Abdulla Al-Ali - Academia.edu (original) (raw)

Papers by Abdulla Al-Ali

Sensors

Drones are becoming increasingly popular not only for recreational purposes but in day-to-day app... more Drones are becoming increasingly popular not only for recreational purposes but in day-to-day applications in engineering, medicine, logistics, security and others. In addition to their useful applications, an alarming concern in regard to the physical infrastructure security, safety and privacy has arisen due to the potential of their use in malicious activities. To address this problem, we propose a novel solution that automates the drone detection and identification processes using a drone’s acoustic features with different deep learning algorithms. However, the lack of acoustic drone datasets hinders the ability to implement an effective solution. In this paper, we aim to fill this gap by introducing a hybrid drone acoustic dataset composed of recorded drone audio clips and artificially generated drone audio samples using a state-of-the-art deep learning technique known as the Generative Adversarial Network. Furthermore, we examine the effectiveness of using drone audio with dif...

Eurasia Journal of Mathematics, Science and Technology Education

This study examined engineering students' initial readiness to transition to emergency online lea... more This study examined engineering students' initial readiness to transition to emergency online learning in response to COVID-19 in Qatar. A theoretical framework is proposed for understanding the factors influencing students' readiness for change. Sequential explanatory mixed-method research was conducted, with 140 participants completing an online survey, of which 68 also contributed written reflections and 8 participated in semi-structured interviews. Exploratory factor analysis displayed a four-factor structure, including initial preparedness and motivation for online learning, self-efficacy beliefs about online learning, self-directed learning online, and support. The qualitative outcomes supported the four factors and provided further insight into their varied and nuanced manifestation. In accounting for the perceived impact of the factors on readiness, significant differences were identified regarding pedagogical mode, with students enrolled in PBL courses reporting higher readiness than those from non-PBL courses. The practical implications for preparing students for future emergency online learning are discussed.

Eurasia Journal of Mathematics, Science and Technology Education

Background: While improved student engagement has been highlighted as an essential goal and a maj... more Background: While improved student engagement has been highlighted as an essential goal and a major outcome of Problem and Project-Based learning (PBL), little empirical evidence has been provided regarding types and forms of student engagement. Material and method: The study explored forms of student engagement in PBL settings, drawing on empirical data of observations and group interviews with 23 project teams (116 students) in four different PBL undergraduate civil engineering courses at Qatar University. Results: The study identified four patterns of student engagement in a PBL setting. Participants reported significant indicators of the first two patterns-engagement as autonomy and as connection. Regarding the other two indicators, namely relational and emotional engagement, they reported positive yet slightly fewer indicators. Three factors were identified that influenced student engagement in a project teams, namely PBL types and its appropriateness to the nature of the course, students' prior experiences with PBL, and team dynamics. Conclusions: These results facilitate the establishment of an institutional framework supporting a progressive approach to embracing PBL. In this framework PBL implementation begins with diverse practices at the course level and has systemic change as its ultimate goal. This framework particularly aims to support an institutionalized approach to transition to PBL in a socio-cultural context (e.g., a non-western context) where instructors are as the primary and authoritative source of knowledge. The overall outcome of the study supports management of change from a lecturebased mode to PBL in a non-western context.

Human-centric Computing and Information Sciences

The term 'navigation' collectively represent tasks that include tracking the user's position, pla... more The term 'navigation' collectively represent tasks that include tracking the user's position, planning feasible routes and guiding the user through the routes to reach the desired destination. In the past, considerable number of navigation systems were developed for accessing outdoor and indoor environments. Most of the outdoor navigation systems adopt GPS and Global Navigation Satellite System (GLONASS) to track the user's position. Important applications of outdoor navigation systems include wayfinding for vehicles, pedestrians, and blind people [1, 2]. In indoor environments, the GPS cannot provide fair accuracy in tracking due to nonline of sight issues [3]. This limitation hinders the implementation of GPS in indoor navigation systems, although it can be solved by using "high-sensitivity GPS receivers or GPS pseudolites" [4]. However, the cost of implementation can be a barrier to applying this system in real-world scenarios.

