectory Prediction, Fisheye Camera, Early Prediction, Feature Engineering, Linear Extrapolation, Autonomous Drones .
Sarra Bouzayane, Yassir Zardoua, Mouad Kahouadji, Imène Sekkiou Research and Development Department, Caplogy Innovation, Paris, France
Fisheye cameras offer wide field-of-view advantages for robotics and autonomous systems, but their inherent radial distortions complicate computer vision tasks. Classical calibration methods require time-consuming manual configuration and must be repeated when intrinsic parameters change. This paper explores deep learning alternatives for direct fisheye-to-perspective image transformation, bypassing traditional calibration. We evaluate two complementary architectures: U-Net with ResNet34 encoder for geometric transformation, and conditional Generative Adversarial Network (cGAN) with Pix2Pix framework for enhanced perceptual realism. Both models are trained on the synthetic Multi-FoV dataset containing 2,500 fisheye-perspective image pairs from urban environments. Experimental results demonstrate successful distortion correction with distinct performance profiles: U-Net achieves Structural Similarity Index Measure (SSIM) of 0.947 emphasizing structural preservation, while cGAN reaches Peak Signal-to-Noise Ratio (PSNR) of 28 dB through adversarial refinement, reducing Mean Absolute Error (MAE) by 82%. We analyze the architectural trade-offs between pixel fidelity and structural coherence, discuss fundamental limitations regarding field-of-view preservation, and propose hybrid multi-projection approaches for future work.
Fisheye Camera Calibration, Deep Learning, U-Net, Generative Adversarial Network, Image Rectification .
Sudhanshu Sekhar Tripathy and Bichitrananda Behera, C.V. Raman Global University, India
Network Intrusion Detection Systems (NIDS) are essential for securing networks by identifying and mitigating unauthorized activities indicative of cyberattacks. As cyber threats grow increasingly sophisticated, NIDS must evolve to detect both emerging threats and deviations from normal behavior. This study explores the application of machine learning (ML) methods to improve the NIDS accuracy through analyzing intricate structures in deep-featured network traffic records. Leveraging the 1999 KDD CUP intrusion dataset as a benchmark, this research evaluates and optimizes several ML algorithms, including Support Vector Machines (SVM), Naïve Bayes variants (MNB, BNB), Random Forest (RF), k-Nearest Neighbors (k-NN), Decision Trees (DT), AdaBoost, XGBoost, Logistic Regression (LR), Ridge Classifier, Passive-Aggressive (PA) Classifier, Rocchio Classifier, Artificial Neural Networks (ANN), and Perceptron (PPN). Initial evaluations without hyper-parameter optimization demonstrated suboptimal performance, highlighting the importance of tuning to enhance classification accuracy. After hyper-parameter optimization using grid and random search techniques, the SVM classifier achieved 99.12% accuracy with a 0.0091 False Alarm Rate (FAR), outperforming its default configuration (98.08% accuracy, 0.0123 FAR) and all other classifiers. This result confirms that SVM accomplishes the highest accuracy among the evaluated classifiers. We validated the effectiveness of all classifiers using a tenfold cross-validation approach, incorporating Recursive Feature Elimination (RFE) for feature selection to enhance the classifiers accuracy and efficiency. Our outcomes indicate that ML classifiers are both adaptable and reliable, contributing to enhanced accuracy in systems for detecting network intrusions.
Machine learning classification systems, Network intrusion detection mechanism, KDD CUP 1999 data repository, Hyper-parameter tuning, Performance evaluation, Classification accuracy .
Nikitha Merilena Jonnada, University of the Cumberlands, USA
Database and system security are critical components in modern information technology, underpinning the reliability and trustworthiness of digital services. With the proliferation of cloud computing, Internet of Things (IoT) devices, and mobile platforms, the attack surface for cyber threats has expanded significantly, creating challenges for confidentiality, integrity, and availability of data. This paper provides a comprehensive review of contemporary database and system security concepts, including access control models, encryption techniques, intrusion detection, and auditing practices. Emerging threats such as ransomware, supply chain attacks, and insider threats are analyzed, alongside mitigation strategies including artificial intelligence (AI)-driven monitoring, blockchain-based integrity verification, and quantum-resistant cryptography. Through case studies in healthcare, finance, and critical infrastructure, the paper highlights practical applications and challenges of security implementation. Finally, it identifies future directions in adaptive security frameworks, zero trust architectures, and privacy-preserving computation, emphasizing the need for a proactive and resilient approach to securing databases and systems.
