Showing items 1 - 20 of 25

A smart priority-based traffic control system for emergency vehicles

- Karmakar, Gour, Chowdhury, Abdullahi, Kamruzzaman, Joarder, Gondal, Iqbal

  • 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
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  • 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.

AI-powered resilience : addressing the mental health impact of mass layoffs in the digital age

- Avik, Sunranjeet, Chowdhury, Abdullahi, Moriwam, Maruf, Md Abdullah, Naha, Ranesh, Ahammad, Imtiaz

  • Authors: Avik, Sunranjeet , Chowdhury, Abdullahi , Moriwam , Maruf, Md Abdullah , Naha, Ranesh , Ahammad, Imtiaz
  • Date: 2024
  • Type: Text , Conference paper
  • Relation: 2nd International Conference on Emerging Trends in Information Technology and Engineering, ic-ETITE 2024, Vellore, India, 22-23 February 2024, 2nd International Conference on Emerging Trends in Information Technology and Engineering, ic-ETITE 2024
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  • Description: In recent years, the technology sector has witnessed substantial disruptions, leading to major layoffs within toptier tech conglomerates. As the momentum of the digital age continues, there's an emerging concern regarding the psycho-logical impact of these layoffs on individual employees. This research delves deep into understanding the repercussions of these widespread job losses on mental health, emphasizing the increased intensity of symptoms like depression and anxiety. Utilizing the Beck Depression Inventory (BDI) for its methodology, the study scrupulously evaluates the depth of these mental health symptoms before and subsequent to the implementation of AI-driven mental health solutions. The conclusive data robustly establishes that when AI-centric interventions are combined with holistic mental health practices, there's a marked reduction in depression and anxiety symptoms, particularly those originating from job-related insecurities. As the world grapples with the swift changes in employment dynamics in this digital age, the study accentuates the significance of AI. Tapping into its vast potential to enhance mental resilience can be seen as an avant-garde strategy, presenting a pragmatic way to address the looming mental health challenges arising from unpredictable economic transitions. © 2024 IEEE.

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
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  • 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.

Assessing trust level of a driverless car using deep learning

- Karmakar, Gour, Chowdhury, Abdullahi, Das, Rajkumar, Kamruzzaman, Joarder, Islam, Syed

  • 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
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  • 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

- 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
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  • 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

- Chowdhury, Abdullahi, Kaisar, Shahriar, Rashid, Md Mamunur, Shafin, Sakib, Kamruzzaman, Joarder

  • 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
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  • 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.

Comprehensive analysis of feature extraction techniques and runtime performance evaluation for phishing detection

- Nath, Subrata, Islam, Mohammad, Chowdhury, Abdullahi, Rashid, Mohammad, Islam, Maheen, Jabid, Taskeed, Naha, Ranesh

Coral reef surveillance with machine learning : a review of datasets, techniques, and challenges

- Chowdhury, Abdullahi, Jahan, Musfera, Kaisar, Shahriar, Khoda, Mahbub, Rajin, S., Naha, Ranesh

  • Authors: Chowdhury, Abdullahi , Jahan, Musfera , Kaisar, Shahriar , Khoda, Mahbub , Rajin, S. , Naha, Ranesh
  • Date: 2024
  • Type: Text , Journal article , Review
  • Relation: Electronics (Switzerland) Vol. 13, no. 24 (2024), p.
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  • Description: Climate change poses a significant threat to our planet, particularly affecting intricate marine ecosystems like coral reefs. These ecosystems are crucial for biodiversity and serve as indicators of the overall health of our oceans. To better understand and predict these changes, this paper discusses a multidisciplinary technical approach incorporating machine learning, artificial intelligence (AI), geographic information systems (GIS), and remote sensing techniques. We focus primarily on the changes that occur in coral reefs over time, taking into account biological components, geographical considerations, and challenges stemming from climate change. We investigate the application of GIS technology in coral reef studies, analyze publicly available datasets from various organisations such as the National Oceanic and Atmospheric Administration (NOAA), the Monterey Bay Aquarium Research Institute, and the Hawaii Undersea Research Laboratory, and present the use of machine and deep learning models in coral reef surveillance. This article examines the application of GIS in coral reef studies across various contexts, identifying key research gaps, particularly the lack of a comprehensive catalogue of publicly available datasets. Additionally, it reviews the existing literature on machine and deep learning techniques for coral reef surveillance, critically evaluating their contributions and limitations. The insights provided in this work aim to guide future research, fostering advancements in coral reef monitoring and conservation. © 2024 by the authors.

Cyber attacks in mechatronics systems based on Internet of Things

- Chowdhury, Abdullahi

  • 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
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  • 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

Detecting intrusion in the traffic signals of an intelligent traffic system

- Chowdhury, Abdullahi, Karmakar, Gour, Kamruzzaman, Joarder, Saha, Tapash

  • 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
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  • 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)

Detection of android malware using tree-based ensemble stacking model

- Shafin, Sakib, Ahmed, Md Maroof, Pranto, Mahmud, Chowdhury, Abdullahi

  • 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
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  • 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.

