Browsing Wayamba University Research Congress (WURC) by Subject "Anomaly detection"

Now showing items 1-3 of 3

Effective Feature Reduction for Classifying IoT Security Threats Using One Class – SVM

Samanwickrama, WD; Munasinghe, Lankeshwara; Perera, LDRD (Wayamba University Research Congress, 2019)

IoT usage has shown rapid increase in the smart world. It has estimated that the number of IoT devices will be over 5 billion by 2020. With the rapid increase in IoT usage, ensuring network security of IoT has become ...
Efficacy of using SVM on Iot Security Threat Identification under Reduced Feature Set Architecture Compared to J48 and Random Forest Algorithms using UCI Benchmark Data Set

Samanwickrama, WD; Munasinghe, Lankeshwara; Perera, LDRD (Wayamba University Research Congress, 2020)

More than 5 billion IoT devices are on the Internet due to smart technology development in 2020. Most of them are connected to the Internet and they will create an easy gateway to hackers. So the network security has ...
Host based machine learning approach to detect data exfiltration on computer networks

Samanwickrama, WD; Wickramasinghe, Indika; Perera, LDRD (Wayamba University Research Congress, 2018)

Data exfiltration, unauthorized transfer of sensitive information, is a most common threat on computer networks. lt uses legitimate communication channels such as hypertext transfer protocol (HTTP) and hypertext transfer ...

Search DSpace