Search Results for author:
Found 6 papers, 1 papers with code
Holmes: An Efficient and Lightweight Semantic Based Anomalous Email Detector
Email threat is a serious issue for enterprise security, which consists of various malicious scenarios, such as phishing, fraud, blackmail and malvertisement.
DualNet: Locate Then Detect Effective Payload with Deep Attention Network
Network intrusion detection (NID) is an essential defense strategy that is used to discover the trace of suspicious user behaviour in large-scale cyberspace, and machine learning (ML), due to its capability of automation and intelligence, has been gradually adopted as a mainstream hunting method in recent years.
Densely Connected Residual Network for Attack Recognition
High false alarm rate and low detection rate are the major sticking points for unknown threat perception.
Pelican: A Deep Residual Network for Network Intrusion Detection
To address this problem, the network intrusion detection system (NIDS) has been used.
LuNet: A Deep Neural Network for Network Intrusion Detection
Our experiments on two network traffic datasets show that compared to the state-of-the-art network intrusion detection techniques, LuNet not only offers a high level of detection capability but also has a much low rate of false positive-alarm.
A Transfer Learning Approach for Network Intrusion Detection
ConvNet can also be applied to network intrusion detection.