IEEE Communications Surveys & Tutorials

The Internet of Things (IoT) integrates billions of smart devices that can communicate with one a... more The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. It is one of the fastest developing fields in the history of computing, with an estimated 50 billion devices by the end of 2020. On the one hand, IoT technologies play a crucial role in enhancing several real-life smart applications that can improve life quality. On the other hand, the crosscutting nature of IoT systems and the multidisciplinary components involved in the deployment of such systems have introduced new security challenges. Implementing security measures, such as encryption, authentication, access control, network security and application security, for IoT devices and their inherent vulnerabilities is ineffective. Therefore, existing security methods should be enhanced to secure the IoT ecosystem effectively. Machine learning and deep learning (ML/DL) have advanced considerably over the last few years, and machine intelligence has transitioned from laboratory curiosity to practical machinery in several important applications. The ability to monitor IoT devices intelligently provides a significant solution to new or zero-day attacks. ML/DL are powerful methods of data exploration for learning about 'normal' and 'abnormal' behaviour according to how IoT components and devices perform within the IoT environment. Consequently, ML/DL methods are important in transforming the security of IoT systems from merely facilitating secure communication between devices to security-based intelligence systems. The goal of this work is to provide a comprehensive survey of ML methods and recent advances in DL methods that can be used to develop enhanced security methods for IoT systems. IoT security threats that are related to inherent or newly introduced threats are presented, and various potential IoT system attack surfaces and the possible threats related to each surface are discussed. We then thoroughly review ML/DL methods for IoT security and present the opportunities, advantages and shortcomings of each method. We discuss the opportunities and challenges involved in applying ML/DL to IoT security. These opportunities and challenges can serve as potential future research directions.

IEEE Access

The surge in demand for Internet of Things (IoT) systems and applications has motivated a paradig... more The surge in demand for Internet of Things (IoT) systems and applications has motivated a paradigm shift in the development of viable radio frequency identification technology (RFID)-based solutions for ubiquitous real-time monitoring and tracking. Bit tracking-based anti-collision algorithms have attracted considerable attention, recently, due to its positive impact on decreasing the identification time. We aim to extend bit tracking to work effectively over erroneous channels and scalable multi RFID readers systems. Towards this objective, we extend the bit tracking technique along two dimensions. First, we introduce and evaluate a type of bit errors that appears only in bit tracking-based anti-collision algorithms called false collided bit error in single reader RFID systems. A false collided bit error occurs when a reader perceives a bit sent by tag as an erroneous bit due to channel imperfection and not because of a physical collision. This phenomenon results in a significant increase in the identification delay. We introduce a novel, zero overhead algorithm called false collided bit error selective recovery tackling the error. There is a repetition gain in bit tracking-based anti-collision algorithms due to their nature, which can be utilized to detect and correct false collided bit errors without adding extra coding bits. Second, we extend bit tracking to ''error-free'' scalable mutli-reader systems, while leaving the study of multi-readers tag identification over imperfect channels for future work. We propose the multi-reader RFID tag identification using bit tracking (MRTI-BT) algorithm which allows concurrent tag identification, by neighboring RFID readers, as opposed to time-consuming scheduling. MRTI-BT identifies tags exclusive to different RFIDs, concurrently. The concept of bit tracking and the proposed parallel identification property are leveraged to reduce the identification time compared to the state-of-the-art. INDEX TERMS Mutli-RFID reader systems, bit tracking, tag identification, reader-reader collision, tag collision, false collided bit errors.

