Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-7323-5.ch010
Anthony Triche, Md Abdullah Al Momin
Launched in 2017 to widespread publicity due to the involvement of tech magnate and outspoken futurist Elon Musk, Neuralink Corp. aims to develop an advanced brain-computer interface (BCI) platform capable of assisting in the treatment of serious neurological conditions with longer-term goals of approaching transhumanism through nonmedical human enhancement to enable human-machine “symbiosis with artificial intelligence.” The first published description of a complete prototype Neuralink system, detailed by Muskin the company's only white paper to date, describes a closed-loop, invasive BCI architecture with an unprecedented magnitude of addressable electrodes. Invasive BCI systems require surgical implantation to allow for directly targeted capture and/or stimulation of neural spiking activity in functionally associated clusters of neurons beneath the surface of the cortex.
{"title":"Brain-Computer Interface","authors":"Anthony Triche, Md Abdullah Al Momin","doi":"10.4018/978-1-7998-7323-5.ch010","DOIUrl":"https://doi.org/10.4018/978-1-7998-7323-5.ch010","url":null,"abstract":"Launched in 2017 to widespread publicity due to the involvement of tech magnate and outspoken futurist Elon Musk, Neuralink Corp. aims to develop an advanced brain-computer interface (BCI) platform capable of assisting in the treatment of serious neurological conditions with longer-term goals of approaching transhumanism through nonmedical human enhancement to enable human-machine “symbiosis with artificial intelligence.” The first published description of a complete prototype Neuralink system, detailed by Muskin the company's only white paper to date, describes a closed-loop, invasive BCI architecture with an unprecedented magnitude of addressable electrodes. Invasive BCI systems require surgical implantation to allow for directly targeted capture and/or stimulation of neural spiking activity in functionally associated clusters of neurons beneath the surface of the cortex.","PeriodicalId":137552,"journal":{"name":"Security, Data Analytics, and Energy-Aware Solutions in the IoT","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115594966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The advance of internet of things (IoT) techniques enables a variety of smart-world systems in energy, transportation, home, and city infrastructure, among others. To provide cost-effective data-oriented service, internet of things search engines (IoTSE) have received growing attention as a platform to support efficient data analytics. There are a number of challenges in designing efficient and intelligent IoTSE. In this chapter, the authors focus on the efficiency issue of IoTSE and design the named data networking (NDN)-based approach for IoTSE. To be specific, they first design a simple simulation environment to compare the IP-based network's performance against named data networking (NDN). They then create four scenarios tailored to study the approach's resilience to address network issues and scalability with the growing number of queries in IoTSE. They implement the four scenarios using ns-3 and carry out extensive performance evaluation to determine the efficacy of the approach concerning network resilience and scalability. They also discuss some remaining issues that need further research.
{"title":"Towards Named Data Networking for Internet of Things Search Engines","authors":"Hengshuo Liang, Lauren Burgess, Weixian Liao, Chao Lu, Wei Yu","doi":"10.4018/978-1-7998-7323-5.ch001","DOIUrl":"https://doi.org/10.4018/978-1-7998-7323-5.ch001","url":null,"abstract":"The advance of internet of things (IoT) techniques enables a variety of smart-world systems in energy, transportation, home, and city infrastructure, among others. To provide cost-effective data-oriented service, internet of things search engines (IoTSE) have received growing attention as a platform to support efficient data analytics. There are a number of challenges in designing efficient and intelligent IoTSE. In this chapter, the authors focus on the efficiency issue of IoTSE and design the named data networking (NDN)-based approach for IoTSE. To be specific, they first design a simple simulation environment to compare the IP-based network's performance against named data networking (NDN). They then create four scenarios tailored to study the approach's resilience to address network issues and scalability with the growing number of queries in IoTSE. They implement the four scenarios using ns-3 and carry out extensive performance evaluation to determine the efficacy of the approach concerning network resilience and scalability. They also discuss some remaining issues that need further research.","PeriodicalId":137552,"journal":{"name":"Security, Data Analytics, and Energy-Aware Solutions in the IoT","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122543069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-7323-5.ch006
Md Fazle Rabby, Md Abdullah Al Momin, X. Hei
Generative adversarial networks have been a highly focused research topic in computer vision, especially in image synthesis and image-to-image translation. There are a lot of variations in generative nets, and different GANs are suitable for different applications. In this chapter, the authors investigated conditional generative adversarial networks to generate fake images, such as handwritten signatures. The authors demonstrated an implementation of conditional generative adversarial networks, which can generate fake handwritten signatures according to a condition vector tailored by humans.
