An evidence theoretic approach for traffic signal intrusion detection
- Chowdhury, Abdullahi, Karmakar, Gour, Kamruzzaman, Joarder, Das, Rajkumar, Newaz, Shah
- Authors: Chowdhury, Abdullahi , Karmakar, Gour , Kamruzzaman, Joarder , Das, Rajkumar , Newaz, Shah
- Date: 2023
- Type: Text , Journal article
- Relation: Sensors Vol. 23, no. 10 (2023), p. 4646
- Full Text:
- Reviewed:
- Description: The increasing attacks on traffic signals worldwide indicate the importance of intrusion detection. The existing traffic signal Intrusion Detection Systems (IDSs) that rely on inputs from connected vehicles and image analysis techniques can only detect intrusions created by spoofed vehicles. However, these approaches fail to detect intrusion from attacks on in-road sensors, traffic controllers, and signals. In this paper, we proposed an IDS based on detecting anomalies associated with flow rate, phase time, and vehicle speed, which is a significant extension of our previous work using additional traffic parameters and statistical tools. We theoretically modelled our system using the Dempster-Shafer decision theory, considering the instantaneous observations of traffic parameters and their relevant historical normal traffic data. We also used Shannon's entropy to determine the uncertainty associated with the observations. To validate our work, we developed a simulation model based on the traffic simulator called SUMO using many real scenarios and the data recorded by the Victorian Transportation Authority, Australia. The scenarios for abnormal traffic conditions were generated considering attacks such as jamming, Sybil, and false data injection attacks. The results show that the overall detection accuracy of our proposed system is 79.3% with fewer false alarms.
- Authors: Chowdhury, Abdullahi , Karmakar, Gour , Kamruzzaman, Joarder , Das, Rajkumar , Newaz, Shah
- Date: 2023
- Type: Text , Journal article
- Relation: Sensors Vol. 23, no. 10 (2023), p. 4646
- Full Text:
- Reviewed:
- Description: The increasing attacks on traffic signals worldwide indicate the importance of intrusion detection. The existing traffic signal Intrusion Detection Systems (IDSs) that rely on inputs from connected vehicles and image analysis techniques can only detect intrusions created by spoofed vehicles. However, these approaches fail to detect intrusion from attacks on in-road sensors, traffic controllers, and signals. In this paper, we proposed an IDS based on detecting anomalies associated with flow rate, phase time, and vehicle speed, which is a significant extension of our previous work using additional traffic parameters and statistical tools. We theoretically modelled our system using the Dempster-Shafer decision theory, considering the instantaneous observations of traffic parameters and their relevant historical normal traffic data. We also used Shannon's entropy to determine the uncertainty associated with the observations. To validate our work, we developed a simulation model based on the traffic simulator called SUMO using many real scenarios and the data recorded by the Victorian Transportation Authority, Australia. The scenarios for abnormal traffic conditions were generated considering attacks such as jamming, Sybil, and false data injection attacks. The results show that the overall detection accuracy of our proposed system is 79.3% with fewer false alarms.
IoT-based emergency vehicle services in intelligent transportation system
- Chowdhury, Abdullahi, Kaisar, Shahriar, Khoda, Mahbub, Naha, Ranesh, Khoshkholghi, Mohammad, Aiash, Mahdi
- Authors: Chowdhury, Abdullahi , Kaisar, Shahriar , Khoda, Mahbub , Naha, Ranesh , Khoshkholghi, Mohammad , Aiash, Mahdi
- Date: 2023
- Type: Text , Journal article
- Relation: Sensors Vol. 23, no. 11 (2023), p. 5324
- Full Text:
- Reviewed:
- Description: Emergency Management System (EMS) is an important component of Intelligent transportation systems, and its primary objective is to send Emergency Vehicles (EVs) to the location of a reported incident. However, the increasing traffic in urban areas, especially during peak hours, results in the delayed arrival of EVs in many cases, which ultimately leads to higher fatality rates, increased property damage, and higher road congestion. Existing literature addressed this issue by giving higher priority to EVs while traveling to an incident place by changing traffic signals (e.g., making the signals green) on their travel path. A few works have also attempted to find the best route for an EV using traffic information (e.g., number of vehicles, flow rate, and clearance time) at the beginning of the journey. However, these works did not consider congestion or disruption faced by other non-emergency vehicles adjacent to the EV travel path. The selected travel paths are also static and do not consider changing traffic parameters while EVs are en route. To address these issues, this article proposes an Unmanned Aerial Vehicle (UAV) guided priority-based incident management system to assist EVs in obtaining a better clearance time in intersections and thus achieve a lower response time. The proposed model also considers disruption faced by other surrounding non-emergency vehicles adjacent to the EVs' travel path and selects an optimal solution by controlling the traffic signal phase time to ensure that EVs can reach the incident place on time while causing minimal disruption to other on-road vehicles. Simulation results indicate that the proposed model achieves an 8% lower response time for EVs while the clearance time surrounding the incident place is improved by 12%.