Neural Computing and Applications

IEEE Communications Surveys & Tutorials

Journal of Sensor and Actuator Networks

Future Generation Computer Systems

The omnipresence of unmanned aerial vehicles, or drones, among civilians can lead to technical, s... more The omnipresence of unmanned aerial vehicles, or drones, among civilians can lead to technical, security, and public safety issues that need to be addressed, regulated and prevented. Security agencies are in continuous search for technologies and intelligent systems that are capable of detecting drones. Unfortunately, breakthroughs in relevant technologies are hindered by the lack of open source databases for drone’s Radio Frequency (RF) signals, which are remotely sensed and stored to enable developing the most effective way for detecting and identifying these drones. This paper presents a stepping stone initiative towards the goal of building a database for the RF signals of various drones under different flight modes. We systematically collect, analyze, and record raw RF signals of different drones under different flight modes such as: off, on and connected, hovering, flying, and video recording. In addition, we design intelligent algorithms to detect and identify intruding drones using the developed RF database. Three deep neural networks (DNN) are used to detect the presence of a drone, the presence of a drone and its type, and lastly, the presence of a drone, its type, and flight mode. Performance of each DNN is validated through a 10-fold cross-validation process and evaluated using various metrics. Classification results show a general decline in performance when increasing the number of classes. Averaged accuracy has decreased from 99.7% for the first DNN (2-classes), to 84.5% for the second DNN (4-classes), and lastly, to 46.8% for the third DNN (10-classes). Nevertheless, results of the designed methods confirm the feasibility of the developed drone RF database to be used for detection and identification. The developed drone RF database along with our implementations are made publicly available for students and researchers alike.

IEEE Access

Diabetic patients use therapy from the insulin pump, a type of implantable medical device, for th... more Diabetic patients use therapy from the insulin pump, a type of implantable medical device, for the infusion of insulin to control blood glucose level. While these devices offer many clinical benefits, there has been a recent increase in the number of cases, wherein, the wireless communication channel of such devices has been compromised. This not only causes the device to malfunction but also potentially threatens the patient's life. In this paper, a neural networks-based multi-layer perceptron model was designed for real-time medical device security. Machine learning algorithms are among the most effective and broadly utilized systems for classification, identification, and segmentation. Although they are effective, they are both computationally and memory intensive, making them hard to be deployed on low-power embedded frameworks. In this paper, we present an on-chip neural system network for securing diabetic treatment. The model achieved 98.1% accuracy in classifying fake versus genuine glucose measurements. The proposed model was comparatively evaluated with a linear support vector machine which achieved only 90.17% accuracy with negligible precision and recall. Moreover, the proposal estimates the reliability of the framework through the use of the Bayesian network. The proposed approach enhances the reliability of the overall framework by 18% when only one device is secured, and over 90% when all devices are secured. INDEX TERMS Security, machine learning, insulin pumps, deep learning, implantable medical devices.

IEEE Access

Deep brain stimulators, a widely used and comprehensively acknowledged restorative methodology, i... more Deep brain stimulators, a widely used and comprehensively acknowledged restorative methodology, is a type of implantable medical device which uses electrical stimulation to treat neurological disorders. These devices are widely used to treat diseases such as Parkinson, movement disorder, epilepsy, and psychiatric disorders. Security in such devices plays a vital role since it can directly affect the mental, emotional, and physical state of human bodies. In worse case situations, it can even lead to the patient's death. An adversary in such devices, for instance, can inhibit the normal functionality of the brain by introducing fake stimulation inside the human brain. Nonetheless, the adversary can impair the motor functions, alter impulse control, induce pain or even modify the emotional pattern of the patient by giving fake stimulations through deep brain stimulators. This paper presents a deep learning methodology to predict different attack stimulations in deep brain stimulators. The proposed work uses long short term memory, a type of recurrent network for forecasting and predicting rest tremor velocity (a type of characteristic observed to evaluate the intensity of the neurological diseases). The prediction helps in diagnosing fake vs genuine stimulations. The effect of deep brain stimulation was tested on Parkinson tremor patients. The proposed methodology was able to detect different types of emulated attack patterns efficiently and thereby notifying the patient about the possible attack. INDEX TERMS deep brain stimulators, deep learning, implantable medical devices, machine learning, security.

