logo

A Deep Transfer Learning Approach for Flow-based Intrusion Detection in SDN-enabled Network

RESEARCH CREW
19:16 02/06/2021

Revolutionizing operation model of traditional network in programmability, scalability, and orchestration, Software-Defined Networking (SDN) has considered as a novel network management approach for a massive network with heterogeneous devices. However, it is also highly susceptible to security attacks like conventional network. Inspired from the success of different machine learning algorithms in other domains, many intrusion detection systems (IDS) are presented to identify attacks aiming to harm the network. In this paper, leveraging the flow-based nature of SDN, we introduce DeepFlowIDS, a deep learning (DL)-based approach for anomaly detection using the flow analysis method in SDN. Furthermore, instead of using a lot of network properties, we only utilize essential characteristics of traffic flows to analyze with deep neural networks in IDS. This is to reduce the computational and time cost of attack traffic detection. Besides, we also study the practical benefits of applying deep transfer learning from computer vision to intrusion detection. This method can inherit the knowledge of an effective DL model from other contexts to resolve another task in cybersecurity. Our DL-based IDSs are built and trained with the NSL-KDD and CICIDS2018 dataset in both fine-tuning and feature extractor strategy of transfer learning. Then, it is integrated with the SDN controller to analyze traffic flows retrieved from OpenFlow statistics to recognize the anomaly action in the network.

TIN LIÊN QUAN
An Approach of Adaptive Cyber Deception for Active Cyber-attack Defense Method based on Deep Reinforcement Learning
The diverse landscape of network models, including Software-Defined Networking (SDN), Cloud Computing (C2), and Internet of Things (IoT), is evolving to meet the demands of flexibility and performance. However, these environments face numerous security challenges due to cyber-attack complexity. Traditional defense mechanisms are no longer effective against modern attacks. Therefore,...
Multimodal Deep Learning Feedback for Generating Evasive Malware Samples against Malware Detector
As data driven-based Windows malware detectors become increasingly prevalent, the need for robust evaluation and enhancement of adversarial malware generation techniques also becomes imperative, as malicious actors will adapt and enhance their malware to evade detection. There are numerous works that introduce new techniques or enhancements for adversarial malware. One...
Medichain: A multi-chain based system for managing prescription medications using Zero Knowledge Proofs
The abuse of prescription medications has become a severe public health crisis fueled by limited coordination and oversight across healthcare systems. Current frameworks lack interoperability between doctors, pharmacies, and regulators, enabling abusive practices like doctor shopping and pharmacy hopping. To address these issues, this research proposes Medichain, a novel multichain-based...