A survey on data-driven network intrusion detection
Data-driven network intrusion detection (NID) has a tendency towards minority attack
classes compared to normal traffic. Many datasets are collected in simulated environments …
classes compared to normal traffic. Many datasets are collected in simulated environments …
Artificial intelligence in cancer diagnosis and therapy: Current status and future perspective
M Sufyan, Z Shokat, UA Ashfaq - Computers in Biology and Medicine, 2023 - Elsevier
Artificial intelligence (AI) in healthcare plays a pivotal role in combating many fatal diseases,
such as skin, breast, and lung cancer. AI is an advanced form of technology that uses …
such as skin, breast, and lung cancer. AI is an advanced form of technology that uses …
Design and development of a deep learning-based model for anomaly detection in IoT networks
I Ullah, QH Mahmoud - IEEE Access, 2021 - ieeexplore.ieee.org
The growing development of IoT (Internet of Things) devices creates a large attack surface
for cybercriminals to conduct potentially more destructive cyberattacks; as a result, the …
for cybercriminals to conduct potentially more destructive cyberattacks; as a result, the …
A survey of CNN-based network intrusion detection
Over the past few years, Internet applications have become more advanced and widely
used. This has increased the need for Internet networks to be secured. Intrusion detection …
used. This has increased the need for Internet networks to be secured. Intrusion detection …
Intrusion Detection System to Advance Internet of Things Infrastructure‐Based Deep Learning Algorithms
H Alkahtani, THH Aldhyani - Complexity, 2021 - Wiley Online Library
Smart grids, advanced information technology, have become the favored intrusion targets
due to the Internet of Things (IoT) using sensor devices to collect data from a smart grid …
due to the Internet of Things (IoT) using sensor devices to collect data from a smart grid …
Genetic convolutional neural network for intrusion detection systems
Intrusion detection is the identification of unauthorized access of a computer network. This
paper proposes a novel algorithm for a network intrusion detection system (NIDS) using an …
paper proposes a novel algorithm for a network intrusion detection system (NIDS) using an …
Intrusion detection for wireless edge networks based on federated learning
Z Chen, N Lv, P Liu, Y Fang, K Chen, W Pan - IEEE Access, 2020 - ieeexplore.ieee.org
Edge computing provides off-load computing and application services close to end-users,
greatly reducing cloud pressure and communication overhead. However, wireless edge …
greatly reducing cloud pressure and communication overhead. However, wireless edge …
Towards secure intrusion detection systems using deep learning techniques: Comprehensive analysis and review
Providing a high-performance Intrusion Detection System (IDS) can be very effective in
controlling malicious behaviors and cyber-attacks. Regarding the ever-growing negative …
controlling malicious behaviors and cyber-attacks. Regarding the ever-growing negative …
Network attacks detection methods based on deep learning techniques: a survey
With the development of the fifth‐generation networks and artificial intelligence
technologies, new threats and challenges have emerged to wireless communication system …
technologies, new threats and challenges have emerged to wireless communication system …
Intrusion detection system in the advanced metering infrastructure: a cross-layer feature-fusion CNN-LSTM-based approach
R Yao, N Wang, Z Liu, P Chen, X Sheng - Sensors, 2021 - mdpi.com
Among the key components of a smart grid, advanced metering infrastructure (AMI) has
become the preferred target for network intrusion due to its bidirectional communication and …
become the preferred target for network intrusion due to its bidirectional communication and …