Future Generation Computer Systems

Abu Dhabi International Petroleum Exhibition & Conference

Diagnostics (Basel, Switzerland), Jan 16, 2018

Recent advances in mobile technology have created a shift towards using battery-driven devices in... more Recent advances in mobile technology have created a shift towards using battery-driven devices in remote monitoring settings and smart homes. Clinicians are carrying out diagnostic and screening procedures based on the electrocardiogram (ECG) signals collected remotely for outpatients who need continuous monitoring. High-speed transmission and analysis of large recorded ECG signals are essential, especially with the increased use of battery-powered devices. Exploring low-power alternative compression methodologies that have high efficiency and that enable ECG signal collection, transmission, and analysis in a smart home or remote location is required. Compression algorithms based on adaptive linear predictors and decimation by a factor B / K are evaluated based on compression ratio (CR), percentage root-mean-square difference (PRD), and heartbeat detection accuracy of the reconstructed ECG signal. With two databases (153 subjects), the new algorithm demonstrates the highest compress...

2014 IEEE International Conference on Communications (ICC), 2014

ABSTRACT Cognitive radio (CR) vehicular networks are poised to opportunistically use the licensed... more ABSTRACT Cognitive radio (CR) vehicular networks are poised to opportunistically use the licensed spectrum for high bandwidth inter-vehicular messaging, driver-assist functions, and passenger entertainment services. Recent rulings that mandate the use of spectrum databases introduce additional challenges in this highly mobile environment, where the CR enabled vehicles must update their spectrum data frequently and complete the data transfers with roadside base stations. As the rules allow local spectrum sensing only under the assurance of high accuracy, there is an associated tradeoff in obtaining assuredly correct spectrum updates from the database at a finite cost, compared to locally obtained sensing results that may have a finite error probability. This paper aims to answer the question of when to undertake local spectrum sensing and when to rely on database updates through a novel method of exploiting the correlation between 2G spectrum bands and TV whitespace. We describe experimental studies that validate our approach and quantify the cost savings made possible by intermittent database queries.

2014 Eleventh Annual IEEE International Conference on Sensing, Communication, and Networking Workshops (SECON Workshops), 2014

IEEE Transactions on Vehicular Technology, 2000

ABSTRACT Cognitive radio (CR) vehicular networks are poised to opportunistically use the licensed... more ABSTRACT Cognitive radio (CR) vehicular networks are poised to opportunistically use the licensed spectrum for high-bandwidth intervehicular messaging, driver-assist functions, and passenger entertainment services. Recent rulings that mandate the use of spectrum databases have introduced additional challenges in this highly mobile environment, where the CR-enabled vehicles must update their spectrum data frequently and complete the data transfers with roadside base stations (BSs) in very short interaction times. This paper aims to answer two fundamental questions: 1) when to undertake local spectrum sensing, as opposed to accessing spectrum database information at a finite cost overhead; and 2) how to ensure correct packet receptions among the multiple BSs and CR vehicles using fewer slots than the messages that need to be transmitted. The contributions of this paper are twofold: First, we introduce a method of qualifying the correctness of spectrum sensing results using out-of-band 2G spectrum data using experimental results. Second, to the best of our knowledge, this is the first work on applying the concept of interference alignment (IA) in a practical network setting, leading to dramatic reduction in message transmission times. Our approach demonstrates significant reductions in the overhead of direct database queries and improvement in the accuracy of spectrum sensing for mobile vehicles.

Ad Hoc Networks, 2013

Reliable and high throughput data delivery in cognitive radio networks remains an open challenge ... more Reliable and high throughput data delivery in cognitive radio networks remains an open challenge owing to the inability of the source to quickly identify and react to changes in spectrum availability. The window-based rate adaptation in TCP relies on acknowledgments (ACKs) to self trigger the sending rate, which are often delayed or lost owing to intermittent primary user (PU) activity, resulting in an incorrect inference of congestion by the source node. This paper proposes the first equation-based transport protocol based on TCP Friendly Rate Control for Cognitive Radio, called as TFRC-CR, which allows immediate changes in the transmission rate based on the spectrum-related changes in the network environment. TFRC-CR has the following unique features: (i) it leverages the recent FCC mandated spectrum databases with minimum querying overhead, (ii) it enables fine adjustment of the transmission rate by identifying the instances of true network congestion, as well as (iii) provides guidelines on when to restart the source transmission after a pause due to PU activity. TFRC-CR is evaluated through an extensive set of module additions to the ns-2 simulator which is also released for further investigation by the research community.

