Showing items 1 - 16 of 16

Your selections:

Trustworthiness of self-driving vehicles for intelligent transportation systems in industry applications

  • Authors: Chowdhury, Abdullahi , Karmakar, Gour , Kamruzzaman, Joarder , Islam, Syed
  • Date: 2021
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Industrial Informatics Vol. 17, no. 2 (2021), p. 961-970
  • Full Text: false
  • Reviewed:
  • Description: 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. © 2005-2012 IEEE.

Detecting intrusion in the traffic signals of an intelligent traffic system

  • Authors: Chowdhury, Abdullahi , Karmakar, Gour , Kamruzzaman, Joarder , Saha, Tapash
  • Date: 2018
  • Type: Text , Conference proceedings
  • Relation: 20th International Conference on Information and Communications Security, ICICS 2018; Lille, France; 29th-31st October 2018; published in Lecure Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11149 LNCS, p. 696-707
  • Full Text: false
  • Reviewed:
  • Description: Traffic systems and signals are used to improve traffic flow, reduce congestion, increase travel time consistency and ensure safety of road users. Malicious interruption or manipulation of traffic signals may cause disastrous instants including huge delays, financial loss and loss of lives. Intrusion into traffic signals by hackers can create such interruption whose consequences will only increase with the introduction of driverless vehicles. Recently, many traffic signals across the world are reported to have intruded, highlighting the importance of accurate detection. To reduce the impact of an intrusion, in this paper, we introduce an intrusion detection technique using the flow rate and phase time of a traffic signal as evidential information to detect the presence of an intrusion. The information received from flow rate and phase time are fused with the Dempster Shaffer (DS) theory. Historical data are used to create the probability mass functions for both flow rate and phase time. We also developed a simulation model using a traffic simulator, namely SUMO for many types of real traffic situations including intrusion. The performance of the proposed Intrusion Detection System (IDS) is appraised with normal traffic condition and induced intrusions. Simulated results show our proposed system can successfully detect intruded traffic signals from normal signals with significantly high accuracy (above 91%).
  • Description: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Using e-learning to engage unemployed rural women in aquaculture in Bangladesh to reduce poverty

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

  • Authors: Chowdhury, Abdullahi
  • Date: 2016
  • Type: Text , Conference proceedings
  • Relation: 2016 International Conference on Engineering and MIS, ICEMIS 2016; Agadir, Morocco; 22nd-24th September 2016; published in Proceedings - 2016 International Conference on Engineering and MIS, ICEMIS 2016 p. 1-6
  • Full Text: false
  • Reviewed:
  • Description: 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. © 2016 IEEE.
  • Description: Proceedings - 2016 International Conference on Engineering and MIS, ICEMIS 2016

Cyber attacks in mechatronics systems based on Internet of Things

  • Authors: Chowdhury, Abdullahi
  • Date: 2017
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 2017 IEEE International Conference on Mechatronics, ICM 2017; Gippsland, Victoria; 13th-15th February 2017 p. 476-481
  • Full Text: false
  • Reviewed:
  • Description: Internet of things (IoT) systems are becoming multidisciplinary day by day and integrating more and more mechanical, electrical, electronics, control and information disciplines. This integration is making mechatronics systems based on IoT easily available for public uses. Government organisations, different industries, healthcare systems and individual users are using these systems to store different kind of public, private, confidential and classified information. This is attracting cyber attackers to make cyber and cyber physical attacks to these systems. Currently, security policy researchers of both industries and academic institutes are analysing existing cyber attacks and are developing different types of techniques to protect the systems against potential cyber-threats and cyber attacks. This paper analyses the increasing exploitation of IoT based mechatronics system, which has created more opportunities for the current cybercrimes. Contemporary and important mitigation approaches for cyber-crimes have also been articulated in this paper. © 2017 IEEE.
  • Description: Proceedings - 2017 IEEE International Conference on Mechatronics, ICM 2017

