The revolutionary idea of the internet of things (IoT) architecture has gained enormous popularity over the last decade, resulting in an exponential growth in the IoT networks, connected devices, and the data processed therein. Since IoT devices generate and exchange sensitive data over the traditional internet, security has become a prime concern due to the generation of zero-day cyberattacks. A network-based intrusion detection system (NIDS) can provide the much-needed efficient security solution to the IoT network by protecting the network entry points through constant network traffic monitoring. Recent NIDS have a high false alarm rate (FAR) in detecting the anomalies, including the novel and zero-day anomalies. This paper proposes an efficient anomaly detection mechanism using mutual information (MI), considering a deep neural network (DNN) for an IoT network. A comparative analysis of different deep-learning models such as DNN, Convolutional Neural Network, Recurrent Neural Network, and its different variants, such as Gated Recurrent Unit and Long Short-term Memory is performed considering the IoT-Botnet 2020 dataset. Experimental results show the improvement of 0.57–2.6% in terms of the model’s accuracy, while at the same time reducing the FAR by 0.23–7.98% to show the effectiveness of the DNN-based NIDS model compared to the well-known deep learning models. It was also observed that using only the 16–35 best numerical features selected using MI instead of 80 features of the dataset result in almost negligible degradation in the model’s performance but helped in decreasing the overall model’s complexity. In addition, the overall accuracy of the DL-based models is further improved by almost 0.99–3.45% in terms of the detection accuracy considering only the top five categorical and numerical features.

The end of 2019 and the start of 2020 remained the time of the world's largest medical emergency due to Coronavirus disease known as COVID-19, outbreak in all over the world. We designed an automated robotic assistance system for the patients who are kept in quarantine. In the proposed system, doctors can remotely monitor the medical condition of a patients by maintaining social distance. A robotic cart will provide food and medicine to the patient. The proposed system also contains an emergency system where the doctor and paramedical staff can be called through the LCD panel installed aside the patient's bed or through the mobile application. The article portrays the three levels of the development of the model: the equipment level, which contains the sensors, actuators and robot execution. The design level, which defines the distinctive practical services; and the application level which illustrate the administrations offered by the system. The setup will enable the medical staff to deal with the maximum patients at less time. Also, it will enable them to make social distancing to pay more attention to severely affected patients.

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In the design and planning of next-generation Internet of Things (IoT), telecommunication, and satellite communication systems, controller placement is crucial in software-defined networking (SDN). The programmability of the SDN controller is sophisticated for the centralized control system of the entire network. Nevertheless, it creates a significant loophole for the manifestation of a distributed denial of service (DDoS) attack straightforwardly. Furthermore, recently a Distributed Reflected Denial of Service (DRDoS) attack, an unusual DDoS attack, has been detected. However, minimal deliberation has given to this forthcoming single point of SDN infrastructure failure problem. Moreover, recently the high frequencies of DDoS attacks have increased dramatically. In this paper, a smart algorithm for planning SDN smart backup controllers under DDoS attack scenarios has proposed. Our proposed smart algorithm can recommend single or multiple smart backup controllers in the event of DDoS occurrence. The obtained simulated results demonstrate that the validation of the proposed algorithm and the performance analysis achieved 99.99% accuracy in placing the smart backup controller under DDoS attacks within 0.125 to 46508.7 s in SDN.

Big data analytics and the Internet of Things (IoT) are currently most evolving topics in the field of technology. The leading idea behind the internet of things is that almost every device that has some mac address must be linked with each other. One of the most unique features of IoT is its real-time communication of information about these devices linked together. In the past few years, huge piles of data had been generated due to the miniaturization and diversifying of physical devices that can have mac address. These huge piles of data have compromised the storage and data collection mechanism. Furthermore, this pyramid of data is completely useless without any analytical power. However, the solutions are still in their embryonic stage and this field lacks a comprehensive study and surveys. The four main distinct features about IoT are a) data processing, data change (OTLP), and high-speed data flow (b) huge data sizes generally in TBs and PBs (c) highly diverse structural and unstructured data, query language (d) diverse source of data. This paper explores the state-of-the-art research efforts projected towards these data analytics. Furthermore, data analytic methods and technologies for mining these big data are discussed.

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