The Internet of Things (IoT) has been rapidly evolving towards making a greater impact on everyday life to large industrial systems. Unfortunately, this has attracted the attention of cybercriminals who made IoT a target of malicious activities, opening the door to a possible attack to the end nodes. Due to the large number and diverse types of IoT devices, it is a challenging task to protect the IoT infrastructure using a traditional intrusion detection system. To protect IoT devices, a novel ensemble Hybrid Intrusion Detection System (HIDS) is proposed by combining a C5 classifier and One Class Support Vector Machine classifier. HIDS combines the advantages of Signature Intrusion Detection System (SIDS) and Anomaly-based Intrusion Detection System (AIDS). The aim of this framework is to detect both the well-known intrusions and zero-day attacks with high detection accuracy and low false-alarm rates. The proposed HIDS is evaluated using the Bot-IoT dataset, which includes legitimate IoT network traffic and several types of attacks. Experiments show that the proposed hybrid IDS provide higher detection rate and lower false positive rate compared to the SIDS and AIDS techniques.

A novel ensemble of hybrid intrusion detection system for detecting internet of things attacks

A novel Ensemble of Hybrid Intrusion Detection System for Detecting Internet of Things Attacks

Evolution of IoT-enabled connectivity and applications in automotive industry: A review

By the dramatic growth of the population in cities requires the traffic systems to be designed efficiently and sustainably by taking full advantage of modern-day technology. Dynamic traffic flow is a significant issue which brings about a block of traffic movement. Thus, for tackling this issue, this paper aims to provide a mechanism to predict the traffic congestion with the help of Artificial Neural Networks (ANN) which shall control or minimize the blockage and result in the smoothening of road traffic. Proposed Modeling Smart Road Traffic Congestion Control using Artificial Back Propagation Neural Networks (MSR2C-ABPNN) for road traffic increase transparency, availability and efficiency in services offered to the citizens. In this paper, the prediction of congestion is operationalized by using the algorithm of backpropagation to train the neural network. The proposed system aims to provide a solution that will increase the comfort level of travellers to make intelligent and better transportation decision, and the neural network is a plausible approach to find traffic situations. Proposed MSR2C-ABPNN with Time series gives attractive results concerning MSE as compared to the fitting approach.

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Modelling smart road traffic congestion control system using machine learning techniques

The Internet of Things (IoT) paradigm is being rapidly adopted for the creation of smart environments in various domains. The IoT-enabled cyber-physical systems associated with smart city, healthcare, Industry 4.0 and Agtech handle a huge volume of data and require data processing services from different types of applications in real time. The Cloud-centric execution of IoT applications barely meets such requirements as the Cloud datacentres reside at a multi-hop distance from the IoT devices. Fog computing, an extension of Cloud at the edge network, can execute these applications closer to data sources. Thus, Fog computing can improve application service delivery time and resist network congestion. However, the Fog nodes are highly distributed and heterogeneous, and most of them are constrained in resources and spatial sharing. Therefore, efficient management of applications is necessary to fully exploit the capabilities of Fog nodes. In this work, we investigate the existing application management strategies in Fog computing and review them in terms of architecture, placement and maintenance. Additionally, we propose a comprehensive taxonomy and highlight the research gaps in Fog-based application management. We also discuss a perspective model and provide future research directions for further improvement of application management in Fog computing.

https://dl.acm.org/doi/pdf/10.1145/3403955

Application Management in Fog Computing Environments: A Taxonomy, Review and Future Directions

Intelligent Traffic Systems (ITS) are currently evolving in the form of a cooperative ITS or connected vehicles. Both forms use the data communications between Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I/I2V) and other on-road entities, and are accelerating the adoption of self-driving cars. The development of cyber-physical systems containing advanced sensors, sub-systems, and smart driving assistance applications over the past decade is equipping unmanned aerial and road vehicles with autonomous decision-making capabilities. The level of autonomy depends upon the make-up and degree of sensor sophistication and the vehicle’s operational applications. As a result, self-driving cars are being compromised perceived as a serious threat. Therefore, analyzing the threats and attacks on self-driving cars and ITSs, and their corresponding countermeasures to reduce those threats and attacks are needed. For this reason, some survey papers compiling potential attacks on VANETs, ITSs and self-driving cars, and their detection mechanisms are available in the current literature. However, up to our knowledge, they have not covered the real attacks already happened in self-driving cars. To bridge this research gap, in this paper, we analyze the attacks that already targeted self-driving cars and extensively present potential cyber-attacks and their impacts on those cars along with their vulnerabilities. For recently reported attacks, we describe the possible mitigation strategies taken by the manufacturers and governments. This survey includes recent works on how a self-driving car can ensure resilient operation even under ongoing cyber-attack. We also provide further research directions to improve the security issues associated with self-driving cars.

