A detailed investigation and analysis of using machine learning techniques for intrusion detection
Intrusion detection is one of the important security problems in todays cyber world. A
significant number of techniques have been developed which are based on machine …
significant number of techniques have been developed which are based on machine …
A survey of data mining and machine learning methods for cyber security intrusion detection
AL Buczak, E Guven - IEEE Communications surveys & tutorials, 2015 - ieeexplore.ieee.org
This survey paper describes a focused literature survey of machine learning (ML) and data
mining (DM) methods for cyber analytics in support of intrusion detection. Short tutorial …
mining (DM) methods for cyber analytics in support of intrusion detection. Short tutorial …
An enhanced intrusion detection model based on improved kNN in WSNs
Aiming at the intrusion detection problem of the wireless sensor network (WSN), considering
the combined characteristics of the wireless sensor network, we consider setting up a …
the combined characteristics of the wireless sensor network, we consider setting up a …
Data mining and machine learning methods for sustainable smart cities traffic classification: A survey
This survey paper describes the significant literature survey of Sustainable Smart Cities
(SSC), Machine Learning (ML), Data Mining (DM), datasets, feature extraction and selection …
(SSC), Machine Learning (ML), Data Mining (DM), datasets, feature extraction and selection …
Threat analysis of IoT networks using artificial neural network intrusion detection system
The Internet of things (IoT) is still in its infancy and has attracted much interest in many
industrial sectors including medical fields, logistics tracking, smart cities and automobiles …
industrial sectors including medical fields, logistics tracking, smart cities and automobiles …
Attention based multi-agent intrusion detection systems using reinforcement learning
Designing an effective network intrusion system (IDS) is a challenging problem because of
the emergence of a large number of novel attacks and heterogeneous network applications …
the emergence of a large number of novel attacks and heterogeneous network applications …
[PDF][PDF] Detecting distributed denial of service attacks using data mining techniques
Users and organizations find it continuously challenging to deal with distributed denial of
service (DDoS) attacks.. The security engineer works to keep a service available at all times …
service (DDoS) attacks.. The security engineer works to keep a service available at all times …
The use of computational intelligence in intrusion detection systems: A review
SX Wu, W Banzhaf - Applied soft computing, 2010 - Elsevier
Intrusion detection based upon computational intelligence is currently attracting
considerable interest from the research community. Characteristics of computational …
considerable interest from the research community. Characteristics of computational …
Machine learning for security and the internet of things: the good, the bad, and the ugly
The advancement of the Internet of Things (IoT) has allowed for unprecedented data
collection, automation, and remote sensing and actuation, transforming autonomous …
collection, automation, and remote sensing and actuation, transforming autonomous …
Random-forests-based network intrusion detection systems
Prevention of security breaches completely using the existing security technologies is
unrealistic. As a result, intrusion detection is an important component in network security …
unrealistic. As a result, intrusion detection is an important component in network security …