Sensors

Drones are becoming increasingly popular not only for recreational purposes but in day-to-day app... more Drones are becoming increasingly popular not only for recreational purposes but in day-to-day applications in engineering, medicine, logistics, security and others. In addition to their useful applications, an alarming concern in regard to the physical infrastructure security, safety and privacy has arisen due to the potential of their use in malicious activities. To address this problem, we propose a novel solution that automates the drone detection and identification processes using a drone’s acoustic features with different deep learning algorithms. However, the lack of acoustic drone datasets hinders the ability to implement an effective solution. In this paper, we aim to fill this gap by introducing a hybrid drone acoustic dataset composed of recorded drone audio clips and artificially generated drone audio samples using a state-of-the-art deep learning technique known as the Generative Adversarial Network. Furthermore, we examine the effectiveness of using drone audio with dif...

Eurasia Journal of Mathematics, Science and Technology Education

This study examined engineering students' initial readiness to transition to emergency online lea... more This study examined engineering students' initial readiness to transition to emergency online learning in response to COVID-19 in Qatar. A theoretical framework is proposed for understanding the factors influencing students' readiness for change. Sequential explanatory mixed-method research was conducted, with 140 participants completing an online survey, of which 68 also contributed written reflections and 8 participated in semi-structured interviews. Exploratory factor analysis displayed a four-factor structure, including initial preparedness and motivation for online learning, self-efficacy beliefs about online learning, self-directed learning online, and support. The qualitative outcomes supported the four factors and provided further insight into their varied and nuanced manifestation. In accounting for the perceived impact of the factors on readiness, significant differences were identified regarding pedagogical mode, with students enrolled in PBL courses reporting higher readiness than those from non-PBL courses. The practical implications for preparing students for future emergency online learning are discussed.

Eurasia Journal of Mathematics, Science and Technology Education

Background: While improved student engagement has been highlighted as an essential goal and a maj... more Background: While improved student engagement has been highlighted as an essential goal and a major outcome of Problem and Project-Based learning (PBL), little empirical evidence has been provided regarding types and forms of student engagement. Material and method: The study explored forms of student engagement in PBL settings, drawing on empirical data of observations and group interviews with 23 project teams (116 students) in four different PBL undergraduate civil engineering courses at Qatar University. Results: The study identified four patterns of student engagement in a PBL setting. Participants reported significant indicators of the first two patterns-engagement as autonomy and as connection. Regarding the other two indicators, namely relational and emotional engagement, they reported positive yet slightly fewer indicators. Three factors were identified that influenced student engagement in a project teams, namely PBL types and its appropriateness to the nature of the course, students' prior experiences with PBL, and team dynamics. Conclusions: These results facilitate the establishment of an institutional framework supporting a progressive approach to embracing PBL. In this framework PBL implementation begins with diverse practices at the course level and has systemic change as its ultimate goal. This framework particularly aims to support an institutionalized approach to transition to PBL in a socio-cultural context (e.g., a non-western context) where instructors are as the primary and authoritative source of knowledge. The overall outcome of the study supports management of change from a lecturebased mode to PBL in a non-western context.

Human-centric Computing and Information Sciences

The term 'navigation' collectively represent tasks that include tracking the user's position, pla... more The term 'navigation' collectively represent tasks that include tracking the user's position, planning feasible routes and guiding the user through the routes to reach the desired destination. In the past, considerable number of navigation systems were developed for accessing outdoor and indoor environments. Most of the outdoor navigation systems adopt GPS and Global Navigation Satellite System (GLONASS) to track the user's position. Important applications of outdoor navigation systems include wayfinding for vehicles, pedestrians, and blind people [1, 2]. In indoor environments, the GPS cannot provide fair accuracy in tracking due to nonline of sight issues [3]. This limitation hinders the implementation of GPS in indoor navigation systems, although it can be solved by using "high-sensitivity GPS receivers or GPS pseudolites" [4]. However, the cost of implementation can be a barrier to applying this system in real-world scenarios.

