Securing Smart Cities using LSTM algorithm and lightweight containers against botnet attacks

Wireless sensor network (WSN) has emerged as a major part of the Internet of Things (IoT) and their collaboration helps in the formation of a smart environment. The usability of WSN has gained impetus with the advancement in the field of wireless technology. The impact of this growth can be seen in expanded smart city applications that have enhanced the living standards of citizens. In a smart city, millions of sensors are deployed in various intelligent applications like smart homes, smart transportation, smart industries, smart parking, and so forth to make the city smarter and efficient. These applications continuously generate a huge amount of data to provide innovative services to citizens. Therefore, an efficient collection of this data is utmost important to make an effective decision for the betterment of society. The sensor nodes work in collaboration to send the collected data to the base station. In WSN, the sensor nodes have resource constraints like low power and limited communication range. So, as a result, in reference to sensor processing, the continuous stream of data generated by sensor nodes need to be processed and delivered to the end-user in the optimal lapse of time. One promising approach to accomplish these needs is to collaborate among the sensors and sink in an efficient way to reduce the transmission cost. Recent research showed the emergence of several data collection approaches as data aggregation, data compression, optimal deployment of sinks and sink mobility. These approaches mainly aim specifically to reduce consumption of energy, time delay management and maximize the network lifetime. Referring to this context, this chapter discusses challenges in the field of data collection approaches and proposes possible future directions.

With recent advancements of Information and Communication technologies (ICT), Internet of things (IoT) development has just started creating a significant value across people, processes and organizations. There are numbers of applications across energy, transportation and public safety sectors that have linked with economic growth in GDP and therefore, governments are taking serious actions to enhance ICT and IoT penetration across their countries and achieve economies of scale. In future, it can be easily anticipated to meet growing demand, IoT applications scale will reach to millions of sensor devices in a few sq. kilometers area in order to transform traditional cities towards smart cities and accommodate the urbanization pressures. With the growing demand of data hungry devices and deployment of millions of sensors in a certain place will eventually increase size of information and data transmission flow through the sensor devices itself and infrastructure network nodes and then millions of IP addresses will be needed to provide connectivity. Wireless Sensor Networks implementation under internet of things context likely to approach limitations of network bandwidth availability and the cost of IoT infrastructure will be extremely high if dedicated network resources deployed therefore this paper proposed the innovative architecture using Software Defined Networks (SDN) to provide on demand and efficient network resources for low power wireless personal area network (6LowPAN) devices. This proposed architecture model will also strengthen the applications of IoT in either side i.e. control and monitoring.

Smart Cities as Large Technological Systems: Collective Action Problems as Organizational Barriers to Urban Digital Transformation

A proactive role of IoT devices in building smart cities

Half of the world’s population resides in cities and this percentage is going to rise in the coming years. This rapid transformation of the economy and society is making traditional city management redundant. Intensive urbanization has brought with it some challenges. Scanty resources, population growth, increased carbon footprint, reduced air quality, congestion on roads, depriving health status, and falling food quality, all are prominent parts of cities. Does this bring a question in mind is accelerated growth sustainable? The answer and solution to this is an adaption of the Smart City Concept. A smart city integrates all the available technology with the requirements of city development and builds infrastructure which can improve the quality of life of citizens. Smart cities are designed in a manner to use minimum resources with maximum efficiency in all areas of urban life. It provides smart solutions using smart technologies. Urban needs and technological advancements meet in smart cities. It manages cities more quickly and effectively by using AI, IT technologies, and robotic process automation. Intelligent transportation, intelligent energy, intelligent governance, intelligent security, intelligent healthcare are all central to the idea of smart cities. The emphasis of this essay is on smart transit as a component of smart cities. The movement of people and goods across several geographical boundaries has been significantly aided by the transportation sector. Globally, the majority of smart cities face logistical and transportation issues. Both the fast growing human population and the increase in the number of cars on the road are to blame for this. In order to design and run a sustainable transportation system, technology may be of great assistance. Robotic systems for real-time vehicle data collection, traffic management, and using mobility on demand in trip planning through a single user interface have emerged as cities struggle with traffic congestion. The transportation industry, which faces difficulties including traffic congestion, unanticipated delays, and routing issues that cause businesses to lose money, has successfully used robotic process automation. Robotic process automation provides reliable results and affordable solutions while reducing uncertainty in the decision-making process. It is very helpful in designing and running a successful transit system. Robotic Process Automation (RPA) systems for obtaining real-time data from vehicles, buses, and trucks for traffic management and using mobility on demand in trip planning through a single user interface have emerged as cities struggle with poor management and congestion. By examining different uses of robotic process automation, it is possible to evaluate precise prediction and detection models that are intended to forecast traffic volume, an appropriate route, traffic conditions, accurate information about timing, incidents, and which time and roadway for transport are suitable for running and mobility. The application of RPA in smart cities could be seen in Intelligent Transportation Systems’ Traffic Management, Public Transportation, Safety Management, Manufacturing, and Logistics. The research focuses on areas of concern for smart cities in the sphere of the transportation sector and related difficulties that may be solved via robotic process automation. The article emphasizes the challenges and solutions for making smart transportation more effective by using Intelligent Robotic Transportation Systems.