Trusted autonomous vehicle : measuring trust using on-board unit data

  • Authors: Chowdhury, Abdullahi , Karmakar, Gour , Kamruzzaman, Joarder
  • Date: 2019
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 18th IEEE International Conference on Trust, Security and Privacy in Computing and Communications/13th IEEE International Conference on Big Data Science and Engineering, TrustCom/BigDataSE 2019 p. 787-792
  • Full Text: false
  • Reviewed:
  • Description: Vehicular Ad-hoc Networks (VANETs) play an essential role in ensuring safe, reliable and faster transportation with the help of an Intelligent Transportation system. The trustworthiness of vehicles in VANETs is extremely important to ensure the authenticity of messages and traffic information transmitted in extremely dynamic topographical conditions where vehicles move at high speed. False or misleading information may cause substantial traffic congestions, road accidents and may even cost lives. Many approaches exist in literature to measure the trustworthiness of GPS data and messages of an Autonomous Vehicle (AV). To the best of our knowledge, they have not considered the trustworthiness of other On-Board Unit (OBU) components of an AV, along with GPS data and transmitted messages, though they have a substantial relevance in overall vehicle trust measurement. In this paper, we introduce a novel model to measure the overall trustworthiness of an AV considering four different OBU components additionally. The performance of the proposed method is evaluated with a traffic simulation model developed by Simulation of Urban Mobility (SUMO) using realistic traffic data and considering different levels of uncertainty. © 2019 IEEE.
  • Description: E1

The co-evolution of cloud and IoT applications : recent and future trends

  • Authors: Chowdhury, Abdullahi , Karmakar, Gour , Kamruzzaman, Joarder
  • Date: 2019
  • Type: Text , Book chapter
  • Relation: Handbook of Research on the IoT, Cloud Computing, and Wireless Network Optimization 11 p. 213-234
  • Full Text: false
  • Reviewed:
  • Description: ICT technologies have contributed to the advances in wireless systems, which provide seamless connectivity for worldwide communication. The growth of interconnected devices and the need to store, manage, and process the data from them has led to increased research on the intersection of the internet of things and cloud computing. The Handbook of Research on the IoT, Cloud Computing, and Wireless Network Optimization is a pivotal reference source that provides the latest research findings and solutions for the design and augmentation of wireless systems and cloud computing. The content within this publication examines data mining, machine learning, and software engineering, and is designed for IT specialists, software engineers, researchers, academicians, industry professionals, and students.

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

Recent cyber security attacks and their mitigation approaches - An overview

  • Authors: Chowdhury, Abdullahi
  • Date: 2016
  • Type: Text , Conference paper
  • Relation: 6th International Conference on Applications and Techniques in Information Security, ATIS 2016; Cairns, Australia; 26th-28th October 2016; published in Applications and Techniques in Information Security (Communications in Computer and Information Science series) Vol. 651, p. 54-65
  • Full Text: false
  • Reviewed:
  • Description: 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. © Springer Nature Singapore Pte Ltd. 2016.

Survey of recent cyber security attacks on robotic systems and their mitigation approaches

  • Authors: Chowdhury, Abdullahi , Karmakar, Gour , Kamruzzaman, Joarder
  • Date: 2017
  • Type: Text , Book chapter
  • Relation: Detecting and mitigating robotic cyber security Risks p. 284-299
  • Full Text: false
  • Reviewed:
  • Description: With the rapid expansion of digital media and the advancement of the artificial intelligence, robotics has drawn the attention of cyber security research community. Robotics systems use many Internet of Things (IoT) devices, web interface, internal and external wireless sensor networks and cellular networks for better communication and smart services. Individuals, industries and governments organisations are facing financial loses, losing time and sensitive data due these cyber attacks. The use these different devices and networks in robotics systems are creating new vulnerabilities and potential risk for cyber attacks. This chapter discusses about the possible cyber attacks and economics losses due to these attacks in robotics systems. In this chapter, we analyse the increasing uses of public and private robots, which has created possibility of having more cyber-crimes. Finally, contemporary and important mitigation approaches for these cyber attacks in robotic systems have been discussed in this chapter. © 2017, IGI Global. All rights reserved.

A smart priority-based traffic control system for emergency vehicles

  • Authors: Karmakar, Gour , Chowdhury, Abdullahi , Kamruzzaman, Joarder , Gondal, Iqbal
  • Date: 2021
  • Type: Text , Journal article
  • Relation: IEEE Sensors Journal Vol. 21, no. 14 (2021), p. 15849-15858
  • Full Text: false
  • Reviewed:
  • Description: Unwanted events on roads, such as incidents and increased traffic jams, can cause human lives and economic loss. For efficient incident management, it is essential to send Emergency Vehicles (EVs) to the incident place as quickly as possible. To reduce incidence clearance time, several approaches exist to provide a clear pathway to EVs mainly fitted with RFID sensors in the urban areas. However, they neither assign priority to the EVs based on the type and severity of an incident nor consider the effect on other on-road traffic. To address this issue, in this paper, we introduce an Emergency Vehicle Priority System (EVPS) by determining the priority level of an EV based on the type and the severity of an incident, and estimating the number of necessary signal interventions while considering the impact of those interventions on the traffic in the roads surrounding the EV's travel path. We present how EVPS determines the priority code and a new algorithm to estimate the number of green signal interventions to attain the quickest incident response while concomitantly reducing impact on others. A simulation model is developed in Simulation of Urban Mobility (SUMO) using the real traffic data of Melbourne, Australia, captured by various sensors. Results show that our system recommends appropriate number of intervention that can reduce emergency response time significantly. © 2001-2012 IEEE.

