Deep learning: a comprehensive overview on techniques, taxonomy, applications and research directions
IH Sarker - SN computer science, 2021 - Springer
Deep learning (DL), a branch of machine learning (ML) and artificial intelligence (AI) is
nowadays considered as a core technology of today's Fourth Industrial Revolution (4IR or …
nowadays considered as a core technology of today's Fourth Industrial Revolution (4IR or …
Cellular, wide-area, and non-terrestrial IoT: A survey on 5G advances and the road toward 6G
The next wave of wireless technologies is proliferating in connecting things among
themselves as well as to humans. In the era of the Internet of Things (IoT), billions of …
themselves as well as to humans. In the era of the Internet of Things (IoT), billions of …
[HTML][HTML] A machine learning-based intrusion detection for detecting internet of things network attacks
Abstract The Internet of Things (IoT) refers to the collection of all those devices that could
connect to the Internet to collect and share data. The introduction of varied devices …
connect to the Internet to collect and share data. The introduction of varied devices …
Network intrusion detection system: A systematic study of machine learning and deep learning approaches
The rapid advances in the internet and communication fields have resulted in a huge
increase in the network size and the corresponding data. As a result, many novel attacks are …
increase in the network size and the corresponding data. As a result, many novel attacks are …
Explainable artificial intelligence in cybersecurity: A survey
Nowadays, Artificial Intelligence (AI) is widely applied in every area of human being's daily
life. Despite the AI benefits, its application suffers from the opacity of complex internal …
life. Despite the AI benefits, its application suffers from the opacity of complex internal …
Deep learning for anomaly detection: A survey
Anomaly detection is an important problem that has been well-studied within diverse
research areas and application domains. The aim of this survey is two-fold, firstly we present …
research areas and application domains. The aim of this survey is two-fold, firstly we present …
Machine learning techniques to detect a DDoS attack in SDN: A systematic review
The recent advancements in security approaches have significantly increased the ability to
identify and mitigate any type of threat or attack in any network infrastructure, such as a …
identify and mitigate any type of threat or attack in any network infrastructure, such as a …
An adaptive ensemble machine learning model for intrusion detection
X Gao, C Shan, C Hu, Z Niu, Z Liu - Ieee Access, 2019 - ieeexplore.ieee.org
In recent years, advanced threat attacks are increasing, but the traditional network intrusion
detection system based on feature filtering has some drawbacks which make it difficult to …
detection system based on feature filtering has some drawbacks which make it difficult to …
Improving performance of autoencoder-based network anomaly detection on nsl-kdd dataset
Network anomaly detection plays a crucial role as it provides an effective mechanism to
block or stop cyberattacks. With the recent advancement of Artificial Intelligence (AI), there …
block or stop cyberattacks. With the recent advancement of Artificial Intelligence (AI), there …
[HTML][HTML] A novel hybrid model for intrusion detection systems in SDNs based on CNN and a new regularization technique
Software-defined networking (SDN) is a new networking paradigm that separates the
controller from the network devices ie routers and switches. The centralized architecture of …
controller from the network devices ie routers and switches. The centralized architecture of …