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Presently, the emergence of internet of things (IoT) has significantly improved the processing, analysis, and management of the substantial volume of big data generated by smart cities. Among the various applications of smart cities, notable ones include location-based services, urban design and transportation management. These applications, however, come with several challenges, including privacy concerns, mining complexities, visualization issues and data security. The integration of blockchain (BC) technology into IoT (BIoT) introduces a novel approach to secure smart cities. This work presents an Archimedes assisted long shortterm memory (LSTM) model intrusion detection for BC based privacy preserving (PP) IoT with smart cities. After the stage of pre-processing, the LSTM is utilized for automated feature extraction and classification. At last, the Archimedes optimizer (AO) is utilized to optimize the LSTM's hyperparameters. In addition, the BC technology is utilized for securing the data transmission. [ABSTRACT FROM AUTHOR]

Based on the information technology service model, an on-demand services towards user becomes cost effective, which is provided with cloud computing. The network attack is detected with research community that pays huge interest. The novel proposed framework is intended with the combination of mitigation and detection of attack. While enormous traffic is obtainable, extract the relevant fields decide with Software-as-a-service (SaaS) provider. According to the network vulnerability and mitigation procedure, perform deep learning-based attack detection model. The golf optimization algorithm (GOA) done the selection of features followed by deep neural network (DNN) detect the attacks from the selected features. The correntropy variational features validates the level of risk and performs vulnerability assessment. Perform the process of bait-oriented mitigation during the phase of attack mitigation. The proposed approach demonstrates 0.97kbps throughput with 0.2% packet loss ratio than traditional methods. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNN) are widely used by researchers due to their extensive advantages over various applications. However, images are highly susceptible to malicious attacks using perturbations that are unrecognized even under human intervention. This causes significant security perils and challenges to CNN-related applications. In this article, an efficient adversarial training model against malevolent attacks is demonstrated. This model is highly robust to black-box malicious examples, it is processed with different malicious samples. Initially, malicious training models like fast gradient descent (FGS), recursive-FGSM (I-FGS), Deep-Fool, and Carlini and Wagner (CW) techniques are utilized that generate adversarial input by means of the CNN acknowledged to the attacker. In the experimentation process, the MNIST dataset comprising 60K and 10K training and testing grey-scale images are utilized. In the experimental section, the adversarial training model reduces the attack accuracy rate (ASR) by an average of 29.2% for different malicious inputs, when preserving the accuracy of 98.9% concerning actual images in the MNIST database. The simulation outcomes show the preeminence of the model against adversarial attacks. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMT) has paved the way for innovative approaches to collecting and managing medical data. With the large and sensitive medical data being processed hence, the need for a strong identity and privacy become necessary. The present paper suggests a comprehensive method of PriMedGuard which aims at protection of the personal medical information. The first stage will be data collection from devices and sensors, then data cleaning to transform the data into the required format. There is also a safety system in the system that registers and authenticates authorized entities as well as ETDO (Enhanced Tasmanian Devil Optimization algorithm) is used for generating asymmetric cryptographic keys. The data is encrypted using the Secure Bit-Count Transmutation (SBCT) Data Encryption Algorithm and then put in the locations provided by the InterPlanetary File System (IPFS), a decentralized and distributed storage system. A safe smart contract on the blockchain is created so that the data retrieval is secure and MedSecEnsemble Detection is proposed as an intrusion detection technique in the IoMT network. By using this method, data will stay available while at the same time integrity, confidentiality and protection against vulnerabilities are ensured. Hence, the Internet of Medical Things ecosystem will be secured from unauthorized access and possible security threats. [ABSTRACT FROM AUTHOR]

The tremendous use of sensitive and consequential information in the advanced web application confronts the security issues. To defend the web application while it processing the information must requires the security system. The detection of attacks of web is made by the payload or HTTP request-based detection in association with the scholars. Some of the scholars provide secured attack model detection; however, it fails to achieve the optimal detection accuracy. In concern with these issues, we propose an innovative technique for the attack detection the web applications. The proposed attack detection is based on the novel deep CapsuleNet based technique and the process begins with pre-processing steps known as decoding, generalization, tokenization/standardization and vectorization. After the pre-processing steps the information are passed to deep CapsuleNet for extracting the features for attaining the temporal dependencies from the sequential data. The subtle patterns in the information also detected using the proposed work. Simulation is effectuated to demonstrate the effectiveness of the proposed work and compared with other existing works. Our proposed system provides better accuracy in detecting the attacks than the state-of-art works. [ABSTRACT FROM AUTHOR]

The daunting issues that are promptly faced worldwide are the sophisticated cyber-attacks in all kinds of organizations and applications. The development of cloud computing pushed organizations to shift their business towards the virtual machines of the cloud. Nonetheless, the lack of security throughout the programmatic and declarative levels explicitly prone to cyber-attacks in the cloud platform. The exploitation of web pages and the cloud is due to the uncrated open web application security projects (OWASP) fragilities and fragilities in the cloud containers and network resources. With the utilization of advanced hacking vectors, the attackers attack data integrity, confidentiality, and availability. Hence, it’s ineluctable to frame the application security-based technique for the reduction of attacks. In concern to this, we propose a novel Deep learning-based secured advanced web application firewall to overcome the lack of missing programmatic and declarative level securities in the application. For this, we adopted the MobileNet-based technique to ensure the assurance of security. Simulations are effectuated and analyzed the robustness with the statistical parameters such as accuracy, precision, sensitivity, and specificity and made the comparative study with the existing works. Our proposed technique surpasses all the other techniques and provides better security in the cloud. [ABSTRACT FROM AUTHOR]

Due to the progress of communication technologies, diverse information is transmitted in distributed systems via a network model. Concurrently, with the evolution of communication technologies, the attacks have broadened, raising concerns about the security of networks. For dealing with different attacks, the analysis of intrusion detection system (IDS) has been carried out. Conventional IDS rely on signatures and are time-consuming for updation, often lacking coverage for all kinds of attacks. Deep learning (DL), specifically generative methods demonstrate potential in detecting intrusions through network data analysis. This work presents a bidirectional generative adversarial network (BiGAN) for the detection of cyberattacks using the IoT23 database. This BiGAN model efficiently detected different attacks and the accuracy and F-score values achieved were 98.8% and 98.2% respectively. [ABSTRACT FROM AUTHOR]

IoT devices produce a gigantic amount of data and it has grown exponentially in previous years. To get insights from this multi-property data, machine learning has proved its worth across the industry. The present paper provides an overview of the variety of data collected through IoT devices. The conflux of machine learning with IoT is also explained using the bibliometric analysis technique. This paper presents a systematic literature review using bibliometric analysis of the data collected from Scopus and WoS. Academic literature for the last six years is used to explore research insights, patterns, and trends in the field of IoT using machine learning. This study analyses and assesses research for the last six years using machine learning in seven IoT domains like Healthcare, Smart City, Energy systems, Industrial IoT, Security, Climate, and Agriculture. The author’s and country-wise citation analysis is also presented in this study. VOSviewer version 1.6.18 is used to provide a graphical representation of author citation analysis. This study may be quite helpful for researchers and practitioners to develop a blueprint of machine learning techniques in various IoT domains. [ABSTRACT FROM AUTHOR]