Active Machine Learning Adversarial Attack Detection in the User Feedback Process

BACKGROUND The COVID-19 pandemic has uncovered the potential of digital misinformation in shaping the health of nations. The deluge of unverified information that spreads faster than the epidemic itself is an unprecedented phenomenon that has put millions of lives in danger. Mitigating this ‘Infodemic’ requires strong health messaging systems that are engaging, vernacular, scalable, effective and continuously learn the new patterns of misinformation. OBJECTIVE We created WashKaro, a multi-pronged intervention for mitigating misinformation through conversational AI, machine translation and natural language processing. WashKaro provides the right information matched against WHO guidelines through AI, and delivers it in the right format in local languages. METHODS We theorize (i) an NLP based AI engine that could continuously incorporate user feedback to improve relevance of information, (ii) bite sized audio in the local language to improve penetrance in a country with skewed gender literacy ratios, and (iii) conversational but interactive AI engagement with users towards an increased health awareness in the community. RESULTS A total of 5026 people who downloaded the app during the study window, among those 1545 were active users. Our study shows that 3.4 times more females engaged with the App in Hindi as compared to males, the relevance of AI-filtered news content doubled within 45 days of continuous machine learning, and the prudence of integrated AI chatbot “Satya” increased thus proving the usefulness of an mHealth platform to mitigate health misinformation. CONCLUSIONS We conclude that a multi-pronged machine learning application delivering vernacular bite-sized audios and conversational AI is an effective approach to mitigate health misinformation. CLINICALTRIAL Not Applicable

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Cyber-attack detection in healthcare using cyber-physical system and machine learning techniques

Soft Computing ◽

10.1007/s00500-021-05926-8 ◽

2021 ◽

Author(s):

Ahmad Ali AlZubi ◽

Mohammed Al-Maitah ◽

Abdulaziz Alarifi

Keyword(s):

Machine Learning ◽

Physical System ◽

Attack Detection ◽

Machine Learning Techniques ◽

Cyber Physical System ◽

Cyber Attack ◽

Learning Techniques

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Deceiving Machine Learning-Based Saturation Attack Detection Systems in SDN

2020 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN) ◽

10.1109/nfv-sdn50289.2020.9289908 ◽

2020 ◽

Author(s):

Samer Y Khamaiseh ◽

Izzat Alsmadi ◽

Abdullah Al-Alaj

Keyword(s):

Machine Learning ◽

Attack Detection ◽

Detection Systems

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Web Attack Detection through Network-Traffic-Based Feature Engineering and Machine Learning

Proceedings of the 2020 International Conference on Cyberspace Innovation of Advanced Technologies ◽

10.1145/3444370.3444555 ◽

2020 ◽

Author(s):

Jian Yang ◽

Hao Wang ◽

Yueming Lu

Keyword(s):

Machine Learning ◽

Network Traffic ◽

Attack Detection ◽

Feature Engineering

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An adversarial attack detection method in deep neural networks based on re-attacking approach

Multimedia Tools and Applications ◽

10.1007/s11042-020-10261-5 ◽

2021 ◽

Author(s):

Morteza Ali Ahmadi ◽

Rouhollah Dianat ◽

Hossein Amirkhani

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

Detection Method ◽

Attack Detection ◽

Adversarial Attack

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Real-Time Adversarial Attack Detection with Deep Image Prior Initialized as a High-Level Representation Based Blurring Network

Electronics ◽

10.3390/electronics10010052 ◽

2020 ◽

Vol 10 (1) ◽

pp. 52

Author(s):

Richard Evan Sutanto ◽

Sukho Lee

Keyword(s):

Neural Network ◽

Attack Detection ◽

Detection Methods ◽

Defense System ◽

Image Prior ◽

The Neural Network ◽

Adversarial Examples ◽

Deep Image ◽

Adversarial Attack ◽

High Level

Several recent studies have shown that artificial intelligence (AI) systems can malfunction due to intentionally manipulated data coming through normal channels. Such kinds of manipulated data are called adversarial examples. Adversarial examples can pose a major threat to an AI-led society when an attacker uses them as means to attack an AI system, which is called an adversarial attack. Therefore, major IT companies such as Google are now studying ways to build AI systems which are robust against adversarial attacks by developing effective defense methods. However, one of the reasons why it is difficult to establish an effective defense system is due to the fact that it is difficult to know in advance what kind of adversarial attack method the opponent is using. Therefore, in this paper, we propose a method to detect the adversarial noise without knowledge of the kind of adversarial noise used by the attacker. For this end, we propose a blurring network that is trained only with normal images and also use it as an initial condition of the Deep Image Prior (DIP) network. This is in contrast to other neural network based detection methods, which require the use of many adversarial noisy images for the training of the neural network. Experimental results indicate the validity of the proposed method.

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