Distributed denial of service attack detection using machine learning and class oversampling

  • Authors: Shafin, Sakib , Prottoy, Shafin , Abbas, Saif , Hakim, Safayat , Chowdhury, Abdullahi , Rashid, Md Mamanur
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: First International Conference on Applied Intelligence and Informatics, AII 2021, Nottingham, UK, July 30-31, 2021 Vol. 1435, p. 247-259
  • Full Text: false
  • Reviewed:
  • Description: Distributed Denial of Services (DDoS) attack, one of the most dangerous types of cyber attack, has been reported to increase during the COVID-19 pandemic. Machine learning techniques have been proposed in the literature to build models to detect DDoS attacks. Existing works in literature tested their models with old datasets where DDoS attacks are not specific. These works mainly focus on detecting the presence of an attack rather than the type of DDoS attacks. However, detection of the attack type is vital for the review and analysis of enterprise-level security policy. Cyber-attacks are inherently an imbalanced data problem, but none of the models treated DDoS attack detection from this perspective. In this work, we present a machine learning model that takes the imbalance nature of the DDoS attack data into consideration for both presence/absence and the type of DDoS attack detection. Extensive experiment analysis with the recent and DDoS attack-specific dataset shows that the proposed technique can effectively identify DDoS attacks. © 2021, Springer Nature Switzerland AG.

Assessing trust level of a driverless car using deep learning

  • Authors: Karmakar, Gour , Chowdhury, Abdullahi , Das, Rajkumar , Kamruzzaman, Joarder , Islam, Syed
  • Date: 2021
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Intelligent Transportation Systems Vol. 22, no. 7 (2021), p. 4457-4466
  • Full Text: false
  • Reviewed:
  • Description: 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. © 2000-2011 IEEE.

Attacks on self-driving cars and their countermeasures : a survey

  • 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.

Churn prediction in telecom industry using machine learning ensembles with class balancing

  • Authors: Chowdhury, Abdullahi , Kaisar, Shahriar , Rashid, Md Mamunur , Shafin, Sakib , Kamruzzaman, Joarder
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2021, Brisbane, 8-10 December 2021
  • Full Text: false
  • Reviewed:
  • Description: Telecommunication service providers are going through a very competitive and challenging time to retain existing customers by offering new and attractive services (e.g., unlimited local and international calls, high-speed internet, new phones). It is therefore imperative to analyse and predict customer churn behaviour more accurately. One of the major challenges to analyse churn data and build better prediction model is the imbalance nature of the data. Customer behaviour for churn and non-churn scenarios may contain resembling features. Using a single classifier or simple oversampling method to handle data imbalance often struggles to identify the minority (churn) class data. To overcome the issue, we introduce a model that uses sophisticated oversampling technique in conjunction with ensemble methods, namely Random Forest, Gradient Boost, Extreme Gradient Boost, and AdaBoost. The hyperparameters of the baseline ensemble methods and the oversampling methods were tuned in several ways to investigate their impact on prediction performances. Using a widely used publicly available customer churn dataset, prediction performance of the proposed model was evaluated in term of various metrics, namely, accuracy, precision, recall, F-1 score, AUC under ROC curve. Our model outperformed the existing models and significantly reduced both false positive and false negative prediction. © IEEE 2022.

Detection of android malware using tree-based ensemble stacking model

  • Authors: Shafin, Sakib , Ahmed, Md Maroof , Pranto, Mahmud , Chowdhury, Abdullahi
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2021, Brisbane, 8-10 December 2021
  • Full Text: false
  • Reviewed:
  • Description: Increasing use of smartphones for everyday activities from banking, education to social networking is putting our personal information at risk as smartphone operating systems and applications are vulnerable to various types of attacks including malware attack. To this end Android operating system is particularly targeted as it is the most widely used mobile operating system. Building a robust detection system that can provide protection against recent attacks and can deliver not only accurate detection but also the type of the attack in order to protect the system is vital. In this study, we propose a twolayer Machine Learning detection model based on Ensemble Learning and Stacked Generalization method to accurately predict and classify the growing attacks on Android smartphones. We evaluated the proposed model on a very recent dataset, named CIC-Maldroid-2020, which contains 11,598 samples with various malicious attack types. The performance of our proposed model was evaluated on widely used metrics, like accuracy, precision, recall & F1-score. It outperforms previous studies done on the same dataset and achieves an accuracy of 99.49% in classifying each attack type. © IEEE 2022.
  • 1