In MANET, group key management is a vital part of multicast security. But distribution of keys in an authenticated manner is a difficult task in group key management. The existing methods provide low security with high processing time during group key management resulting does not provide sufficient results. Therefore, enabling security in MANETs using an efficient cluster based group key management with elliptical curve cryptography in consort with sail fish optimization algorithm is proposed in this article for two‐level security with reduced computational overhead. At first, all the nodes in the cluster are structured in hierarchy method. The key server creates public key utilizing private key of the group node. Here, elliptical curve cryptography based meta‐heuristic sail fish optimization algorithm is used to select the optimal private key for better secure communication. After selecting this optimum private key, the key server creates the public key, and a common group key is created using this generated public keys. If the nodes joint or exit from the subgroup, the reset process is executed in group key management process. Finally, this process reduces the computational overhead of rekeying method. The proposed method is simulated by Python programming and network simulator‐3. The proposed elliptical curve cryptography based sail fish optimization algorithm attains 10.9%, 22.21%, and 11.43% low computational overhead, 19.34%, 13.45%, and 42.13% low latency, 43.45%, 22.21%, and 12.22% high packet delivery rate, 11.23%, 13.41%, and 21.11% high network life time than the existing methods.

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The Internet‐of‐Things (IoT) refers to the interconnection of things to the physical network that is embedded with software, sensors, and other devices to exchange information from one device to the other. The interconnection of devices means there is the possibility of challenges like security, trustworthiness, reliability, confidentiality, and so on. To address those issues, we have proposed a novel GTBSS‐HDNN approach which hybridization of Group theory (GT), Binary Spring search (BSS) algorithm, and Hybrid deep neural network (HDNN). The proposed GTBSS‐HDNN approach effectively detects the intrusion in the IoT nodes. Initially, the privacy‐preserving technology was implemented using a Blockchain‐based methodology. Our proposed privacy‐preserving methods are divided into two parts. The first stage utilizes blockchain and the second stage involves Modified Independent Component Algorithm (MICA) to prevent intrusion attacks. The authentication of data is performed by blockchain‐based Enhanced Proof of Work (EPoW) and achieves better authentication. Furthermore, the experimental study is carried out using the ToN‐IoT dataset, which is used to evaluate the performance of our proposed work. To analyze the performance we have taken the performance metrics such as F1‐measure, Detection Rate, Precision, and Accuracy. The performance analyzes depict that the proposed method effectively preserves the accuracy and thereby avert the intrusion attacks. The proposed model achieved 95.3% accuracy, 96.54% precision, 95.23% recall, and 95.67% F‐score values on the ToN‐IoT dataset and 96.23% accuracy, 95.94% precision, 97.03% recall, and 96.70% F‐score results on the BoT‐IoT dataset.

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