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

- Shafin, Sakib, Prottoy, Shafin, Abbas, Saif, Hakim, Safayat, Chowdhury, Abdullahi, Rashid, Md Mamanur

  • 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
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  • 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.

Enhancing service quality and reliability in intelligent traffic system

- Chowdhury, Abdullahi

  • Authors: Chowdhury, Abdullahi
  • Date: 2020
  • Type: Text , Thesis , PhD
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  • 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

Enhancing telemarketing success using ensemble-based online machine learning

- Kaisar, Shahriar, Rashid, Md Mamunur, Chowdhury, Abdullahi, Shafin, Sakib, Kamruzzaman, Joarder, Diro, Abebe

  • Authors: Kaisar, Shahriar , Rashid, Md Mamunur , Chowdhury, Abdullahi , Shafin, Sakib , Kamruzzaman, Joarder , Diro, Abebe
  • Date: 2024
  • Type: Text , Journal article
  • Relation: Big Data Mining and Analytics Vol. 7, no. 2 (2024), p. 294-314
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  • Description: Telemarketing is a well-established marketing approach to offering products and services to prospective customers. The effectiveness of such an approach, however, is highly dependent on the selection of the appropriate consumer base, as reaching uninterested customers will induce annoyance and consume costly enterprise resources in vain while missing interested ones. The introduction of business intelligence and machine learning models can positively influence the decision-making process by predicting the potential customer base, and the existing literature in this direction shows promising results. However, the selection of influential features and the construction of effective learning models for improved performance remain a challenge. Furthermore, from the modelling perspective, the class imbalance nature of the training data, where samples with unsuccessful outcomes highly outnumber successful ones, further compounds the problem by creating biased and inaccurate models. Additionally, customer preferences are likely to change over time due to various reasons, and/or a fresh group of customers may be targeted for a new product or service, necessitating model retraining which is not addressed at all in existing works. A major challenge in model retraining is maintaining a balance between stability (retaining older knowledge) and plasticity (being receptive to new information). To address the above issues, this paper proposes an ensemble machine learning model with feature selection and oversampling techniques to identify potential customers more accurately. A novel online learning method is proposed for model retraining when new samples are available over time. This newly introduced method equips the proposed approach to deal with dynamic data, leading to improved readiness of the proposed model for practical adoption, and is a highly useful addition to the literature. Extensive experiments with real-world data show that the proposed approach achieves excellent results in all cases (e.g., 98.6% accuracy in classifying customers) and outperforms recent competing models in the literature by a considerable margin of 3% on a widely used dataset. © 2018 Tsinghua University Press.

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
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  • 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%.

Leveraging oversampling techniques in machine learning models for multi-class malware detection in smart home applications

- Chowdhury, Abdullahi, Islam, Mohammad, Kaisar, Shahriar, Khoda, Mahbub, Naha, Ranesh, Khoshkholghi, Mohammad, Aiash, Mahdi

Performance and cryptographic evaluation of security protocols in distributed networks using applied pi calculus and Markov Chain

- Edris, Ed, Aiash, Mahdi, Khoshkholghi, Mohammad, Naha, Ranesh, Chowdhury, Abdullahi, Loo, Jonathan

  • Authors: Edris, Ed , Aiash, Mahdi , Khoshkholghi, Mohammad , Naha, Ranesh , Chowdhury, Abdullahi , Loo, Jonathan
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Internet of Things (Netherlands) Vol. 24, no. (2023), p.
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  • Description: The development of cryptographic protocols goes through two stages, namely, security verification and performance analysis. The verification of the protocol's security properties could be analytically achieved using threat modelling, or formally using formal methods and model checkers. The performance analysis could be mathematical or simulation-based. However, mathematical modelling is complicated and does not reflect the actual deployment environment of the protocol in the current state of the art. Simulation software provides scalability and can simulate complicated scenarios, however, there are times when it is not possible to use simulations due to a lack of support for new technologies or simulation scenarios. Therefore, this paper proposes a formal method and analytical model for evaluating the performance of security protocols using applied pi-calculus and Markov Chain processes. It interprets algebraic processes and associates cryptographic operatives with quantitative measures to estimate and evaluate cryptographic costs. With this approach, the protocols are presented as processes using applied pi-calculus, and their security properties are an approximate abstraction of protocol equivalence based on the verification from ProVerif and evaluated using analytical and simulation models for quantitative measures. The interpretation of the quantities is associated with process transitions, rates, and measures as a cost of using cryptographic primitives. This method supports users’ input in analysing the protocol's activities and performance. As a proof of concept, we deploy this approach to assess the performance of security protocols designed to protect large-scale, 5G-based Device-to-Device communications. We also conducted a performance evaluation of the protocols based on analytical and network simulator results to compare the effectiveness of the proposed approach. © 2023 The Author(s)

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

- Chowdhury, Abdullahi

  • 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
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  • 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

Recent advancements in IoT security-based challenges : a brief review

- Avik, Suranjeet, Chowdhury, Abdullahi, Naha, Ranesh, Kaisar, Shahriar, Arulappan, Arunkumar, Mahanti, Aniket

Recent cyber security attacks and their mitigation approaches - An overview

- Chowdhury, Abdullahi

  • 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
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  • 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.