Incremental Learning Implementations and Vision for Cyber Risk Detection in IoT

Rahouti, Mohamed; Ayyash, Moussa; Jagatheesaperumal, Senthil Kumar; Oliveira, Daniela Cabral de

doi:10.1109/iotm.0011.2100019

“…Even though AI could significantly improve cyber security for IoT devices, hackers may also exploit the usage of AI for criminal activity. Therefore, AI systems are widely used for both cyber defense and attacks [81]. AI can automate the authentication of IoT devices by strengthening them with strong passwords, biometric-based security features, and can protect them against cloud computing vulnerabilities.…”

Section: B Security Enhancement In Iot Devicesmentioning

confidence: 99%

Explainable AI Over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions

Jagatheesaperumal¹,

Pham

²

,

Ruby

³

et al. 2022

IEEE Open J. Commun. Soc.

Self Cite

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Explainable Artificial Intelligence (XAI) is transforming the field of Artificial Intelligence (AI) by enhancing the trust of end-users in machines. As the number of connected devices keeps on growing, the Internet of Things (IoT) market needs to be trustworthy for the end-users. However, existing literature still lacks a systematic and comprehensive survey work on the use of XAI for IoT. To bridge this lacking, in this paper, we address the XAI frameworks with a focus on their characteristics and support for IoT. We illustrate the widely-used XAI services for IoT applications, such as security enhancement, Internet of Medical Things (IoMT), Industrial IoT (IIoT), and Internet of City Things (IoCT). We also suggest the implementation choice of XAI models over IoT systems in these applications with appropriate examples and summarize the key inferences for future works. Moreover, we present the cutting-edge development in edge XAI structures and the support of sixth-generation (6G) communication services for IoT applications, along with key inferences. In a nutshell, this paper constitutes the first holistic compilation on the development of XAI-based frameworks tailored for the demands of future IoT use cases.

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“…The paper performed a comparative analysis between randomly independent sampling and Markov resampling in incremental learning. In addition to image recognition, incremental SVM research was conducted in different domains such as Human action recognition [24] and cyber risk detection [25]. Several other researchers reported their findings based on incremental learning, but as far as we know, none of them was conducted on audio recognition.…”

Section: Incremental Learning Via Svmmentioning

confidence: 99%

Incremental multiclass open-set audio recognition

Jleed

¹

,

Bouchard²

2022

Int. J. Adv. Intell. Informatics

1

0

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Incremental learning aims to learn new classes if they emerge while maintaining the performance for previously known classes. It acquires useful information from incoming data to update the existing models. Open-set recognition, however, requires the ability to recognize examples from known classes and reject examples from new/unknown classes. There are two main challenges in this matter. First, new class discovery: the algorithm needs to not only recognize known classes but it must also detect unknown classes. Second, model extension: after the new classes are identified, the model needs to be updated. Focusing on this matter, we introduce incremental open-set multiclass support vector machine algorithms that can classify examples from seen/unseen classes, using incremental learning to increase the current model with new classes without entirely retraining the system. Comprehensive evaluations are carried out on both open set recognition and incremental learning. For open-set recognition, we adopt the openness test that examines the effectiveness of a varying number of known/unknown labels. For incremental learning, we adapt the model to detect a single novel class in each incremental phase and update the model with unknown classes. Experimental results show promising performance for the proposed methods, compared with some representative previous methods.

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“…Even though AI could significantly improve cyber security for IoT devices, hackers may also exploit the usage of AI for criminal activity. Therefore, AI systems are widely used for both cyber defense and attacks [80]. AI can automate the authentication of IoT devices by strengthening them with strong passwords, biometric-based security features, and can protect them against cloud computing vulnerabilities.…”

Section: B Security Enhancement In Iot Devicesmentioning

confidence: 99%

Explainable AI over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions

Jagatheesaperumal,

Pham,

Ruby

et al. 2022

Preprint

0

View full text Add to dashboard Cite

Explainable Artificial Intelligence (XAI) is transforming the field of Artificial Intelligence (AI) by enhancing the trust of end-users in machines. As the number of connected devices keeps on growing, the Internet of Things (IoT) market needs to be trustworthy for the end-users. However, existing literature still lacks a systematic and comprehensive survey work on the use of XAI for IoT. To bridge this lacking, in this paper, we address the XAI frameworks with a focus on their characteristics and support for IoT. We illustrate the widely-used XAI services for IoT applications, such as security enhancement, Internet of Medical Things (IoMT), Industrial IoT (IIoT), and Internet of City Things (IoCT). We also suggest the implementation choice of XAI models over IoT systems in these applications with appropriate examples and summarize the key inferences for future works. Moreover, we present the cutting-edge development in edge XAI structures and the support of sixth-generation (6G) communication services for IoT applications, along with key inferences. In a nutshell, this paper constitutes the first holistic compilation on the development of XAI-based frameworks tailored for the demands of future IoT use cases.

show abstract

Incremental Learning Implementations and Vision for Cyber Risk Detection in IoT

Cited by 6 publications

References 11 publications

Explainable AI Over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions

Explainable AI Over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions

Incremental multiclass open-set audio recognition

Explainable AI over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions

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