Using the internet of things in smart energy systems and networks

T Ahmad, D Zhang - Sustainable Cities and Society, 2021 - Elsevier
Private businesses and policymakers are accelerating the deployment and advancement of
smart grid technology innovations that can support smart energy systems. Technological …

[HTML][HTML] Deep learning for network traffic monitoring and analysis (NTMA): A survey

M Abbasi, A Shahraki, A Taherkordi - Computer communications, 2021 - Elsevier
Modern communication systems and networks, eg, Internet of Things (IoT) and cellular
networks, generate a massive and heterogeneous amount of traffic data. In such networks …

Deep anomaly detection for time-series data in industrial IoT: A communication-efficient on-device federated learning approach

Y Liu, S Garg, J Nie, Y Zhang, Z **ong… - IEEE Internet of …, 2020 - ieeexplore.ieee.org
Since edge device failures (ie, anomalies) seriously affect the production of industrial
products in Industrial IoT (IIoT), accurately and timely detecting anomalies are becoming …

A deep learning-based cryptocurrency price prediction scheme for financial institutions

MM Patel, S Tanwar, R Gupta, N Kumar - Journal of information security …, 2020 - Elsevier
A cryptocurrency is a network-based digital exchange medium, where the records are
secured using strong cryptographic algorithms such as Secure Hash Algorithm 2 (SHA-2) …

An optimized dense convolutional neural network model for disease recognition and classification in corn leaf

A Waheed, M Goyal, D Gupta, A Khanna… - … and Electronics in …, 2020 - Elsevier
An optimized dense convolutional neural network (CNN) architecture (DenseNet) for corn
leaf disease recognition and classification is proposed in this paper. Corn is one of the most …

A review of intrusion detection systems using machine and deep learning in internet of things: Challenges, solutions and future directions

J Asharf, N Moustafa, H Khurshid, E Debie, W Haider… - Electronics, 2020 - mdpi.com
The Internet of Things (IoT) is poised to impact several aspects of our lives with its fast
proliferation in many areas such as wearable devices, smart sensors and home appliances …

A unified deep learning anomaly detection and classification approach for smart grid environments

I Siniosoglou, P Radoglou-Grammatikis… - … on Network and …, 2021 - ieeexplore.ieee.org
The interconnected and heterogeneous nature of the next-generation Electrical Grid (EG),
widely known as Smart Grid (SG), bring severe cybersecurity and privacy risks that can also …

Practical autoencoder based anomaly detection by using vector reconstruction error

H Torabi, SL Mirtaheri, S Greco - Cybersecurity, 2023 - Springer
Nowadays, cloud computing provides easy access to a set of variable and configurable
computing resources based on user demand through the network. Cloud computing …

A novel intrusion detection method based on lightweight neural network for internet of things

R Zhao, G Gui, Z Xue, J Yin, T Ohtsuki… - IEEE Internet of …, 2021 - ieeexplore.ieee.org
The purpose of a network intrusion detection (NID) is to detect intrusions in the network,
which plays a critical role in ensuring the security of the Internet of Things (IoT). Recently …

Malware detection with artificial intelligence: A systematic literature review

MG Gaber, M Ahmed, H Janicke - ACM Computing Surveys, 2024 - dl.acm.org
In this survey, we review the key developments in the field of malware detection using AI and
analyze core challenges. We systematically survey state-of-the-art methods across five …