Senet-i: An approach for detecting network intrusions through serialized network traffic images
The exponential growth of the internet and inter-connectivity has resulted in an extensive
increase in network size and the corresponding data, which has led to numerous novel …
increase in network size and the corresponding data, which has led to numerous novel …
Payload-byte: A tool for extracting and labeling packet capture files of modern network intrusion detection datasets
Adapting modern approaches for network intrusion detection is becoming critical, given the
rapid technological advancement and adversarial attack rates. Therefore, packet-based …
rapid technological advancement and adversarial attack rates. Therefore, packet-based …
Transfer learning for raw network traffic detection
Traditional machine learning models used for network intrusion detection systems rely on
vast amounts of network traffic data with expertly engineered features. The abundance of …
vast amounts of network traffic data with expertly engineered features. The abundance of …
Deep packgen: A deep reinforcement learning framework for adversarial network packet generation
Recent advancements in artificial intelligence (AI) and machine learning (ML) algorithms,
coupled with the availability of faster computing infrastructure, have enhanced the security …
coupled with the availability of faster computing infrastructure, have enhanced the security …
Challenges and opportunities for generative methods in the cyber domain
M Chalé, ND Bastian - 2021 Winter Simulation Conference …, 2021 - ieeexplore.ieee.org
Large, high quality data sets are essential for training machine learning models to perform
their tasks accurately. The lack of such training data has constrained machine learning …
their tasks accurately. The lack of such training data has constrained machine learning …
Constrained optimization based adversarial example generation for transfer attacks in network intrusion detection systems
Deep learning has enabled network intrusion detection rates as high as 99.9% for malicious
network packets without requiring feature engineering. Adversarial machine learning …
network packets without requiring feature engineering. Adversarial machine learning …
A novel graph convolutional networks model for an intelligent network traffic analysis and classification
Network security in the midst of evolving and complex cyber-attacks is a growing concern.
As the complexity of network architectures grows, so does the need for advanced methods in …
As the complexity of network architectures grows, so does the need for advanced methods in …
[HTML][HTML] A sequential deep learning framework for a robust and resilient network intrusion detection system
Ensuring the security and integrity of computer and network systems is of utmost importance
in today's digital landscape. Network intrusion detection systems (NIDS) play a critical role in …
in today's digital landscape. Network intrusion detection systems (NIDS) play a critical role in …
A transformer-based framework for payload malware detection and classification
As malicious cyber threats become more sophisticated in breaching computer networks, the
need for effective intrusion detection systems (IDSs) becomes crucial. Techniques such as …
need for effective intrusion detection systems (IDSs) becomes crucial. Techniques such as …
Novelty detection in network traffic: Using survival analysis for feature identification
Network Intrusion Detection Systems (NIDS) are an important component of many
organizations' cyber defense, resiliency and assurance strategies. However, one downside …
organizations' cyber defense, resiliency and assurance strategies. However, one downside …