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Crypto Jacking Streamlit Train ML in Python Projects
र 1000
Crypto Jacking Streamlit Train ML in Python Projects
Technical Details
Domain : Python
Database : Sqlite
Tools : Anaconda
Run Tools: VS Code
Database : Sqlite
Tools : Anaconda
Run Tools: VS Code
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About This Product
Crypto Jacking Streamlit Train ML in Python Projects
Abstract
Cryptojacking is an emerging cybersecurity threat where malicious actors exploit user systems and cloud servers to mine cryptocurrency without consent. These attacks often occur through compromised websites, malware-infected software, or hidden scripts that consume processing power. This project, Crypto Jacking Streamlit Train ML in Python, develops a machine learning–based detection system to identify cryptojacking activities by analyzing system metrics such as CPU usage, memory consumption, network activity, and power utilization. The model is trained using classifiers like Random Forest, Support Vector Machine (SVM), Decision Tree, and Deep Learning models, and is deployed in an interactive Streamlit web application. This enables real-time detection and visualization of cryptojacking attempts, thereby improving system security and resource efficiency.
Existing System
Current cryptojacking detection mechanisms primarily depend on signature-based antivirus tools, browser extensions, or manual monitoring of system performance. These methods often fail to detect new or evolving cryptojacking attacks, as they rely on known patterns or blacklists. Furthermore, traditional systems lack predictive intelligence and real-time visualization, making it difficult for users to detect anomalies proactively. Such systems also struggle with scalability in cloud or enterprise environments where cryptojacking is most damaging.
Proposed System
The proposed system applies machine learning–based anomaly detection to identify cryptojacking activities more accurately. It collects system resource usage data, preprocesses it, and trains multiple ML models to differentiate between normal usage and cryptojacking-influenced behavior. The trained model is integrated into a Streamlit app, which allows users to upload datasets, view model predictions, and monitor resource usage in real time with visual dashboards. This system provides early warnings, interactive reports, and accurate classification, reducing dependency on outdated signature-based tools. Compared to the existing system, the proposed solution is adaptive, scalable, and effective in detecting unknown or zero-day cryptojacking attacks.
Abstract
Cryptojacking is an emerging cybersecurity threat where malicious actors exploit user systems and cloud servers to mine cryptocurrency without consent. These attacks often occur through compromised websites, malware-infected software, or hidden scripts that consume processing power. This project, Crypto Jacking Streamlit Train ML in Python, develops a machine learning–based detection system to identify cryptojacking activities by analyzing system metrics such as CPU usage, memory consumption, network activity, and power utilization. The model is trained using classifiers like Random Forest, Support Vector Machine (SVM), Decision Tree, and Deep Learning models, and is deployed in an interactive Streamlit web application. This enables real-time detection and visualization of cryptojacking attempts, thereby improving system security and resource efficiency.
Existing System
Current cryptojacking detection mechanisms primarily depend on signature-based antivirus tools, browser extensions, or manual monitoring of system performance. These methods often fail to detect new or evolving cryptojacking attacks, as they rely on known patterns or blacklists. Furthermore, traditional systems lack predictive intelligence and real-time visualization, making it difficult for users to detect anomalies proactively. Such systems also struggle with scalability in cloud or enterprise environments where cryptojacking is most damaging.
Proposed System
The proposed system applies machine learning–based anomaly detection to identify cryptojacking activities more accurately. It collects system resource usage data, preprocesses it, and trains multiple ML models to differentiate between normal usage and cryptojacking-influenced behavior. The trained model is integrated into a Streamlit app, which allows users to upload datasets, view model predictions, and monitor resource usage in real time with visual dashboards. This system provides early warnings, interactive reports, and accurate classification, reducing dependency on outdated signature-based tools. Compared to the existing system, the proposed solution is adaptive, scalable, and effective in detecting unknown or zero-day cryptojacking attacks.
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