{"title":"Handwritten Signature Spoofing With Conditional Generative Adversarial Nets","authors":"Md Fazle Rabby, Md Abdullah Al Momin, X. Hei","doi":"10.4018/978-1-7998-7323-5.ch006","DOIUrl":"https://doi.org/10.4018/978-1-7998-7323-5.ch006","url":null,"abstract":"Generative adversarial networks have been a highly focused research topic in computer vision, especially in image synthesis and image-to-image translation. There are a lot of variations in generative nets, and different GANs are suitable for different applications. In this chapter, the authors investigated conditional generative adversarial networks to generate fake images, such as handwritten signatures. The authors demonstrated an implementation of conditional generative adversarial networks, which can generate fake handwritten signatures according to a condition vector tailored by humans.","PeriodicalId":137552,"journal":{"name":"Security, Data Analytics, and Energy-Aware Solutions in the IoT","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122474348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-7323-5.ch005
Michael Arienmughare, Andrew S. Yoshimura, Md Abdullah Al Momin
This chapter will provide a survey on cyber-physical systems security related to automobiles. In modern vehicles, there has been discussion on how automobiles fit into the world of cyber-physical systems, considering their interaction with both the cyber and physical worlds and interconnected systems. With many modern vehicles being connected to the outside world, there are many vulnerabilities introduced. Modern cars contain many electronic control units and millions of lines of code, which, if compromised, could have fatal consequences. Interfaces to the outside world (e.g., in-vehicle infotainment) may be used as a vector to attack these critical components.
{"title":"Survey of Automotive Cyber-Physical System Security","authors":"Michael Arienmughare, Andrew S. Yoshimura, Md Abdullah Al Momin","doi":"10.4018/978-1-7998-7323-5.ch005","DOIUrl":"https://doi.org/10.4018/978-1-7998-7323-5.ch005","url":null,"abstract":"This chapter will provide a survey on cyber-physical systems security related to automobiles. In modern vehicles, there has been discussion on how automobiles fit into the world of cyber-physical systems, considering their interaction with both the cyber and physical worlds and interconnected systems. With many modern vehicles being connected to the outside world, there are many vulnerabilities introduced. Modern cars contain many electronic control units and millions of lines of code, which, if compromised, could have fatal consequences. Interfaces to the outside world (e.g., in-vehicle infotainment) may be used as a vector to attack these critical components.","PeriodicalId":137552,"journal":{"name":"Security, Data Analytics, and Energy-Aware Solutions in the IoT","volume":"166 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114912325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-7323-5.ch004
Jie Lien, Md Abdullah Al Momin, Xu Yuan
Voice assistant systems (e.g., Siri, Alexa) have attracted wide research attention. However, such systems could receive voice information from malicious sources. Recent work has demonstrated that the voice authentication system is vulnerable to different types of attacks. The attacks are categorized into two main types: spoofing attacks and hidden voice commands. In this chapter, how to launch and defend such attacks is explored. For the spoofing attack, there are four main types, such as replay attacks, impersonation attacks, speech synthesis attacks, and voice conversion attacks. Although such attacks could be accurate on the speech recognition system, they could be easily identified by humans. Thus, the hidden voice commands have attracted a lot of research interest in recent years.
{"title":"Attacks on Voice Assistant Systems","authors":"Jie Lien, Md Abdullah Al Momin, Xu Yuan","doi":"10.4018/978-1-7998-7323-5.ch004","DOIUrl":"https://doi.org/10.4018/978-1-7998-7323-5.ch004","url":null,"abstract":"Voice assistant systems (e.g., Siri, Alexa) have attracted wide research attention. However, such systems could receive voice information from malicious sources. Recent work has demonstrated that the voice authentication system is vulnerable to different types of attacks. The attacks are categorized into two main types: spoofing attacks and hidden voice commands. In this chapter, how to launch and defend such attacks is explored. For the spoofing attack, there are four main types, such as replay attacks, impersonation attacks, speech synthesis attacks, and voice conversion attacks. Although such attacks could be accurate on the speech recognition system, they could be easily identified by humans. Thus, the hidden voice commands have attracted a lot of research interest in recent years.","PeriodicalId":137552,"journal":{"name":"Security, Data Analytics, and Energy-Aware Solutions in the IoT","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132138183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-7323-5.ch008
Vijay Srinivas Tida, Raghabendra Shah, X. Hei
The laser-based audio signal injection can be used for attacking voice controllable systems. An attacker can aim an amplitude-modulated light at the microphone's aperture, and the signal injection acts as a remote voice-command attack on voice-controllable systems. Attackers are using vulnerabilities to steal things that are in the form of physical devices or the form of virtual using making orders, withdrawal of money, etc. Therefore, detection of these signals is important because almost every device can be attacked using these amplitude-modulated laser signals. In this project, the authors use deep learning to detect the incoming signals as normal voice commands or laser-based audio signals. Mel frequency cepstral coefficients (MFCC) are derived from the audio signals to classify the input audio signals. If the audio signals are identified as laser signals, the voice command can be disabled, and an alert can be displayed to the victim. The maximum accuracy of the machine learning model was 100%, and in the real world, it's around 95%.
