Crypto jacking Trap LSTM and ResNet and GA in Python Projects
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Crypto jacking Trap LSTM and ResNet and GA in Python Projects
Abstract
Crypto-jacking is the covert hijacking of compute resources to mine cryptocurrencies, causing degraded performance, elevated energy use, and security exposure across cloud, enterprise, and edge environments. The project Crypto-jacking Trap using LSTM, ResNet and Genetic Algorithm in Python Projects proposes a hybrid detection and mitigation pipeline that combines temporal modeling, image-based pattern recognition, and evolutionary optimization to detect stealthy and evolving mining campaigns. Time-series telemetry (CPU, GPU, process metrics, network flows) is modeled with Long Short-Term Memory (LSTM) networks to capture sustained or periodic mining behavior, while ResNet-based convolutional models analyze image-like encodings (heatmaps, spectrograms, or visualized process-feature matrices) to detect structural signatures of miners. A Genetic Algorithm (GA) automatically selects features, tunes model hyperparameters, and optimizes alert thresholds and remediation policies to maximize detection performance while minimizing false positives and runtime overhead. Implemented in Python using standard data-science and ML libraries, the system aims to be adaptive, explainable, and deployable in real operational environments.
Existing System
Current defenses against crypto-jacking commonly rely on simple thresholding (e.g., sustained high CPU/GPU usage), signature-based detection, or static heuristics that are brittle to obfuscation and low-and-slow mining techniques. Static binary analysis and sandboxing detect known miner artifacts but struggle with in-memory, script-based, or mutated miners running inside containers and browsers. Rule-based monitors typically produce many false positives during legitimate high-load operations and require manual tuning that does not scale across heterogeneous workloads. Many monitoring pipelines also treat telemetry either as raw time series or isolated snapshots instead of leveraging both temporal patterns and structural representations, which reduces resilience to novel or stealthy attacks. As a result, organizations experience missed detections, noisy alerts, and costly manual investigations.
Proposed System
The proposed system builds a multi-stage crypto-jacking trap that fuses LSTM-based temporal detectors with ResNet image classifiers and uses a Genetic Algorithm to automate optimization. Telemetry collectors aggregate multivariate time series (CPU/GPU utilization, per-process threads, context-switch rates, network I/O, system-call frequencies) and transform them into sliding windows for LSTM consumption and into image-like encodings (time × feature heatmaps, frequency spectrograms of resource usage, or bytecode-activity visualizations) for ResNet analysis. The LSTM flags anomalous temporal behaviors such as prolonged low-intensity mining or periodic bursts, while the ResNet captures structural fingerprints that distinguish mining workloads from benign processes. A GA searches the feature subset, time-window sizes, model architectures, and thresholds using a fitness function that balances recall, precision, computational cost, and alert latency. The pipeline includes explainability outputs (attention/importance scores, Grad-CAM visualizations) and a REST interface for SIEM integration to automate containment actions (throttling, sandboxing, process quarantine). By combining temporal and visual perspectives with automated optimization, the system increases robustness against evasive miners, reduces false alarms, and provides practical, tunable defenses suitable for cloud, enterprise, and edge deployments.
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