Learning attack mechanisms in wireless sensor networks using Markov decision processes
In this work, we identify two related problems that arise in many Wireless Sensor Networks
defense mechanisms: the problem of ad-hoc defense and the problem of optimality. These …
defense mechanisms: the problem of ad-hoc defense and the problem of optimality. These …
Concept drift-based runtime reliability anomaly detection for edge services adaptation
To meet the rapidly increasing need of computation-intensive and latency-sensitive
applications, mobile edge computing (MEC) has attracted tremendous attention from both …
applications, mobile edge computing (MEC) has attracted tremendous attention from both …
A physical layer security scheme with compressed sensing in OFDM-based IoT systems
The Internet of Things (IoT) features pervasive sensing and control capabilities by deploying
a massive machine-type communication (MTC) devices. However, low hardware complexity …
a massive machine-type communication (MTC) devices. However, low hardware complexity …
Malware threat analysis techniques and approaches for iot applications: A review
CC Uchenna, N Jamil, R Ismail, LK Yan… - Bulletin of Electrical …, 2021 - beei.org
Internet of things (IoT) is a concept that has been widely used to improve business efficiency
and customer's experience. It involves resource constrained devices connecting to each …
and customer's experience. It involves resource constrained devices connecting to each …
[PDF][PDF] Ransomware auto-detection in IoT devices using machine learning
The term Internet of Things (often abbreviated IoT) was coined by industry researchers but
has emerged into mainstream public view only more recently. The IoT is a massive group of …
has emerged into mainstream public view only more recently. The IoT is a massive group of …
A lightweight attribute based encryption scheme with constant size ciphertext for Internet of Things
The Internet of Things technology has been used in a wide range of fields, ranging from
industrial applications to individual lives. As a result, a massive amount of sensitive data is …
industrial applications to individual lives. As a result, a massive amount of sensitive data is …
Deep reinforcement learning for attacking wireless sensor networks
Recent advances in Deep Reinforcement Learning allow solving increasingly complex
problems. In this work, we show how current defense mechanisms in Wireless Sensor …
problems. In this work, we show how current defense mechanisms in Wireless Sensor …
SoftSystem: smart edge computing device selection method for IoT based on soft set technique
The Internet of Things (IoT) is growing day by day, and new IoT devices are introduced and
interconnected. Due to this rapid growth, IoT faces several issues related to communication …
interconnected. Due to this rapid growth, IoT faces several issues related to communication …
A spectrum sensing method based on empirical mode decomposition and K‐means clustering algorithm
Y Wang, Y Zhang, P Wan, S Zhang… - … and Mobile Computing, 2018 - Wiley Online Library
To solve the problems of poor performance of traditional spectrum sensing method under
low signal‐to‐noise ratio, a new spectrum sensing method based on Empirical Mode …
low signal‐to‐noise ratio, a new spectrum sensing method based on Empirical Mode …
Dynamic malware analysis using machine learning algorithm
N Udayakumar, S Anandaselvi… - 2017 International …, 2017 - ieeexplore.ieee.org
Malware detection is a vital think about the protection of the Personal computer systems.
However, presently using signature-based strategies cannot offer correct detection of zero …
However, presently using signature-based strategies cannot offer correct detection of zero …