In the current ongoing world of IoT (Internet of things) devices, it is vital to have a safe, secure and reliable cyberspace. Cyberspace or network is free from all sorts of unethical activities like hacked systems, data breaches and stolen data. For that goal to be accomplished, we need to have a modern, solid and rigid cybersecurity system to stay safe from harm's way. Our research includes recent IoT data security issues and a robust security model that ensures the IoT devices' data security based on a smart city's perspective. According to the recent research data on the vulnerable IoT devices we propose an intelligent and effective security approach to tackle the modern IoT security issues because our data needs to be protected as the IoT devices or “things” as we call it needs to have proper security. A few areas of future research indicate the use of blockchain to resolve the cybersecurity issues of IoT devices are the most promising and exciting.

Extensive internet of things (IoT) devices being in-fected by malware are an increasingly important viable objective in IoT cyberattacks e.g., botnet, virus, trojan, etc. The botnets got leverage from unsecured IoT devices (e.g., CCTV, Raspberry Pi, Arduino Uno, ESP8266, etc) that operates by using the Internet traffic. In recent year, the high-profile IoT device’s vendor and the researcher from all over universities are exploring the robustness of IoT devices against botnet attacks. This research uses a deep learning approach to prevent botnet attacks on IoT networks. The deep residual one-dimensional CNN (1DCNN) model as the proposed method is used for botnet traffic detection. Two algorithms are provided: data processing for the N-BaIoT dataset and IoT botnet detection training and testing. For data processing, training, and testing, the datasets were evaluated, and the model was optimized with different optimizers. This research used RMS Prop, ADaDelta, AdaGrad, AdaMax, and Adam as optimizers and the CNN was compared with LSTM, CNN with RNN, and Deep residual 1DCNN, respectively. The results showed that Deep Residual 1DCNN with Adam has the highest training accuracy of 88.67%, 88.67% for validation accuracy, and 88.53%for test accuracy.

Big data and artificial intelligence based early risk warning system of fire hazard for smart cities

Internet of Things (IoT) is an emerging technology that consists of tiny sensors embedded with IoT devices used in e-Health, smart cities and assisted living. IoT devices are equipped with constrained power, memory, and computing capabilities, which creates new security challenges. By integrating sensors and wireless technologies, this cutting-edge technology automates numerous routine tasks in different domains. The main goal of advancing IoT in the existing healthcare system is to automate the monitoring of hospitals and patients’ health by spreading goodwill toward the IoT vision. IoT has gained popularity on practically all platforms that are available. This paper presents a remote patient health monitoring and diagnosis system that uses Message Queuing Telemetry Transport protocol (MQTT) and Internet of Things (IoT) devices with one time pad cryptography. The proposed plan offers end-to-end data secrecy for an IoT system using MQTT that enables e-health and mobility. Patients’ movements inside nursing homes and healthcare facilities are also managed safely without the need for periodic reconfigurations. Prior to communicating through the MQTT protocol, this secure crypto scheme encrypts the message on both the publisher and subscriber ends. According to the results, the proposed scheme is more efficient than delegation-based architecture in terms of security and lightweight communication. The proposed system established communication between patients and medical experts using IoT technologies to alleviate the hospital strain caused by congestion in medical treatment and enable an expressway of medical responses. According to our results the proposed scheme has low computational overhead compared to other solutions that use conventional encryption to guarantee robust protection against most of the IoT security attacks.

