[HTML][HTML] A deep density based and self-determining clustering approach to label unknown traffic
Analyzing non-labeled data is a major concern in the field of intrusion detection as the attack
clusters are continuously evolving which are unknown for the system. Many studies have …
clusters are continuously evolving which are unknown for the system. Many studies have …
Performance evaluation of a combined anomaly detection platform
Hybrid Anomaly Detection Model (HADM) is a platform that filters network traffic and
identifies malicious activities on the network. The platform applies data mining techniques to …
identifies malicious activities on the network. The platform applies data mining techniques to …
[PDF][PDF] Machine Learning Techniques to Detect Known and Novel Cyber-attacks
M Monshizadeh - 2023 - aaltodoc.aalto.fi
Intrusion detection systems are considered well-known tools for monitoring and detecting
malicious traffic in communication networks. However, traditional intrusion detection systems …
malicious traffic in communication networks. However, traditional intrusion detection systems …
LiaaS: Lawful Interception as a Service
M Monshizadeh, V Khatri, M Varfan… - 2018 26th …, 2018 - ieeexplore.ieee.org
Machine learning techniques are the key to success for big data analytics in forthcoming 5G
and cloud networks. Internet Service Providers (ISPs) and mobile networks are still relying …
and cloud networks. Internet Service Providers (ISPs) and mobile networks are still relying …
IoT Use Cases and Implementations: Healthcare
M Monshizadeh, V Khatri, O Koskimies… - IoT Security …, 2020 - Wiley Online Library
This chapter introduces a secure digital remote patient monitoring solution, which is a
healthcare Internet of Things system that can be implemented on virtual machines. With this …
healthcare Internet of Things system that can be implemented on virtual machines. With this …