IEEE Communications Surveys & Tutorials

The Internet of Things (IoT) integrates billions of smart devices that can communicate with one a... more The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. It is one of the fastest developing fields in the history of computing, with an estimated 50 billion devices by the end of 2020. On the one hand, IoT technologies play a crucial role in enhancing several real-life smart applications that can improve life quality. On the other hand, the crosscutting nature of IoT systems and the multidisciplinary components involved in the deployment of such systems have introduced new security challenges. Implementing security measures, such as encryption, authentication, access control, network security and application security, for IoT devices and their inherent vulnerabilities is ineffective. Therefore, existing security methods should be enhanced to secure the IoT ecosystem effectively. Machine learning and deep learning (ML/DL) have advanced considerably over the last few years, and machine intelligence has transitioned from laboratory curiosity to practical machinery in several important applications. The ability to monitor IoT devices intelligently provides a significant solution to new or zero-day attacks. ML/DL are powerful methods of data exploration for learning about 'normal' and 'abnormal' behaviour according to how IoT components and devices perform within the IoT environment. Consequently, ML/DL methods are important in transforming the security of IoT systems from merely facilitating secure communication between devices to security-based intelligence systems. The goal of this work is to provide a comprehensive survey of ML methods and recent advances in DL methods that can be used to develop enhanced security methods for IoT systems. IoT security threats that are related to inherent or newly introduced threats are presented, and various potential IoT system attack surfaces and the possible threats related to each surface are discussed. We then thoroughly review ML/DL methods for IoT security and present the opportunities, advantages and shortcomings of each method. We discuss the opportunities and challenges involved in applying ML/DL to IoT security. These opportunities and challenges can serve as potential future research directions.

IEEE Access

The surge in demand for Internet of Things (IoT) systems and applications has motivated a paradig... more The surge in demand for Internet of Things (IoT) systems and applications has motivated a paradigm shift in the development of viable radio frequency identification technology (RFID)-based solutions for ubiquitous real-time monitoring and tracking. Bit tracking-based anti-collision algorithms have attracted considerable attention, recently, due to its positive impact on decreasing the identification time. We aim to extend bit tracking to work effectively over erroneous channels and scalable multi RFID readers systems. Towards this objective, we extend the bit tracking technique along two dimensions. First, we introduce and evaluate a type of bit errors that appears only in bit tracking-based anti-collision algorithms called false collided bit error in single reader RFID systems. A false collided bit error occurs when a reader perceives a bit sent by tag as an erroneous bit due to channel imperfection and not because of a physical collision. This phenomenon results in a significant increase in the identification delay. We introduce a novel, zero overhead algorithm called false collided bit error selective recovery tackling the error. There is a repetition gain in bit tracking-based anti-collision algorithms due to their nature, which can be utilized to detect and correct false collided bit errors without adding extra coding bits. Second, we extend bit tracking to ''error-free'' scalable mutli-reader systems, while leaving the study of multi-readers tag identification over imperfect channels for future work. We propose the multi-reader RFID tag identification using bit tracking (MRTI-BT) algorithm which allows concurrent tag identification, by neighboring RFID readers, as opposed to time-consuming scheduling. MRTI-BT identifies tags exclusive to different RFIDs, concurrently. The concept of bit tracking and the proposed parallel identification property are leveraged to reduce the identification time compared to the state-of-the-art. INDEX TERMS Mutli-RFID reader systems, bit tracking, tag identification, reader-reader collision, tag collision, false collided bit errors.

