A Network Traffic Collection System for Space Information Networks Emulation Platform

Kuang, Chengpeng; Hou, Dongxu; Zhang, Qi; Zhao, Kanglian; Li, Wenfeng

doi:10.1007/978-3-030-93398-2_22

Cited by 2 publications

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A bash script, named a Shark script, was programed for the purpose of feature extraction from PCAP files so that the network transmission characteristics and properties could be studied. The developed script was able to extract 46 features from a PCAP file using Tshark [ 30 ], which is a command line [ 31 ] version of Wireshark that can capture live traffic or investigate packets from PCAP files. It writes its output in a file using various kinds of filters that can be applied to assess a particular packet.…”

Section: Proposed Methodologymentioning

confidence: 99%

Lightweight Internet of Things Botnet Detection Using One-Class Classification

Malik

Rehman

Maqsood

et al. 2022

Sensors

View full text Add to dashboard Cite

Like smart phones, the recent years have seen an increased usage of internet of things (IoT) technology. IoT devices, being resource constrained due to smaller size, are vulnerable to various security threats. Recently, many distributed denial of service (DDoS) attacks generated with the help of IoT botnets affected the services of many websites. The destructive botnets need to be detected at the early stage of infection. Machine-learning models can be utilized for early detection of botnets. This paper proposes one-class classifier-based machine-learning solution for the detection of IoT botnets in a heterogeneous environment. The proposed one-class classifier, which is based on one-class KNN, can detect the IoT botnets at the early stage with high accuracy. The proposed machine-learning-based model is a lightweight solution that works by selecting the best features leveraging well-known filter and wrapper methods for feature selection. The proposed strategy is evaluated over different datasets collected from varying network scenarios. The experimental results reveal that the proposed technique shows improved performance, consistent across three different datasets used for evaluation.

show abstract

Section: Proposed Methodologymentioning

confidence: 99%

Lightweight Internet of Things Botnet Detection Using One-Class Classification

Malik

Rehman

Maqsood

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

Effective DDoS attack detection in software-defined vehicular networks using statistical flow analysis and machine learning

Babbar,

Rani,

Driss

2024

PLoS ONE

View full text Add to dashboard Cite

Vehicular Networks (VN) utilizing Software Defined Networking (SDN) have garnered significant attention recently, paralleling the advancements in wireless networks. VN are deployed to optimize traffic flow, enhance the driving experience, and ensure road safety. However, VN are vulnerable to Distributed Denial of Service (DDoS) attacks, posing severe threats in the contemporary Internet landscape. With the surge in Internet traffic, this study proposes novel methodologies for effectively detecting DDoS attacks within Software-Defined Vehicular Networks (SDVN), wherein attackers commandeer compromised nodes to monopolize network resources, disrupting communication among vehicles and between vehicles and infrastructure. The proposed methodology aims to: (i) analyze statistical flow and compute entropy, and (ii) implement Machine Learning (ML) algorithms within SDN Intrusion Detection Systems for Internet of Things (IoT) environments. Additionally, the approach distinguishes between reconnaissance, Denial of Service (DoS), and DDoS traffic by addressing the challenges of imbalanced and overfitting dataset traces. One of the significant challenges in this integration is managing the computational load and ensuring real-time performance. The ML models, especially complex ones like Random Forest, require substantial processing power, which necessitates efficient data handling and possibly leveraging edge computing resources to reduce latency. Ensuring scalability and maintaining high detection accuracy as network traffic grows and evolves is another critical challenge. By leveraging a minimal subset of features from a given dataset, a comparative study is conducted to determine the optimal sample size for maximizing model accuracy. Further, the study evaluates the impact of various dataset attributes on performance thresholds. The K-nearest Neighbor, Random Forest, and Logistic Regression supervised ML classifiers are assessed using the BoT-IoT dataset. The results indicate that the Random Forest classifier achieves superior performance metrics, with Precision, F1-score, Accuracy, and Recall rates of 92%, 92%, 91%, and 90%, respectively, over five iterations.

show abstract

A Network Traffic Collection System for Space Information Networks Emulation Platform

Cited by 2 publications

References 7 publications

Lightweight Internet of Things Botnet Detection Using One-Class Classification

Lightweight Internet of Things Botnet Detection Using One-Class Classification

Effective DDoS attack detection in software-defined vehicular networks using statistical flow analysis and machine learning

Contact Info

Product

Resources

About