{"title":"Deep Learning Approach for Protecting Voice-Controllable Devices From Laser Attacks","authors":"Vijay Srinivas Tida, Raghabendra Shah, X. Hei","doi":"10.4018/978-1-7998-7323-5.ch008","DOIUrl":"https://doi.org/10.4018/978-1-7998-7323-5.ch008","url":null,"abstract":"The laser-based audio signal injection can be used for attacking voice controllable systems. An attacker can aim an amplitude-modulated light at the microphone's aperture, and the signal injection acts as a remote voice-command attack on voice-controllable systems. Attackers are using vulnerabilities to steal things that are in the form of physical devices or the form of virtual using making orders, withdrawal of money, etc. Therefore, detection of these signals is important because almost every device can be attacked using these amplitude-modulated laser signals. In this project, the authors use deep learning to detect the incoming signals as normal voice commands or laser-based audio signals. Mel frequency cepstral coefficients (MFCC) are derived from the audio signals to classify the input audio signals. If the audio signals are identified as laser signals, the voice command can be disabled, and an alert can be displayed to the victim. The maximum accuracy of the machine learning model was 100%, and in the real world, it's around 95%.","PeriodicalId":137552,"journal":{"name":"Security, Data Analytics, and Energy-Aware Solutions in the IoT","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127350408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-7323-5.ch009
Md Abdullah Al Momin, Md. Nazmul Islam
Technology has greatly increased the availability of medical procedures in remote locations that are difficult to access, such as battlefields. Teleoperated surgical robots can be used to perform surgeries on patients over the internet in remote locations. A surgeon can remotely operate the robot to perform a procedure in another room or in a different continent. However, security technology has not yet caught up to these cyber-physical devices. There exist potential cybersecurity attacks on these medical devices that could expose a patient to danger in contrast to traditional surgery. Hence, the security of the system is very important. A malicious actor can gain control of the device and potentially threaten the life of a patient. In this chapter, the authors conduct a survey of potential attack vectors a malicious actor could exploit to deny service to the device, gain control of the device, and steal patient data. Furthermore, after the vulnerability analysis, the authors provide mitigation techniques to limit the risk of these attack vectors.
{"title":"Teleoperated Surgical Robot Security","authors":"Md Abdullah Al Momin, Md. Nazmul Islam","doi":"10.4018/978-1-7998-7323-5.ch009","DOIUrl":"https://doi.org/10.4018/978-1-7998-7323-5.ch009","url":null,"abstract":"Technology has greatly increased the availability of medical procedures in remote locations that are difficult to access, such as battlefields. Teleoperated surgical robots can be used to perform surgeries on patients over the internet in remote locations. A surgeon can remotely operate the robot to perform a procedure in another room or in a different continent. However, security technology has not yet caught up to these cyber-physical devices. There exist potential cybersecurity attacks on these medical devices that could expose a patient to danger in contrast to traditional surgery. Hence, the security of the system is very important. A malicious actor can gain control of the device and potentially threaten the life of a patient. In this chapter, the authors conduct a survey of potential attack vectors a malicious actor could exploit to deny service to the device, gain control of the device, and steal patient data. Furthermore, after the vulnerability analysis, the authors provide mitigation techniques to limit the risk of these attack vectors.","PeriodicalId":137552,"journal":{"name":"Security, Data Analytics, and Energy-Aware Solutions in the IoT","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123628194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-7323-5.ch011
Md Abdullah Al Momin
Implantable medical devices (IMDs) are miniaturized computer systems used to monitor and treat various medical conditions. Examples of IMDs include insulin pumps, artificial pacemakers, neuro-stimulators, and implantable cardiac defibrillators. These devices have adopted wireless communication to help facilitate the care they provide for patients by allowing easier transferal of data or remote control of machine operations. However, with such adoption has come exposure to various security risks and issues that must be addressed due to the close relation of patient health and IMD performance. With patient lives on the line, these security risks pose increasingly real problems. This chapter hopes to provide an overview of these security risks, their proposed solutions, and the limitations on IMD systems which make solving these issues nontrivial. Later, the chapter will analyze the security issues and the history of vulnerabilities in pacemakers to illustrate the theoretical topics by considering a specific device.
