Today, smart cities are being built with the wide deployment of the Internet of Things (IoT). Smart cities (SCs) set out in real time to ameliorate the quality of human life in respect of efficiency and comfort. Security along with privacy are the main issues in most SCs. The IoT-centric frameworks impose certain security threats on smart city applications as they are susceptible to security issues. On this account, an Intrusion Detection System (IDS) is requisite for mitigating the IoTassociated security attacks which take advantage of certain security vulnerabilities. The aim of this paper is to improve the security and attack detection rate as early as possible. In existing works, the accuracy of the attack detection rate and security are the main challenge. To overcome any drawbacks, this work proposes an IDS for detecting the IoT attacks in a city centered on the DLMNN classification. First, the sensor values from a SC are sent to the IDS system (the training phase), which is utilized for testing the respective values. Next, the preprocessing step is performed, and then feature selection (FS) is carried out with the utilization of the Entropy-HOA method. Further on, the classification using DLMNN is performed for detecting the IoT attacks. Then, the results of the classification are analyzed and the attack is identified. Next, a secure data sharing task is performed by using the KH-AES algorithm. Last, the resulting data is forecast. The weights for each layer of the DLMNN have a high impact on the classifier's output. The comparison of the existing technique and of the proposed technique with regard to FS, classification and secure data sharing reveals that the proposed technique obtained the best results.

Nowadays, multimedia big data have grown exponentially in diverse applications like social networks, transportation, health, and e-commerce, etc. Accessing preferred data in large-scale datasets needs efficient and sophisticated retrieval approaches. Multimedia big data consists of the most significant features with different types of data. Even though the multimedia supports various data formats with corresponding storage frameworks, similar semantic information is expressed by the multimedia. The overlap of semantic features is most efficient for theory and research related to semantic memory. Correspondingly, in recent years, deep multimodal hashing gets more attention owing to the efficient performance of huge-scale multimedia retrieval applications. On the other hand, the deep multimodal hashing has limited efforts for exploring the complex multilevel semantic structure. The main intention of this proposal is to develop enhanced deep multimedia big data retrieval with the Adaptive Semantic Similarity Function (A-SSF). The proposed model of this research covers several phases “(a) Data collection, (b) deep feature extraction, (c) semantic feature selection and (d) adaptive similarity function for retrieval. The two main processes of multimedia big data retrieval are training and testing. Once after collecting the dataset involved with video, text, images, and audio, the training phase starts. Here, the deep semantic feature extraction is performed by the Convolutional Neural Network (CNN), which is again subjected to the semantic feature selection process by the new hybrid algorithm termed Spider Monkey-Deer Hunting Optimization Algorithm (SM-DHOA). The final optimal semantic features are stored in the feature library. During testing, selected semantic features are added to the map-reduce framework in the Hadoop environment for handling the big data, thus ensuring the proper big data distribution. Here, the main contribution termed A-SSF is introduced to compute the correlation between the multimedia semantics of the testing data and training data, thus retrieving the data with minimum similarity. Extensive experiments on benchmark multimodal datasets demonstrate that the proposed method can outperform the state-of-the-art performance for all types of data.

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