Non-orthogonal multiple access (NOMA) is a promising radio access technique that enables massive connectivity and increased spectral efficiency. The deployment of aerial base stations (ABSs) as a relay is also an optimistic goal that fairly serves a large number of internet of things (IoT) devices. On one side, ABS-assisted communication leverages effective communication services for secondary IoT devices in smart cities. On the other hand, NOMA allows several IoT devices to concurrently acquire the same frequency-time resource. To this end, weighted sum-rate (WSR) is an essential goal because it allows numerous trade-offs between user fairness and sum-rate efficiency. Therefore, this work aims to investigate the WSR for an integrated aerial terrestrial network subject to cellular power and delay constraints in downlink NOMA. Herein, a theoretical insight-based low-complexity iterative solution is provided for optimal power and blocklength allocation to achieve maximum sum-rate. For this purpose, the mixed-integer non-linear problem is formulated and a low-complexity near-optimal solution is proposed. Numerical results show that the proposed scheme achieves a near-optimal solution and outperforms baseline techniques, i.e., the performance gain of 5.18% over the legacy OMA system for NOMA with two IoT devices per subcarrier.

A smart city refers to an intelligent environment obtained by deploying all available resources and recent technologies in a coordinated and smart manner. Intelligent sensors (Internet of Things (IoT) devices) along with 5G technology working mutually are steadily becoming more pervasive and accomplish users' desires more effectively. Among a variety of IoT use cases, e-voting is a considerable application of IoT that relegates it to the next phase in the growth of technologies related to smart cities. In conventional applications, all the devices are often assumed to be cooperative and trusted. However, in practice, devices may be disrupted by the intruders to behave maliciously with the aim of degradation of the network services. Therefore, the privacy and security flaws in the e-voting systems in particular lead to a huge problem where intruders may perform a number of frauds for rigging the polls. Thus, the potential challenge is to distinguish the legitimate IoT devices from the malicious ones by computing their trust values through social optimizer in order to establish a legitimate communication environment. Further, in order to prevent from future modifications of data captured by smart devices, a Blockchain is maintained where blocks of all legitimate IoT devices are recorded. This article has introduced a secure and transparent e-voting mechanism through IoT devices using Blockchain technology with the aim of detecting and resolving the various threats caused by an intruder at various levels. Further, in order to validate the proposed mechanism, it is analyzed against various security parameters such as message alteration, Denial of Service (DoS) and Distributed Denial of Service (DDoS) attack and authentication delay.

Internet of Things (IoT) plays an increasingly significant role in our daily activities, connecting physical objects around us into digital services. In other words, IoT is the driving force behind home automation, smart cities, modern health systems, and advanced manufacturing. This also increases the likelihood of cyber threats against IoT devices and services. Attackers may attempt to exploit vulnerabilities in application protocols, including Domain Name System (DNS), Hyper Text Transfer Protocol (HTTP) and Message Queue Telemetry Transport (MQTT) that interact directly with backend database systems and client-server applications to store data of IoT services. Successful exploitation of one or more of these protocols can result in data leakage and security breaches. In this paper, an ensemble intrusion detection technique is proposed to mitigate malicious events, in particular botnet attacks against DNS, HTTP, and MQTT protocols utilized in IoT networks. New statistical flow features are generated from the protocols based on an analysis of their potential properties. Then, an AdaBoost ensemble learning method is developed using three machine learning techniques, namely decision tree, Naive Bayes (NB), and artificial neural network, to evaluate the effect of these features and detect malicious events effectively. The UNSW-NB15 and NIMS botnet datasets with simulated IoT sensors' data are used to extract the proposed features and evaluate the ensemble technique. The experimental results show that the proposed features have the potential characteristics of normal and malicious activity using the correntropy and correlation coefficient measures. Moreover, the proposed ensemble technique provides a higher detection rate and a lower false positive rate compared with each classification technique included in the framework and three other state-of-the-art techniques.