Neural Computing and Applications

IEEE Communications Surveys & Tutorials

Journal of Sensor and Actuator Networks

Future Generation Computer Systems

The omnipresence of unmanned aerial vehicles, or drones, among civilians can lead to technical, s... more The omnipresence of unmanned aerial vehicles, or drones, among civilians can lead to technical, security, and public safety issues that need to be addressed, regulated and prevented. Security agencies are in continuous search for technologies and intelligent systems that are capable of detecting drones. Unfortunately, breakthroughs in relevant technologies are hindered by the lack of open source databases for drone’s Radio Frequency (RF) signals, which are remotely sensed and stored to enable developing the most effective way for detecting and identifying these drones. This paper presents a stepping stone initiative towards the goal of building a database for the RF signals of various drones under different flight modes. We systematically collect, analyze, and record raw RF signals of different drones under different flight modes such as: off, on and connected, hovering, flying, and video recording. In addition, we design intelligent algorithms to detect and identify intruding drones using the developed RF database. Three deep neural networks (DNN) are used to detect the presence of a drone, the presence of a drone and its type, and lastly, the presence of a drone, its type, and flight mode. Performance of each DNN is validated through a 10-fold cross-validation process and evaluated using various metrics. Classification results show a general decline in performance when increasing the number of classes. Averaged accuracy has decreased from 99.7% for the first DNN (2-classes), to 84.5% for the second DNN (4-classes), and lastly, to 46.8% for the third DNN (10-classes). Nevertheless, results of the designed methods confirm the feasibility of the developed drone RF database to be used for detection and identification. The developed drone RF database along with our implementations are made publicly available for students and researchers alike.

IEEE Access

Diabetic patients use therapy from the insulin pump, a type of implantable medical device, for th... more Diabetic patients use therapy from the insulin pump, a type of implantable medical device, for the infusion of insulin to control blood glucose level. While these devices offer many clinical benefits, there has been a recent increase in the number of cases, wherein, the wireless communication channel of such devices has been compromised. This not only causes the device to malfunction but also potentially threatens the patient's life. In this paper, a neural networks-based multi-layer perceptron model was designed for real-time medical device security. Machine learning algorithms are among the most effective and broadly utilized systems for classification, identification, and segmentation. Although they are effective, they are both computationally and memory intensive, making them hard to be deployed on low-power embedded frameworks. In this paper, we present an on-chip neural system network for securing diabetic treatment. The model achieved 98.1% accuracy in classifying fake versus genuine glucose measurements. The proposed model was comparatively evaluated with a linear support vector machine which achieved only 90.17% accuracy with negligible precision and recall. Moreover, the proposal estimates the reliability of the framework through the use of the Bayesian network. The proposed approach enhances the reliability of the overall framework by 18% when only one device is secured, and over 90% when all devices are secured. INDEX TERMS Security, machine learning, insulin pumps, deep learning, implantable medical devices.

IEEE Access

Deep brain stimulators, a widely used and comprehensively acknowledged restorative methodology, i... more Deep brain stimulators, a widely used and comprehensively acknowledged restorative methodology, is a type of implantable medical device which uses electrical stimulation to treat neurological disorders. These devices are widely used to treat diseases such as Parkinson, movement disorder, epilepsy, and psychiatric disorders. Security in such devices plays a vital role since it can directly affect the mental, emotional, and physical state of human bodies. In worse case situations, it can even lead to the patient's death. An adversary in such devices, for instance, can inhibit the normal functionality of the brain by introducing fake stimulation inside the human brain. Nonetheless, the adversary can impair the motor functions, alter impulse control, induce pain or even modify the emotional pattern of the patient by giving fake stimulations through deep brain stimulators. This paper presents a deep learning methodology to predict different attack stimulations in deep brain stimulators. The proposed work uses long short term memory, a type of recurrent network for forecasting and predicting rest tremor velocity (a type of characteristic observed to evaluate the intensity of the neurological diseases). The prediction helps in diagnosing fake vs genuine stimulations. The effect of deep brain stimulation was tested on Parkinson tremor patients. The proposed methodology was able to detect different types of emulated attack patterns efficiently and thereby notifying the patient about the possible attack. INDEX TERMS deep brain stimulators, deep learning, implantable medical devices, machine learning, security.

