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.
Enhancing the quality of human's daily life in respects of comfort is the chief objective of smart environments (SE). The Internet of Things (IoT) is basically an increasing network of smart objects. It commences diverse services in human's routine life relying on its available and also dependable activities. The chief problems in any real-globe SE centered upon the IoT model are the security in addition to privacy. The security susceptibility in IoT-centered systems creates security risks that affect SE applications. So, an Intrusion Detection System (IDS) based Modified Adaptive Neuro-Fuzzy Inference System is proposed aimed at detecting the attacks on IoT Smart Cities (SM). The proposed method comprises '2' phases. They are training and testing. First, the IDS are trained by performing three processes: preprocessing, feature selection (FS) and classification. For training, the proposed technique utilizes the data from the NSL_KDD dataset. Then, IoT sensor values are tested employing the same steps of training. The result of testing comprises '2' models. They are the attacked data and non-attacked data. The non-attacked data is sent to the user securely with the help of Improved Rivest Shamir Adleman method. After that, the user receives and decrypts the data. Then, the decrypted data is forecasted for further analysis. The proposed techniques' experimental outcomes used in FS, classification, and also secure data transmission are contrasted with the existent methods. KeywordsAttack detection • Internet of Things (IoT) • Smart cities • Crow search optimization (CSO) • Chaotic mapping (CM) • CM based CSO (CM-CSO) • Adaptive neuro fuzzy inference system (ANFIS) • Modified ANFIS (MANFIS) • Intrusion detection system (IDS) • Rivest Shamir Adleman (RSA) encryption • Improved RSA (IRSA)
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