Utilizing Adaptive Machine Learning Algorithms for Information Risk Warning and Network Security Scenario Awareness in Cloud Computing Environments

Summary

The paper proposes a novel Machine Learning (ML)-based solution of adaptive network security in a cloud-based system that integrates hierarchical multi-label classification and a dynamic trust evaluation system to advance the accuracy of threat detection and decrease the number of false positives.

Abstract

This study proposes a novel framework for network security situational awareness and risk warning in cloud computing environments, integrating adaptive Machine Learning (ML), Hierarchical Multi-Label Classification (HMC), and a dynamic trust evaluation mechanism based on the cloud model. The complexity, diversity, and real-time nature of emerging cyberattacks-such as zero-day exploits, distributed denial-of-service (DDoS), and botnets-pose significant challenges to traditional rule-based and static detection methods. To address these challenges, we developed an effective SDN-based cloud architecture utilizing the Ryu OpenFlow controller and OpenFlow switches. This architecture enables real-time link information collection, dynamic scheduling, and scalable, reliable data transmission. The hierarchical classification framework suggested can break multiclass problems into binary tasks, alleviating the effect of sample imbalance and enhancing the recognition of low-frequency attacks, including User to Root (U2R). Ensemble learning techniques, including AdaBoost and Bagging, further enhance detection accuracy for fine-grained attack types. Experiments conducted on DDoS datasets, cloud traffic data, and simulations in Mininet and EstiNet demonstrate that the combined ML-HMC-trust approach significantly improves detection precision, reduces false positives, and enables real-time response. These results confirm that integrating adaptive learning, hierarchical classification, and dynamic trust evaluation provides a robust and scalable solution for securing large-scale cloud platforms.

Introduction

With the widespread application of cloud computing technology in various industries, the scale and amount of data in information systems are increasing rapidly, and network threats are becoming more complex, hidden, and dynamic^1,2. Traditional security defense mechanisms based on rules and static models are no longer able to meet the requirements of real-time detection with accurate early warning when facing changing attack strategies, zero-day vulnerabilities, and large-scale distributed attacks³. Therefore, leveraging adaptive ML algorithms to fully integrate distributed data processi....

Protocol

NOTE: This protocol describes how to construct a cloud-based network security situational awareness system and implement hierarchical classification with dynamic trust evaluation. Follow the steps below to design the cloud network topology, collect and annotate data flows, and deploy the hierarchical multiclass classification and trust assessment modules. Figure 1 illustrates the proposed SDN-cloud framework integrating adaptive ML, hierarchical classification, and trust evaluation for real-time attack detection .

1. Cloud network topology design

NOTE: Ensure administrative access to O....

Results

Experimental validation and performance analysis

Cloud-based validation

To test the efficiency and feasibility of the proposed algorithm, simulation tests were performed in a controlled network laboratory setting. The verification was conducted on the Windows operating system, and the core algorithm is coded in VC (Visual C++) programming tools.

In case of experimental data, we chose the publicly availabl.......

Discussion

An effective deployment of this protocol relies on critical steps within the cloud-based architecture. Proper configuration of the Ryu OpenFlow controller, correct setup of Open vSwitch rules, and robust formation of a multi-layer topology are essential to ensure full traffic capture. The selection of Ryu as the controller and Open vSwitch as the switching platform significantly strengthens the system's practical value; their lightweight, modular, and fully programmable characteristics make them ideal for real-time netwo.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
AdaBoost (Ensemble Learning Library)	Scikit-learn, Python	https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html	Software
Bagging Classifier	Scikit-learn, Python	https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.BaggingClassifier.html	Software
Cloud Model Trust Evaluation Code	Custom implementation	N/A	Algorithm/Software
Convolutional Neural Network (CNN)	TensorFlow / PyTorch	https://www.tensorflow.org/tutorials/images/cnn	Software
Deep Learning Frameworks (MLP, RNN, LSTM, GRU)	TensorFlow / PyTorch		Software
EstiNet Network Simulator	EstiNet Technologies	https://sites.google.com/view/estinet-network-simulator	Software
Kafka (Data Streaming Platform)	Apache Foundation	https://kafka.apache.org/	Software
KDD CUP 10% Dataset	UCI Machine Learning Repository	http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html	Dataset
Mininet Emulator	Mininet Project	Mininet 2.3.1	Network emulation for SDN topology, bandwidth, and mixed attack simulation.
Open vSwitch (OVS)	Open vSwitch Org	OVS 3.2.2	Virtual switch implementing flow-table control and attack traffic redirection.
OpenStack Cloud Platform	Open Infrastructure Foundation	https://www.openstack.org/	Cloud Software
Python 3.x	Python Software Foundation	https://www.python.org/downloads/	Programming Language
Ryu SDN Controller	NTT R&D	Ryu 4.34	SDN controller for real-time network traffic capture and situation awareness.
Spark Streaming Framework	Apache Foundation	https://spark.apache.org/docs/latest/streaming-programming-guide.html	Software
Visual C++ (VC++) Compiler	Microsoft	https://visualstudio.microsoft.com/	Software
Windows 11 Workstation	Microsoft	Windows 11 Pro 23H2	OS used for model compilation, training, and testing.