Cybersecurity in IoT: Sequence-Based Threat Pattern Identification

The increasing use of IoT devices is one factor that causes better chances of damaging cyber threats, and that’s why robust safety functions to deter real-time threats have been implemented. This research suggests a sequence-based threat pattern identification approach to improve cybersecurity in IoT networks. The suggested system exploits Long Short-term Memories (LSTM) networks for sequence learning which allows the identification of anomalous patterns in IoT traffic. To enhance detection efficiency, the feature extraction approach is performed by a hybrid approach that combines Term Frequency-Inverses Document Frequency (TF-IDF) and Principal Component Analysis (PCA), thus, it ensures the best feature selection and computational overhead reduction. Furthermore, an adaptive thresholding mechanism is connected to regulate the sensitivity of anomaly detection in accordance with the status of the network emerging conditions. The experimental results on the benchmark datasets dedicated to IoT prove the model’s efficiency in identifying all sorts of cyber threats, such as Distributed Denial-of-Services (DDoS) attacks, data injection, and unauthorized access attempts. On the comparison of the traditional machine learning classifiers with the proposed system shows the superior performance of the suggested system in terms of detection accuracy, false positive rate, and computational efficiency. The fusion of sequencebased analysis, deep learning, and feature optimization with real-time threat detection leads to a more secure and resilient IoT ecosystem.