- Authors: Chowdhury, Abdullahi , Kaisar, Shahriar , Khoda, Mahbub , Naha, Ranesh , Khoshkholghi, Mohammad , Aiash, Mahdi
- Date: 2023
- Type: Text , Journal article
- Relation: Sensors Vol. 23, no. 11 (2023), p. 5324
- Full Text:
- Reviewed:
- Description: Emergency Management System (EMS) is an important component of Intelligent transportation systems, and its primary objective is to send Emergency Vehicles (EVs) to the location of a reported incident. However, the increasing traffic in urban areas, especially during peak hours, results in the delayed arrival of EVs in many cases, which ultimately leads to higher fatality rates, increased property damage, and higher road congestion. Existing literature addressed this issue by giving higher priority to EVs while traveling to an incident place by changing traffic signals (e.g., making the signals green) on their travel path. A few works have also attempted to find the best route for an EV using traffic information (e.g., number of vehicles, flow rate, and clearance time) at the beginning of the journey. However, these works did not consider congestion or disruption faced by other non-emergency vehicles adjacent to the EV travel path. The selected travel paths are also static and do not consider changing traffic parameters while EVs are en route. To address these issues, this article proposes an Unmanned Aerial Vehicle (UAV) guided priority-based incident management system to assist EVs in obtaining a better clearance time in intersections and thus achieve a lower response time. The proposed model also considers disruption faced by other surrounding non-emergency vehicles adjacent to the EVs' travel path and selects an optimal solution by controlling the traffic signal phase time to ensure that EVs can reach the incident place on time while causing minimal disruption to other on-road vehicles. Simulation results indicate that the proposed model achieves an 8% lower response time for EVs while the clearance time surrounding the incident place is improved by 12%.
Attacks on self-driving cars and their countermeasures : a survey
- Chowdhury, Abdullahi, Karmakar, Gour, Kamruzzaman, Joarder, Jolfaei, Alireza, Das, Rajkumar
- Authors: Chowdhury, Abdullahi , Karmakar, Gour , Kamruzzaman, Joarder , Jolfaei, Alireza , Das, Rajkumar
- Date: 2020
- Type: Text , Journal article , Review
- Relation: IEEE Access Vol. 8, no. (2020), p. 207308-207342
- Full Text:
- Reviewed:
- Description: 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. © 2013 IEEE.
- Authors: Chowdhury, Abdullahi , Karmakar, Gour , Kamruzzaman, Joarder , Jolfaei, Alireza , Das, Rajkumar
- Date: 2020
- Type: Text , Journal article , Review
- Relation: IEEE Access Vol. 8, no. (2020), p. 207308-207342
- Full Text:
- Reviewed:
- Description: 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. © 2013 IEEE.
