Machine learning for encrypted malicious traffic detection: Approaches, datasets and comparative study
As people's demand for personal privacy and data security becomes a priority, encrypted
traffic has become mainstream in the cyber world. However, traffic encryption is also …
traffic has become mainstream in the cyber world. However, traffic encryption is also …
Towards sFlow and adaptive polling sampling for deep learning based DDoS detection in SDN
Abstract Distributed Denial of Service (DDoS) is one of the most rampant attacks in the
modern Internet of Things (IoT) network infrastructures. Security plays a very vital role for an …
modern Internet of Things (IoT) network infrastructures. Security plays a very vital role for an …
Detecting HTTP-based application layer DoS attacks on web servers in the presence of sampling
A recent escalation of application layer Denial of Service (DoS) attacks on the Internet has
quickly shifted the interest of the research community traditionally focused on network-based …
quickly shifted the interest of the research community traditionally focused on network-based …
[图书][B] The state of the art in intrusion prevention and detection
ASK Pathan - 2014 - api.taylorfrancis.com
Most of the security threats in various communications networks are posed by the illegitimate
entities that enter or intrude within the network perimeter, which could commonly be termed …
entities that enter or intrude within the network perimeter, which could commonly be termed …
Intrusion detection in the era of IoT: Building trust via traffic filtering and sampling
W Meng - Computer, 2018 - ieeexplore.ieee.org
In the Internet of Things (IoT) era, the number of connected devices and subnets of devices
is rapidly increasing. Yet, it remains a challenge for intrusion detection mechanisms to build …
is rapidly increasing. Yet, it remains a challenge for intrusion detection mechanisms to build …
Enhancing trust management for wireless intrusion detection via traffic sampling in the era of big data
Internet of Things (IoT) has been widely used in our daily life, which enables various objects
to be interconnected for data exchange, including physical devices, vehicles, and other …
to be interconnected for data exchange, including physical devices, vehicles, and other …
[PDF][PDF] An overview of flow-based and packet-based intrusion detection performance in high speed networks
H Alaidaros, M Mahmuddin, A Al Mazari - Proceedings of the …, 2011 - academia.edu
Network Intrusion Detection Systems (NIDSs) are widely-deployed security tools for
detecting cyber-attacks and activities conducted by intruders for observing network traffics …
detecting cyber-attacks and activities conducted by intruders for observing network traffics …
Suspicious traffic sampling for intrusion detection in software-defined networks
In order to defend a cloud computing system from security attackers, an intrusion detection
system (IDS) is widely used to inspect suspicious traffic on the network. However, the …
system (IDS) is widely used to inspect suspicious traffic on the network. However, the …
Network anomaly detection and classification via opportunistic sampling
G Androulidakis, V Chatzigiannakis… - IEEE …, 2009 - ieeexplore.ieee.org
In this article the emphasis is placed on the evaluation of the impact of intelligent flow
sampling techniques on the detection and classification of network anomalies. Based on the …
sampling techniques on the detection and classification of network anomalies. Based on the …
Anomaly detection approaches for communication networks
In recent years, network anomaly detection has become an important area for both
commercial interests as well as academic research. Applications of anomaly detection …
commercial interests as well as academic research. Applications of anomaly detection …