Security Issues in Internet of Things (IoT)-Enabled Systems: Problem and Prospects

Poonia, Ajeet Singh; Banerjee, C.; Banerjee, Arpita; Sharma, Sandeep

doi:10.1007/978-981-15-0751-9_130

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…Therefore, the protection of a user’s privacy is another challenge [ 154 , 160 ]. Furthermore, digital identity and identity management are two other important issues that must be taken care of concerning the dynamic nature and scale of the number of devices, applications, and associated services in a large-scale emotion recognition system [ 154 , 161 , 162 ]. The challenge also is to address the resource-constraint nature of the IoT devices e.g., battery capacity, processing speed, and memory capacity to capture, store and analyze effective human emotions [ 163 , 164 , 165 , 166 ].…”

Section: Current Trends and Future Directionsmentioning

confidence: 99%

Development and Progress in Sensors and Technologies for Human Emotion Recognition

Pal

Mukhopadhyay

Suryadevara

2021

Sensors

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With the advancement of human-computer interaction, robotics, and especially humanoid robots, there is an increasing trend for human-to-human communications over online platforms (e.g., zoom). This has become more significant in recent years due to the Covid-19 pandemic situation. The increased use of online platforms for communication signifies the need to build efficient and more interactive human emotion recognition systems. In a human emotion recognition system, the physiological signals of human beings are collected, analyzed, and processed with the help of dedicated learning techniques and algorithms. With the proliferation of emerging technologies, e.g., the Internet of Things (IoT), future Internet, and artificial intelligence, there is a high demand for building scalable, robust, efficient, and trustworthy human recognition systems. In this paper, we present the development and progress in sensors and technologies to detect human emotions. We review the state-of-the-art sensors used for human emotion recognition and different types of activity monitoring. We present the design challenges and provide practical references of such human emotion recognition systems in the real world. Finally, we discuss the current trends in applications and explore the future research directions to address issues, e.g., scalability, security, trust, privacy, transparency, and decentralization.

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Section: Current Trends and Future Directionsmentioning

confidence: 99%

Development and Progress in Sensors and Technologies for Human Emotion Recognition

Pal

Mukhopadhyay

Suryadevara

2021

Sensors

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“…For example, the difference between an IoT sensor and a smartphone that has a significant gap. Since the devices have various specifications, the application of security should differ by the device [4]- [10]. To cover the security vulnerabilities in IoT devices, it is necessary to continuously application of software updates, firmware updates, and security patches from the manufacture.…”

Section: Introductionmentioning

confidence: 99%

A Development of Embedded Anomaly Behavior Packet Detection System for IoT Environment using Machine Learning Techniques

Lim

Choi

Lee

2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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Despite the growth of IoT technology and related markets, aspect of the IoT security in the IoT field is not handled correctly due to several factors such as indiscreet participation in the market, poor optimization for the various specifications. In this paper, an embedded anomaly packet detection system using machine learning technology for an IoT environment is proposed and evaluated. The suggesting system is composed of two main devices-the packet collection device and the packet analysis device. The packet collection device collects network packets from the IoT devices that are connected to the system. The packet analysis device detects anomalies from the packet data by using the machine learning model. Detected anomalies, which are mostly considered as intrusions such as new or bypassing HTTP attacks as well as existing attacks, are responded in real-time. For conformity assessment in a real-time environment, TPR, FPR, accuracy, and detection speed was measured, and the measured values of the target embedded board are 100%, 0.56%, 99.5, and 2.4 to 13.4 seconds, respectively. The results of TPR, FPR, and accuracy indicate the model itself has an excellent ability to discriminate between anomalies, but it is challenging to apply it to an embedded system in terms of detection speed. Future studies need to apply anomaly detection models that are more suitable for embedded devices and unique hardware accelerators for computing artificial neural networks.

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A Proposed IoT Security Framework and Analysis of Network Layer Attacks in IoT

Gupta

Garg

2021

Advances in Intelligent Systems and Computing

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Security Issues in Internet of Things (IoT)-Enabled Systems: Problem and Prospects

Cited by 4 publications

References 7 publications

Development and Progress in Sensors and Technologies for Human Emotion Recognition

Development and Progress in Sensors and Technologies for Human Emotion Recognition

A Development of Embedded Anomaly Behavior Packet Detection System for IoT Environment using Machine Learning Techniques

A Proposed IoT Security Framework and Analysis of Network Layer Attacks in IoT

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