Enhancing service quality and reliability in intelligent traffic system
- Authors: Chowdhury, Abdullahi
- Date: 2020
- Type: Text , Thesis , PhD
- Full Text:
- Description: Intelligent Traffic Systems (ITS) can manage on-road traffic efficiently based on real-time traffic conditions, reduce delay at the intersections, and maintain the safety of the road users. However, emergency vehicles still struggle to meet their targeted response time, and an ITS is vulnerable to various types of attacks, including cyberattacks. To address these issues, in this dissertation, we introduce three techniques that enhance the service quality and reliability of an ITS. First, an innovative Emergency Vehicle Priority System (EVPS) is presented to assist an Emergency Vehicle (EV) in attending the incident place faster. Our proposed EVPS determines the proper priority codes of EV based on the type of incidents. After priority code generation, EVPS selects the number of traffic signals needed to be turned green considering the impact on other vehicles gathered in the relevant adjacent cells. Second, for improving reliability, an Intrusion Detection System for traffic signals is proposed for the first time, which leverages traffic and signal characteristics such as the flow rate, vehicle speed, and signal phase time. Shannon’s entropy is used to calculate the uncertainty associated with the likelihood of particular evidence and Dempster-Shafer (DS) decision theory is used to fuse the evidential information. Finally, to improve the reliability of a future ITS, we introduce a model that assesses the trust level of four major On-Board Units (OBU) of a self-driving car along with Global Positioning System (GPS) data and safety messages. Both subjective logic (DS theory) and CertainLogic are used to develop the theoretical underpinning for estimating the trust value of a self-driving car by fusing the trust value of four OBU components, GPS data and safety messages. For evaluation and validation purposes, a popular and widely used traffic simulation package, namely Simulation of Urban Mobility (SUMO), is used to develop the simulation platform using a real map of Melbourne CBD. The relevant historical real data taken from the VicRoads website were used to inject the traffic flow and density in the simulation model. We evaluated the performance of our proposed techniques considering different traffic and signal characteristics such as occupancy rate, flow rate, phase time, and vehicle speed under many realistic scenarios. The simulation result shows the potential efficacy of our proposed techniques for all selected scenarios.
- Description: Doctor of Philosophy
- Authors: Chowdhury, Abdullahi
- Date: 2020
- Type: Text , Thesis , PhD
- Full Text:
- Description: Intelligent Traffic Systems (ITS) can manage on-road traffic efficiently based on real-time traffic conditions, reduce delay at the intersections, and maintain the safety of the road users. However, emergency vehicles still struggle to meet their targeted response time, and an ITS is vulnerable to various types of attacks, including cyberattacks. To address these issues, in this dissertation, we introduce three techniques that enhance the service quality and reliability of an ITS. First, an innovative Emergency Vehicle Priority System (EVPS) is presented to assist an Emergency Vehicle (EV) in attending the incident place faster. Our proposed EVPS determines the proper priority codes of EV based on the type of incidents. After priority code generation, EVPS selects the number of traffic signals needed to be turned green considering the impact on other vehicles gathered in the relevant adjacent cells. Second, for improving reliability, an Intrusion Detection System for traffic signals is proposed for the first time, which leverages traffic and signal characteristics such as the flow rate, vehicle speed, and signal phase time. Shannon’s entropy is used to calculate the uncertainty associated with the likelihood of particular evidence and Dempster-Shafer (DS) decision theory is used to fuse the evidential information. Finally, to improve the reliability of a future ITS, we introduce a model that assesses the trust level of four major On-Board Units (OBU) of a self-driving car along with Global Positioning System (GPS) data and safety messages. Both subjective logic (DS theory) and CertainLogic are used to develop the theoretical underpinning for estimating the trust value of a self-driving car by fusing the trust value of four OBU components, GPS data and safety messages. For evaluation and validation purposes, a popular and widely used traffic simulation package, namely Simulation of Urban Mobility (SUMO), is used to develop the simulation platform using a real map of Melbourne CBD. The relevant historical real data taken from the VicRoads website were used to inject the traffic flow and density in the simulation model. We evaluated the performance of our proposed techniques considering different traffic and signal characteristics such as occupancy rate, flow rate, phase time, and vehicle speed under many realistic scenarios. The simulation result shows the potential efficacy of our proposed techniques for all selected scenarios.
- Description: Doctor of Philosophy
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