{"title":"Medical Device Security","authors":"Md Abdullah Al Momin","doi":"10.4018/978-1-7998-7323-5.ch011","DOIUrl":"https://doi.org/10.4018/978-1-7998-7323-5.ch011","url":null,"abstract":"Implantable medical devices (IMDs) are miniaturized computer systems used to monitor and treat various medical conditions. Examples of IMDs include insulin pumps, artificial pacemakers, neuro-stimulators, and implantable cardiac defibrillators. These devices have adopted wireless communication to help facilitate the care they provide for patients by allowing easier transferal of data or remote control of machine operations. However, with such adoption has come exposure to various security risks and issues that must be addressed due to the close relation of patient health and IMD performance. With patient lives on the line, these security risks pose increasingly real problems. This chapter hopes to provide an overview of these security risks, their proposed solutions, and the limitations on IMD systems which make solving these issues nontrivial. Later, the chapter will analyze the security issues and the history of vulnerabilities in pacemakers to illustrate the theoretical topics by considering a specific device.","PeriodicalId":137552,"journal":{"name":"Security, Data Analytics, and Energy-Aware Solutions in the IoT","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123054169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-7323-5.ch002
Sidi Mohamed Sidi Ahmed
The internet of things (IoT) is one of successive technological waves that could have great impact on different aspects of modern life. It is being used in transport, smart grids, healthcare, environmental monitoring, logistics, as well as for processing pure personal data through a fitness tracker, wearable medical device, smartwatch, smart clothing, wearable camera, and so forth. From a legal viewpoint, processing personal data has to be done in accordance with rules of data protection law. This law aims to protect data from collection to retention. It usually applies to the processing of personal data that identifies or can identify a specific natural person. Strict adherence to this law is necessary for protecting personal data from being misused and also for promoting the IoT industry. This chapter discusses the applicability of the data protection law to IoT and the consequences of non-compliance with this law. It also provides recommendations on how to effectively comply with the data protection law in the IoT environment.
{"title":"The Internet of Things From a Legal Perspective","authors":"Sidi Mohamed Sidi Ahmed","doi":"10.4018/978-1-7998-7323-5.ch002","DOIUrl":"https://doi.org/10.4018/978-1-7998-7323-5.ch002","url":null,"abstract":"The internet of things (IoT) is one of successive technological waves that could have great impact on different aspects of modern life. It is being used in transport, smart grids, healthcare, environmental monitoring, logistics, as well as for processing pure personal data through a fitness tracker, wearable medical device, smartwatch, smart clothing, wearable camera, and so forth. From a legal viewpoint, processing personal data has to be done in accordance with rules of data protection law. This law aims to protect data from collection to retention. It usually applies to the processing of personal data that identifies or can identify a specific natural person. Strict adherence to this law is necessary for protecting personal data from being misused and also for promoting the IoT industry. This chapter discusses the applicability of the data protection law to IoT and the consequences of non-compliance with this law. It also provides recommendations on how to effectively comply with the data protection law in the IoT environment.","PeriodicalId":137552,"journal":{"name":"Security, Data Analytics, and Energy-Aware Solutions in the IoT","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132615097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-7323-5.ch007
Md Imran Hossen, Md Abdullah Al Momin, X. Hei
Currently, the vast majority of smart devices with LEDs are on the rise. It has been observed that the lights emitted by each LED have unique spectral characteristics. Despite the fact that there are a number of methods out there to generate fingerprints, none seem to explore the possibility of generating fingerprints using this unique feature. In this chapter, the method to perform device fingerprinting using the unique spectrum emitted from the LED lights is discussed. The generated fingerprint is then used in device pairing.
{"title":"Generating Device Fingerprints for Smart Device Pairing Using the Unique Spectrum Characteristic From LEDs","authors":"Md Imran Hossen, Md Abdullah Al Momin, X. Hei","doi":"10.4018/978-1-7998-7323-5.ch007","DOIUrl":"https://doi.org/10.4018/978-1-7998-7323-5.ch007","url":null,"abstract":"Currently, the vast majority of smart devices with LEDs are on the rise. It has been observed that the lights emitted by each LED have unique spectral characteristics. Despite the fact that there are a number of methods out there to generate fingerprints, none seem to explore the possibility of generating fingerprints using this unique feature. In this chapter, the method to perform device fingerprinting using the unique spectrum emitted from the LED lights is discussed. The generated fingerprint is then used in device pairing.","PeriodicalId":137552,"journal":{"name":"Security, Data Analytics, and Energy-Aware Solutions in the IoT","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121444161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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