Blockchain technology is regarded as the emergent security solution for many applications related to the Internet of Things (IoT). In concept, blockchain has a linear structure that grows with the number of transactions entered. This growth in size is the main obstacle to the blockchain, which makes it unsuitable for resource-constrained IoT environments. Moreover, conventional consensus algorithms such as PoW, PoS are very computationally heavy. This paper solves these problems by introducing a new lightweight blockchain structure and lightweight consensus algorithm. The Multi-Zone Direct Acyclic Graph (DAG) Blockchain (Multizone-DAG-Blockchain) framework is proposed for the fog-based IoT environment. In this context, fog computing technology is integrated with the IoT to offload IoT tasks to the fog nodes, thus preserving the energy consumption of the IoT devices. Both IoT and fog nodes are initially authenticated using a non-cloneable physical function-based validation mechanism (DPUF-VM) in which multiple authentication certificates are verified in the blockchain. Each transaction is stored in a hash function in the blockchain using the lightweight CubeHash algorithm and signed by the Four-Q-Curve algorithm. In the cloud, sensitive data is stored as ciphertext. Fog nodes provide data security to avoid the energy consumption and complexity of IoT nodes. The fog node first performs a redundancy analysis using the Jaccard Similarity (JS) measure and sensitivity analysis using the Neutrosophic Neural Intelligent Network (N2IN) algorithm. A lightweight proof-of-authentication (PoAh) algorithm is presented and executed by the optimal consensus node selected by the bi-objective spiral optimization (BoSo) algorithm for transaction validation. The proposed work is modeled in Network Simulator 3.26 (ns-3.26), and the performance is evaluated in terms of energy consumption, storage cost, response time, and throughput.

Blockchain merges technology with the Internet of Things (IoT) for addressing security and privacy-related issues. However, conventional blockchain suffers from scalability issues due to its linear structure, which increases the storage overhead, and Intrusion detection performed was limited with attack severity, leading to performance degradation. To overcome these issues, we proposed MZWB (Multi-Zone-Wise Blockchain) model. Initially, all the authenticated IoT nodes in the network ensure their legitimacy by using the Enhanced Blowfish Algorithm (EBA), considering several metrics. Then, the legitimately considered nodes for network construction for managing the network using Bayesian-Direct Acyclic Graph (B-DAG), which considers several metrics. The intrusion detection is performed based on two tiers. In the first tier, a Deep Convolution Neural Network (DCNN) analyzes the data packets by extracting packet flow features to classify the packets as normal, malicious, and suspicious. In the second tier, the suspicious packets are classified as normal or malicious using the Generative Adversarial Network (GAN). Finally, intrusion scenario performed reconstruction to reduce the severity of attacks in which Improved Monkey Optimization (IMO) is used for attack path discovery by considering several metrics, and the Graph cut utilized algorithm for attack scenario reconstruction (ASR). UNSW-NB15 and BoT-IoT utilized datasets for the MZWB method simulated using a Network simulator (NS-3.26). Compared with previous performance metrics such as energy consumption, storage overhead accuracy, response time, attack detection rate, precision, recall, and F-measure. The simulation result shows that the proposed MZWB method achieves high performance than existing works

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