Next-Gen IoT Security using Polar Codes-based Cryptography for malware defence through quantum self-attention neural network

• NGIT-PCBC-MD-QSANN enhances malware detection and secures data transmission in IoT environments. • Pre-processing utilizes Multi-Aspect Co-Attentional Collaborative Filtering (MAACF) to handle data irregularities. • Contextual features and trust ratings are extracted using Signed Cumulative Distribution Transform (SCDT). • Malware detection is performed using Quantum Self-Attention Neural Network (QSANN) for identifying DoS, probe, anomaly, and R2L attacks. • Polar Codes-based Cryptography (PCBC) is applied with key optimization using Multi-Objective Fitness Dependent Optimizer Algorithm (MOFDOA). The rapid expansion of the Internet of Things (IoT) has increased security concerns, particularly regarding malware threats. The static and dynamic analysis of malware signatures and behaviour patterns used by current malware detection technologies is time-consuming and have proven to be ineffective for unknown malware detection in real-time. This paper proposes a Next-Gen IoT Security using Polar Codes-based Cryptography for Malware Defense through Quantum Self-Attention Neural Network (NGIT-PCBC-MD-QSANN) for enhancing malware detection and secure data transmission in IoT environments. The framework integrates advanced pre-processing using Multi-Aspect Co-Attentional Collaborative Filtering (MAACF) to address the data irregularities and employs the Signed Cumulative Distribution Transform (SCDT) for extracting contextual features and trust rating. After extraction, the malware detection is performed using Quantum Self-Attention Neural Network (QSANN). It leverages quantum-inspired mechanisms for accurate classification of attacks such as DoS, probe, anomaly and R2L. The Polar Codes-based Cryptography (PCBC) is used with cryptographic key optimization managed by the Multi-Objective Fitness Dependent Optimizer Algorithm (MOFDOA) to ensure secure communication. The experimental results demonstrates high accuracy of 99.5%, precision of 96% and low error rate of 5% when compared with existing methods such as: Enhancement of IoT device security with an Improved Elliptic Curve Cryptography algorithm and malware detection utilizing deep LSTM: High-Confidence Computing (EIDS-IECC-MDUL), An Advance Encryption and Attack Detection Framework for Securing Smart Cities Data in Blockchain using Deep Learning Approach:Wireless Personal Communications (AE-ADF-SSCD-BUDL), an optimized hybrid deep neural network architecture for intrusion detection in real‐time IoT networks (AOH-DNNA-IDRIN) respectively.