Hyperband tuned deep neural network with well posed stacked sparse autoencoder for detection of DDoS attacks in cloud
A Bhardwaj, V Mangat, R Vig - IEEE Access, 2020 - ieeexplore.ieee.org
Cloud computing has very attractive features like elastic, on demand and fully managed
computer system resources and services. However, due to its distributed and dynamic …
computer system resources and services. However, due to its distributed and dynamic …
A deep learning approach combining autoencoder with one-class SVM for DDoS attack detection in SDNs
L Mhamdi, D McLernon, F El-Moussa… - 2020 IEEE Eighth …, 2020 - ieeexplore.ieee.org
Software Defined Networking (SDN) provides us with the capability of collecting network
traffic information and managing networks proactively. Therefore, SDN facilitates the …
traffic information and managing networks proactively. Therefore, SDN facilitates the …
Towards DDoS attack detection using deep learning approach
Due to the extensive use and evolution in the cyber world, different network attacks have
recently increased significantly. Distributed Denial-of-Service (DDoS) attack has become …
recently increased significantly. Distributed Denial-of-Service (DDoS) attack has become …
Efficient detection of DDoS attacks using a hybrid deep learning model with improved feature selection
DDoS (Distributed Denial of Service) attacks have now become a serious risk to the integrity
and confidentiality of computer networks and systems, which are essential assets in today's …
and confidentiality of computer networks and systems, which are essential assets in today's …
Deep learning approaches for detecting DDoS attacks: A systematic review
In today's world, technology has become an inevitable part of human life. In fact, during the
Covid-19 pandemic, everything from the corporate world to educational institutes has shifted …
Covid-19 pandemic, everything from the corporate world to educational institutes has shifted …
Ae-mlp: A hybrid deep learning approach for ddos detection and classification
Distributed Denial-of-Service (DDoS) attacks are increasing as the demand for Internet
connectivity massively grows in recent years. Conventional shallow machine learning-based …
connectivity massively grows in recent years. Conventional shallow machine learning-based …
A generalized machine learning model for DDoS attacks detection using hybrid feature selection and hyperparameter tuning
In the digital era, the usage of network-connected devices is rapidly growing which leads to
an increase in cyberattacks. Among them, Distributed Denial of Service (DDoS) attacks are …
an increase in cyberattacks. Among them, Distributed Denial of Service (DDoS) attacks are …
Optimization Enabled Deep Learning‐Based DDoS Attack Detection in Cloud Computing
S Balasubramaniam, C Vijesh Joe… - … Journal of Intelligent …, 2023 - Wiley Online Library
Cloud computing is a vast revolution in information technology (IT) that inhibits scalable and
virtualized sources to end users with low infrastructure cost and maintenance. They also …
virtualized sources to end users with low infrastructure cost and maintenance. They also …
Fuzzy-Taylor-elephant herd optimization inspired Deep Belief Network for DDoS attack detection and comparison with state-of-the-arts algorithms
S Velliangiri, HM Pandey - Future Generation Computer Systems, 2020 - Elsevier
Cloud computing environment support resource sharing as cloud service over the internet. It
enables the users to outsource data into the cloud server that can be accessed remotely …
enables the users to outsource data into the cloud server that can be accessed remotely …
Detection of DDOS attack using deep learning model in cloud storage application
In recent years, distributed denial of service (DDoS) attacks pose a serious threat to network
security. How to detect and defend against DDoS attacks is currently a hot topic in both …
security. How to detect and defend against DDoS attacks is currently a hot topic in both …