Elevating IoT Security: Integrating LSTM with Symmetric Key Protocols in Distributed Environments
Keywords:
Long Short-Term Memory, Enhanced Elliptic Curve Cryptography, IoT, Symmetric Key, Encryption, DecryptionAbstract
A research integrated Long Short-Term Memory (LSTM) networks with symmetric key encryption techniques in remote situations to improve IoT device security. The research evaluates machine learning algorithms, including the innovative Proposed Deep LSTM model, and compares them on accuracy, precision, recall, specificity, FPR, FNR, and NPV. The Proposed Deep LSTM model surpasses its competitors with 98% accuracy, 98% precision, and 97% recall. It has the best specificity and NPV of 97% and the lowest FPR and FNR of 2% and 8%. These data show the Proposed Deep LSTM's strong prediction skills and its significant advantage over standard models like Radial Basis Function Networks (RBFN), which have an 81% specificity and 83% NPV score. The research compares encryption techniques' encryption and decryption speeds and processing efficiency across file sizes. IoT situations where fast data processing are needed may benefit from Enhanced Elliptic Curve Cryptography (EECC), the fastest technique for encryption and decryption. Blowfish encryption takes longer to complete, making it less efficient for time-sensitive applications. These detailed studies help choose models and algorithms that optimise IoT security, including accuracy, efficiency, and performance scalability. This study leads the application of advanced deep learning models and encryption strategies to secure IoT networks.
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