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Adversarial learning techniques for security and privacy preservation: A comprehensive review
In recent years, the use of smart devices has increased exponentially, resulting in massive
amounts of data. To handle this data, effective data storage and management has required …
amounts of data. To handle this data, effective data storage and management has required …
Security of machine learning-based anomaly detection in cyber physical systems
With the emergence of the Internet of Things (IoT) and Artificial Intelligence (AI) services and
applications in the Cyber Physical Systems (CPS), the methods of protecting CPS against …
applications in the Cyber Physical Systems (CPS), the methods of protecting CPS against …
Image transformation for IoT time-series data: A review
D Altunkaya, FY Okay, S Ozdemir - ar**_Attack_Case_Study/links/638df256484e65005be693d2/Classification-and-Analysis-of-Adversarial-Machine-Learning-Attacks-in-IoT-a-Label-Flip**-Attack-Case-Study.pdf" data-clk="hl=id&sa=T&oi=gga&ct=gga&cd=3&d=6233977650988695910&ei=aV-6Z8ndO9mlieoPh8LdsQY" data-clk-atid="ZsHnAKWJg1YJ" target="_blank">[PDF] researchgate.net
Classification and analysis of adversarial machine learning attacks in iot: a label flip** attack case study
With the increased usage of Internet of Things (IoT) devices in recent years, various Machine
Learning (ML) algorithms have also developed dramatically for attack detection in this …
Learning (ML) algorithms have also developed dramatically for attack detection in this …
Exploiting smartphone defence: a novel adversarial malware dataset and approach for adversarial malware detection
Adversarial malware poses novel threats to smart devices since they grow progressively
integrated into daily life, highlighting their potential weaknesses and importance. Several …
integrated into daily life, highlighting their potential weaknesses and importance. Several …
Secured optimized resource allocation in mobile edge computing
Mobile edge computing (MEC) uses multiple mobiles to compute several complex tasks that
are unable to compute on a single device. Taking advantage of all abundant mobile …
are unable to compute on a single device. Taking advantage of all abundant mobile …
[Retracted] On the Modeling of RTT Time Series for Network Anomaly Detection
Y Kuang, D Li, X Huang, M Zhou - Security and Communication …, 2022 - Wiley Online Library
Network anomalies can seriously influence the performance of networks and cause huge
financial losses. Existing studies modeled the round‐trip time (RTT) time series of each link …
financial losses. Existing studies modeled the round‐trip time (RTT) time series of each link …
Systematic Literature Review of the Adversarial Attacks on AI in Cyber-Physical Systems
N Valeev - 2022 - diva-portal.org
Cyber-physical systems, built from the integration of cyber and physical components, are
being used in multiple domains ranging from manufacturing and healthcare to traffic control …
being used in multiple domains ranging from manufacturing and healthcare to traffic control …
Low FreqAttack: An Frequency Attack
NN **ong¹, W He, M Lu - … , SmartCom 2022, New York City, NY …, 2023 - books.google.com
Time series prediction has become an important research direction in data mining because
of the time-varying pattern of data in various fields. However, time series prediction suffers …
of the time-varying pattern of data in various fields. However, time series prediction suffers …
LowFreqAttack: An Frequency Attack Method in Time Series Prediction
NN **ong, W He, M Lu - International Conference on Smart Computing …, 2022 - Springer
Time series prediction has become an important research direction in data mining because
of the time-varying pattern of data in various fields. However, time series prediction suffers …
of the time-varying pattern of data in various fields. However, time series prediction suffers …