Future Generation Computer Systems

Abu Dhabi International Petroleum Exhibition & Conference

Diagnostics (Basel, Switzerland), Jan 16, 2018

Recent advances in mobile technology have created a shift towards using battery-driven devices in... more Recent advances in mobile technology have created a shift towards using battery-driven devices in remote monitoring settings and smart homes. Clinicians are carrying out diagnostic and screening procedures based on the electrocardiogram (ECG) signals collected remotely for outpatients who need continuous monitoring. High-speed transmission and analysis of large recorded ECG signals are essential, especially with the increased use of battery-powered devices. Exploring low-power alternative compression methodologies that have high efficiency and that enable ECG signal collection, transmission, and analysis in a smart home or remote location is required. Compression algorithms based on adaptive linear predictors and decimation by a factor B / K are evaluated based on compression ratio (CR), percentage root-mean-square difference (PRD), and heartbeat detection accuracy of the reconstructed ECG signal. With two databases (153 subjects), the new algorithm demonstrates the highest compress...

2014 IEEE International Conference on Communications (ICC), 2014

ABSTRACT Cognitive radio (CR) vehicular networks are poised to opportunistically use the licensed... more ABSTRACT Cognitive radio (CR) vehicular networks are poised to opportunistically use the licensed spectrum for high bandwidth inter-vehicular messaging, driver-assist functions, and passenger entertainment services. Recent rulings that mandate the use of spectrum databases introduce additional challenges in this highly mobile environment, where the CR enabled vehicles must update their spectrum data frequently and complete the data transfers with roadside base stations. As the rules allow local spectrum sensing only under the assurance of high accuracy, there is an associated tradeoff in obtaining assuredly correct spectrum updates from the database at a finite cost, compared to locally obtained sensing results that may have a finite error probability. This paper aims to answer the question of when to undertake local spectrum sensing and when to rely on database updates through a novel method of exploiting the correlation between 2G spectrum bands and TV whitespace. We describe experimental studies that validate our approach and quantify the cost savings made possible by intermittent database queries.

2014 Eleventh Annual IEEE International Conference on Sensing, Communication, and Networking Workshops (SECON Workshops), 2014

IEEE Transactions on Vehicular Technology, 2000

ABSTRACT Cognitive radio (CR) vehicular networks are poised to opportunistically use the licensed... more ABSTRACT Cognitive radio (CR) vehicular networks are poised to opportunistically use the licensed spectrum for high-bandwidth intervehicular messaging, driver-assist functions, and passenger entertainment services. Recent rulings that mandate the use of spectrum databases have introduced additional challenges in this highly mobile environment, where the CR-enabled vehicles must update their spectrum data frequently and complete the data transfers with roadside base stations (BSs) in very short interaction times. This paper aims to answer two fundamental questions: 1) when to undertake local spectrum sensing, as opposed to accessing spectrum database information at a finite cost overhead; and 2) how to ensure correct packet receptions among the multiple BSs and CR vehicles using fewer slots than the messages that need to be transmitted. The contributions of this paper are twofold: First, we introduce a method of qualifying the correctness of spectrum sensing results using out-of-band 2G spectrum data using experimental results. Second, to the best of our knowledge, this is the first work on applying the concept of interference alignment (IA) in a practical network setting, leading to dramatic reduction in message transmission times. Our approach demonstrates significant reductions in the overhead of direct database queries and improvement in the accuracy of spectrum sensing for mobile vehicles.

Ad Hoc Networks, 2013

Reliable and high throughput data delivery in cognitive radio networks remains an open challenge ... more Reliable and high throughput data delivery in cognitive radio networks remains an open challenge owing to the inability of the source to quickly identify and react to changes in spectrum availability. The window-based rate adaptation in TCP relies on acknowledgments (ACKs) to self trigger the sending rate, which are often delayed or lost owing to intermittent primary user (PU) activity, resulting in an incorrect inference of congestion by the source node. This paper proposes the first equation-based transport protocol based on TCP Friendly Rate Control for Cognitive Radio, called as TFRC-CR, which allows immediate changes in the transmission rate based on the spectrum-related changes in the network environment. TFRC-CR has the following unique features: (i) it leverages the recent FCC mandated spectrum databases with minimum querying overhead, (ii) it enables fine adjustment of the transmission rate by identifying the instances of true network congestion, as well as (iii) provides guidelines on when to restart the source transmission after a pause due to PU activity. TFRC-CR is evaluated through an extensive set of module additions to the ns-2 simulator which is also released for further investigation by the research community.