Advanced Spam Detection for IoT Security with Real-Time Processing

Abstract

In our attempt to strengthen IoT ecosystem security, we introduce a creative improvement to spam detection within IoT data stream. Our perspective amalgamate stacked Convolutional Neural Networks      (CNN),  Random Forest, Logistic Regression, Support Vector Machine(SVM), and Decision Tree algorithm. While logistic regression, and SVM provide foundational analysis, stacked CNNs through complicated spatial patterns, LSTM and GRU purify intolerance by catching long-term temporal dependencies. This extensive fusion allow  our model to notice delicate features and subtle dissimilarity in IoT data, improving its ability to differentiate between legitimate and spam data instance. Moreover, clustering algorithms like k-means and DBSCAN are utilized for anomaly detection, while privacy-conserving methods, such as Homomorphic Encryption and Differential Privacy safeguard the delicate data. To improve realtime responsiveness, we introduce edge computing paradigms which permits swift processing of IoT data streams and making easier to timely detection and reduction of security threats. Ensemble learning methodologies include random forests and gradient boosting increase prediction exactness and flexibility against adversarial exploit, improving robustness for our spam detection system. Updated approaches like adversarial training are united to fortify the model against to dodging trails, reducing potential suspectability. User feedback mechanism authorizes iterative clarification, permitting the model to adapt and enhance its decision-making process. In addition, the user will be notified if there is any potential spam recognized in his data. In summary, our clear methodology combine advanced machine learning approaches, Real-time processing, elasticity against adversarial threats, and techniques for iterative enhancement, authorizing organizations to fortify their IoT environments with increased security and efficiency.