Attacks on Self-Driving Cars and Their Countermeasures: A Survey

To enhance industrial production and automation, rapid and faster transportation of raw materials and finished products to and from distributed factories, warehouses and outlets are essential. To reduce cost with increased efficiency, this will increasingly see the use of connected and self-driving commercial vehicles fitted with industrial grade sensors on roads, shared with normal and self-driving passenger vehicles. For its wide adoption, the trustworthiness of self-driving vehicles in the intelligent transportation system (ITS) is pivotal. In this article, we introduce a novel model to measure the overall trustworthiness of a self-driving vehicle considering on-Board unit (OBU) components, GPS data and safety messages. In calculating the trustworthiness of individual OBU components, CertainLogic and beta distribution function (BDF) are used. Those trust values are fused using both the dempster–Shafer Theory (DST) and a logical operator of CertainLogic. Results of our simulation show that our proposed method can effectively determine the trust of self-driving vehicles.

Trustworthiness of Self-Driving Vehicles for Intelligent Transportation Systems in Industry Applications

Intelligent Traffic System (ITS) is one of the most recent research topics in the Internet of Things (IoT). The ever increasing number of vehicles in modern cities is creating heavy traffic congestion. To reduce the traffic congestion, a number of research have already been done to provide a clear pathway to the emergency vehicles in the urban area. However, they often fail to meet the target travel time of an emergency vehicle set by the Department of Treasury and Finances Budget and Financial Management Guidance (BFMG). To address this issue directly, an innovative ITS system considering the priorities of emergency vehicles based on the type of an incident and a method for detecting and responding to the hacking of traffic signals have been proposed in this paper. An experiment using a simulation software, namely Simulation of Urban Mobility (SUMO) was conducted. The simulation results have exhibited superior performance of our proposed system over the currently operational and recently proposed ITS for emergency vehicles, in terms of both congestion avoidance and travel time. The response time attained by our scheme meets the target set by BFMG for both normal and hacked traffic signals.

Priority based and secured traffic management system for emergency vehicle using IoT

The advent of digital media, Internet, web and online social media has drawn the attention of relevant research community significantly and created many new research challenges on cyber security. People, organisations and governments around the world are losing a huge amount of money because of having cyber-attacks. For this reason, cyber security has become one of the most difficult and significant problems across the world. Currently, cyber security researchers of both industries and academic institutes are analysing existing cyber-attacks happening across the world and are developing different types of techniques to protect the systems against potential cyber-threats and attacks. This paper discusses the recent cyber security-attacks and the economic loss resulted from the growing cyber-attacks. This paper also analyses the increasing exploitation of a computer system, which has created more opportunities for the current cyber-crimes. Protective mechanisms and relevant laws are being implemented to reduce cyber- crimes around the world. Contemporary and important mitigation approaches for cyber-crimes have also been articulated in this paper.

Recent Cyber Security Attacks and Their Mitigation Approaches – An Overview

The increasing adoption of driverless cars already providing a shift to move away from traditional transportation systems to automated ones in many industrial and commercial applications. Recent research has justified that driverless vehicles will considerably reduce traffic congestions, accidents, carbon emissions, and enhance the accessibility of driving to wider cross-section of people and lifestyle choices. However, at present, people’s main concerns are about its privacy and security. Since traditional protocol layers based security mechanisms are not so effective for a distributed system, trust value-based security mechanisms, a type of pervasive security, are appearing as popular and promising techniques. A few statistical non-learning based models for measuring the trust level of a driverless are available in the current literature. These are not so effective because of not being able to capture the extremely distributed, dynamic, and complex nature of the traffic systems. To bridge this research gap, in this paper, for the first time, we propose two deep learning-based models that measure the trustworthiness of a driverless car and its major On-Board Unit (OBU) components. The second model also determines its OBU components that were breached during the driving operation. Results produced using real and simulated traffic data demonstrate that our proposed DNN based deep learning models outperform other machine learning models in assessing the trustworthiness of individual car as well as its OBU components. The average precision of detection accuracies for the car, LiDAR, camera, and radar are 0.99, 0.96, 0.81, and 0.83, respectively, which indicates the potential real-life application of our models in assessing the trust level of a driverless car.

Abdullahi Chowdhury

Papers

Attacks on Self-Driving Cars and Their Countermeasures: A Survey

Trustworthiness of Self-Driving Vehicles for Intelligent Transportation Systems in Industry Applications

Priority based and secured traffic management system for emergency vehicle using IoT

Recent Cyber Security Attacks and Their Mitigation Approaches – An Overview

Assessing Trust Level of a Driverless Car Using Deep Learning