Software defined networking architecture, security and energy efficiency: A survey
DB Rawat, SR Reddy - IEEE Communications Surveys & …, 2016 - ieeexplore.ieee.org
Software-defined networking (SDN) is an emerging paradigm, which breaks the vertical
integration in traditional networks to provide the flexibility to program the network through …
integration in traditional networks to provide the flexibility to program the network through …
Flow-based intrusion detection: Techniques and challenges
MF Umer, M Sher, Y Bi - Computers & Security, 2017 - Elsevier
Flow-based intrusion detection is an innovative way of detecting intrusions in high-speed
networks. Flow-based intrusion detection only inspects the packet header and does not …
networks. Flow-based intrusion detection only inspects the packet header and does not …
GPT-2C: A parser for honeypot logs using large pre-trained language models
F Setianto, E Tsani, F Sadiq, G Domalis… - Proceedings of the …, 2021 - dl.acm.org
Deception technologies like honeypots generate large volumes of log data, which include
illegal Unix shell commands used by latent intruders. Several prior works have reported …
illegal Unix shell commands used by latent intruders. Several prior works have reported …
Deep learning to detect botnet via network flow summaries
A Pektaş, T Acarman - Neural Computing and Applications, 2019 - Springer
Compromised computer systems on the Internet, namely botnets, receive commands and
share information with their central malicious systems while executing frequent and common …
share information with their central malicious systems while executing frequent and common …
A novel and highly efficient botnet detection algorithm based on network traffic analysis of smart systems
L Duan, J Zhou, Y Wu, W Xu - International Journal of …, 2022 - journals.sagepub.com
In smart systems, attackers can use botnets to launch different cyber attack activities against
the Internet of Things. The traditional methods of detecting botnets commonly used machine …
the Internet of Things. The traditional methods of detecting botnets commonly used machine …
A deep learning method to detect network intrusion through flow‐based features
A Pektaş, T Acarman - International Journal of Network …, 2019 - Wiley Online Library
In this paper, we present a deep neural network model to enhance the intrusion detection
performance. A deep learning architecture combining convolution neural network and long …
performance. A deep learning architecture combining convolution neural network and long …
Botnet detection based on network flow summary and deep learning
A Pektaş, T Acarman - International Journal of Network …, 2018 - Wiley Online Library
A botnet is a group of compromised Internet‐connected devices controlled remotely by cyber
criminals to launch coordinated attacks and to perform various malicious activities. Since …
criminals to launch coordinated attacks and to perform various malicious activities. Since …
[HTML][HTML] Role-based lateral movement detection with unsupervised learning
BA Powell - Intelligent Systems with Applications, 2022 - Elsevier
Adversarial lateral movement via compromised accounts remains difficult to discover via
traditional rule-based defenses because it generally lacks explicit indicators of compromise …
traditional rule-based defenses because it generally lacks explicit indicators of compromise …
A Network Traffic Abnormal Detection Method: Sketch-Based Profile Evolution
J Yi, S Zhang, L Tan, Y Tian - Applied Sciences, 2023 - mdpi.com
Network anomaly detection faces unique challenges from dynamic traffic, including large
data volume, few attributes, and human factors that influence it, making it difficult to identify …
data volume, few attributes, and human factors that influence it, making it difficult to identify …