The large deployment of Internet of things (IoT) is actually enabling smart city projects and initiatives all over the world. Objects used in daily life are being equipped with electronic devices and protocol suites in order to make them interconnected and connected to the Internet. According to a recent Gartner study, 50 billion connected objects will be deployed in smart cities by 2020. These connected objects will make the authors' cities smart. However, they will also open up risks and privacy issues. As various smart city initiatives and projects have been launched in recent years, they have witnessed not only the expected benefits, but the risks introduced. They describe the current and future trends of smart city and IoT. They also discuss the interaction between smart cities and IoT and explain some of the drivers behind the evolution and development of IoT and smart city. Finally, they discuss some of the IoT weaknesses and how they can be addressed when used for smart cities.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-net.2017.0163

IoT technologies for smart cities

Anomaly detection is crucial to ensure the security of cyber-physical systems (CPS). However, due to the increasing complexity of CPSs and more sophisticated attacks, conventional anomaly detection methods, which face the growing volume of data and need domain-specific knowledge, cannot be directly applied to address these challenges. To this end, deep learning-based anomaly detection (DLAD) methods have been proposed. In this article, we review state-of-the-art DLAD methods in CPSs. We propose a taxonomy in terms of the type of anomalies, strategies, implementation, and evaluation metrics to understand the essential properties of current methods. Further, we utilize this taxonomy to identify and highlight new characteristics and designs in each CPS domain. Also, we discuss the limitations and open problems of these methods. Moreover, to give users insights into choosing proper DLAD methods in practice, we experimentally explore the characteristics of typical neural models, the workflow of DLAD methods, and the running performance of DL models. Finally, we discuss the deficiencies of DL approaches, our findings, and possible directions to improve DLAD methods and motivate future research.

Deep Learning-based Anomaly Detection in Cyber-physical Systems: Progress and Opportunities

Anomaly detection is crucial to ensure the security of cyber-physical systems (CPS). However, due to the increasing complexity of CPSs and more sophisticated attacks, conventional anomaly detection methods, which face the growing volume of data and need domain-specific knowledge, cannot be directly applied to address these challenges. To this end, deep learning-based anomaly detection (DLAD) methods have been proposed. In this paper, we review state-of-the-art DLAD methods in CPSs. We propose a taxonomy in terms of the type of anomalies, strategies, implementation, and evaluation metrics to understand the essential properties of current methods. Further, we utilize this taxonomy to identify and highlight new characteristics and designs in each CPS domain. Also, we discuss the limitations and open problems of these methods. Moreover, to give users insights into choosing proper DLAD methods in practice, we experimentally explore the characteristics of typical neural models, the workflow of DLAD methods, and the running performance of DL models. Finally, we discuss the deficiencies of DL approaches, our findings, and possible directions to improve DLAD methods and motivate future research.

Deep Learning-Based Anomaly Detection in Cyber-Physical Systems: Progress and Opportunities

F3TM: Flooding Factor based Trust Management Framework for secure data transmission in MANETs

Towards a framework of key technologies for drones

A stochastic approach for packet dropping attacks detection in mobile Ad hoc networks

Mobile Ad hoc Networks (MANETs) are dynamic and self-organized networks composed of mobile wireless entities. The communications between nodes are multihop, and provided in a decentralized way without preexisting infrastructure. These characteristics make MANETs vulnerable to many types of Denial of Service (DoS) attacks, this including, Wormhole, Blackhole and Grayhole attack. This latter targets some reactive routing protocols in the aim of disrupting the forwarding process in the network. Grayhole attack occurs during the route discovery phase when a malicious node drops some of received packets. The watchdog is a well-known intrusion detection mechanism and usually used to detect this kind of attack. However, watchdogs are characterized by a relatively high rate of false alerts. In this paper, we propose a novel approach of watchdog based on two Bayesian filters: Bernoulli and Multinomial. We use these two models in a complementary manner to successfully detect the packet dropping attacks in mobile ad hoc networks. Based on simulation results, our filters prove that these attacks can be detected with a high rate of accuracy.

Denial of service (DoS) attacks detection in MANETs using Bayesian classifiers

Flooding attacks are well-known security threats that can lead to a denial of service (DoS) in computer networks. These attacks consist of an excessive traffic generation, by which an attacker aim to disrupt or interrupt some services in the network. The impact of flooding attacks is not just about some nodes, it can be also the whole network. Many routing protocols are vulnerable to these attacks, especially those using reactive mechanism of route discovery, like AODV. In this paper, we propose a statistical approach to defense against RREQ flooding attacks in MANETs. Our detection mechanism can be applied on AODV-based ad hoc networks. Simulation results prove that these attacks can be detected with a low rate of false alerts.

Flooding attacks detection in MANETs

A Mobile ad hoc network (MANET) is a set of nodes that communicate together in a cooperative way using the wireless medium, and without any central administration. Due to its inherent open nature and the lack of infrastructure, security is a complicated issue compared to other networks. That is, these networks are vulnerable to a a wide range of attacks at different network layers. At the network level, malicious nodes can perform several attacks ranging from passive eavesdropping to active interfering. Wormhole is an example of severe attack that has attracted much attention recently. It involves the redirection of traffic between two end-nodes through a Wormhole tunnel, and manipulates the routing algorithm to give illusion that nodes located far from each other are neighbors. To handle with this issue, we propose a novel detection model to allow a node to check whether a presumed shortest path contains a Wormhole tunnel or not. Our approach is based on the fact that the Wormhole tunnel reduces significantly the length of the paths passing through it.

Graph-based wormhole attack detection in mobile ad hoc networks (MANETs)

Mobile ad-hoc networks (MANETs) are well known to be vulnerable to various attacks, due to features such as lack of centralized control, dynamic topology, and limited physical security. Denial of Service attacks still represent a serious threat for wireless networks. These attacks not only consume the system resources but also isolate legitimate users from the network. Grayhole attack is one of these attacks, which occurs when a malicious node drop some of received data packets during the route discovery process. To detect this attack, we propose in this paper a novel approach based on two Bayesian classification models: Bernoulli and Multinomial. Several tests have been performed using NS2 simulator. Our filters prove that intentionally dropping packets can be fully detected with a low-level of false alerts.

M. Rmayti

Papers

A stochastic approach for packet dropping attacks detection in mobile Ad hoc networks

Denial of service (DoS) attacks detection in MANETs using Bayesian classifiers

Flooding attacks detection in MANETs

Graph-based wormhole attack detection in mobile ad hoc networks (MANETs)

Denial of Service (DoS) attacks detection in MANETs through statistical models