In this paper we discuss electromagnetic compatibility problems that arise in "Smart city" with electronic infrastructure. There the technical means and system are using in a hard electromagnetic environment in industrial, residential and public areas, as well as in transport. A special load falls on telecommunication networks, which provide both traditional communication and the management of various IoT devices, technical systems and ets. Radio emissions increase significantly. It was emphasized that the continuous and full functioning of technical means in the electromagnetic environment of "Smart city" and the elimination of unacceptable electromagnetic interference created by technical means are mandatory conditions for the implementation of "Smart city" concept, so the safety problem consideration is required. We overview the possible problems overview in the concept of a "Smart city" related to equipment and systems electromagnetic safety. Particular attention is paid to technical means and systems related to safety, for example, used in transport and also telecommunication networks. It was emphasized that the continuous and full quality functioning of technical means in "Smart city" electromagnetic environment and the elimination of unacceptable electromagnetic interference created by technical means are mandatory conditions for the implementation of "Smart city" concept, and should take into account all aspects associated with a specific electromagnetic environment with a high density of electronic equipment, requirements for telecommunications networks quality communication

Internet of Things (IoT) paves the way for the modern smart industrial applications and cities. Trusted Authority acts as a sole control in monitoring and maintaining the communications between the IoT devices and the infrastructure. The communication between the IoT devices happens from one trusted entity of an area to the other by way of generating security certificates. Establishing trust by way of generating security certificates for the IoT devices in a smart city application can be of high cost and expensive. In order to facilitate this, a secure group authentication scheme that creates trust amongst a group of IoT devices owned by several entities has been proposed. The majority of proposed authentication techniques are made for individual device authentication and are also utilized for group authentication; nevertheless, a unique solution for group authentication is the Dickson polynomial based secure group authentication scheme. The secret keys used in our proposed authentication technique are generated using the Dickson polynomial, which enables the group to authenticate without generating an excessive amount of network traffic overhead. IoT devices' group authentication has made use of the Dickson polynomial. Blockchain technology is employed to enable secure, efficient, and fast data transfer among the unique IoT devices of each group deployed at different places. Also, the proposed secure group authentication scheme developed based on Dickson polynomials is resistant to replay, man-in-the-middle, tampering, side channel and signature forgeries, impersonation, and ephemeral key secret leakage attacks. In order to accomplish this, we have implemented a hardware-based physically unclonable function. Implementation has been carried using python language and deployed and tested on Blockchain using Ethereum Goerli’s Testnet framework. Performance analysis has been carried out by choosing various benchmarks and found that the proposed framework outperforms its counterparts through various metrics. Different parameters are also utilized to assess the performance of the proposed blockchain framework and shows that it has better performance in terms of computation, communication, storage and latency.

In this study, we first present a framework that jointly optimizes energy harvesting and information decoding for Internet of Things (IoT) devices, which are capable of simultaneous wireless information and power reception, in a smarty city. In particular, a generalized power-splitting receiver for IoT devices is designed, where each antenna in the receiver has an independent power splitter, unlike the existing works in which only one power splitter is employed regardless of the number of antennas in the receiver. Such a receiver design can provide a great degree of freedom to improve the network performance. Based on the presented framework, for each IoT device, we formulate an optimization problem whose objective is to maximize the harvested energy of each IoT device while satisfying its data rate requirement. To solve this problem, we propose a double-deep deterministic policy gradient-based online learning algorithm which enables each IoT device to jointly determine receive beamforming and power-splitting ratio vectors in real time. Furthermore, each IoT device can implement the proposed algorithm in a distributed manner using only its local channel state information. As such, cooperation and information exchange among the base stations and IoT devices are not necessary when performing the proposed algorithm at IoT devices. The extensive simulation results show the validity of the proposed algorithm.