Database security, system security, access control, encryption, Artificial Intelligence.
Nijat Hasanli, Faculty of Economic Sciences, University of Warsaw, Warsaw, Poland
This paper evaluates whether large language model (LLM)-based sentiment analysis can detect financial distress more accurately than traditional dictionary-based methods. Using the 2023 U.S. bank failures as a natural experiment, Silicon Valley Bank (SVB), Signature Bank, and First Republic Bank each failed during March–May 2023, we construct monthly sentiment indices for five banks using an LLM alongside VADER, TextBlob, and FinBERT under an identical weighting framework. The LLM index consistently declines ahead of and during the failure period for the three distressed institutions while remaining stable for the two control banks (Bank of America, JPMorgan Chase). VADER, TextBlob, and FinBERT fail to detect the distress, remaining strongly positive throughout. Cohen’s Kappa coefficients near zero (0.01–0.22) confirm that the methods capture fundamentally different signals. The LLM index is constructed using a severity-weighted aggregation scheme incorporating source credibility, model confidence, and recency, normalised via a tanh transformation. These findings suggest that LLMs interpret financial context rather than merely counting sentiment-bearing words, offering a meaningful advance for early-warning and financial risk monitoring applications.
LLM, sentiment analysis, financial distress, bank failures, VADER, TextBlob, FinBERT, early-warning systems, Cohen’s Kappa, sentiment index
Kandarp Mukeshkumar Sharda and Aliyu Sani Sambo, University of Wales Trinity Saint David, United Kingdom
Traditional portfolio management systems rely on static rules or fixed prompts, which limit adaptability to dynamic market conditions. This paper proposes a closed loop, multi agent decision system that introduces prompt level learning as a scalable alternative to model retraining. The architecture consists of specialised agents responsible for market signal extraction, sentiment understanding, macroeconomic analysis, risk control, and portfolio construction, coordinated through the DSPy framework and powered by Llama 3.1 8B. A key contribution is a closed-loop optimisation mechanism that continuously refines agent reasoning using real trading outcomes without human intervention: moderate drawdowns trigger incremental prompt updates, while severe drawdowns activate full prompt reconfiguration. Empirical evaluation on a six year dataset (2015–2020) shows the system achieves over 80% cumulative returns with improved risk adjusted performance (Sharpe > 1.5), outperforming a SPY buy and hold benchmark, including during the COVID 19 market disruption. These findings demonstrate the potential of prompt level adaptation for robust, autonomous financial decision systems.
Multi-Agent Systems, Large Language Models, Portfolio Management, Prompt Optimisation, Dynamic Feedback Loop
Vikas Jain, Network Technology Department, India
The exponential growth of 5G deployments and the emergence of 6G research have created unprecedented operational complexity for network operators managing heterogeneous, multi-vendor IoT ecosystems. This paper proposes a vendor-neutral, multi-tenant Network Digital Twin (NDT) platform integrated with AI- driven Operations (AIOps) to address real-time telemetry ingestion, cross-vendor KPI normalisation, predictive anomaly detection, and closed-loop automation at scale. The proposed architecture employs an event-driven microservices model underpinned by a graph-based topology engine, time-series persistence, and ensemble machine learning models. Key use cases including network health twinning, what-if simulation, energy optimisation, and root cause analysis are detailed. The framework is evaluated against operational benchmarks, demonstrating significant reductions in mean-time-to-detect and operational expenditure. The architecture aligns with O-RAN principles and IoT data pipeline requirements, positioning the platform as a foundational layer for autonomous, intelligent network operations.
Network Digital Twin, AIOps, 5G/6G IoT, O-RAN, Predictive Anomaly Detection
