Feature Engineering and Machine Learning Framework for DDoS Attack Detection in the Standardized Internet of Things

Kamaldeep,; Malik, Manisha; Dutta, Maitreyee

doi:10.1109/jiot.2023.3245153

Cited by 30 publications

(23 citation statements)

References 32 publications

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“…Kamaldeep et al 80 proposed a feature engineering and machine learning framework for detecting DDoS attacks in standardized IoT networks using a novel dataset called “IoT‐CIDDS,” which contains 21 features and a single labelling attribute. The framework has two phases: in the first phase, the algorithms are developed for dataset enrichment and advanced feature engineering, including statistical analysis of the dataset with probability distribution and correlation among features.…”

Section: Ml‐based Ddos Detection Methodsmentioning

confidence: 99%

DDoS attacks and machine‐learning‐based detection methods: A survey and taxonomy

Najafimehr

Zarifzadeh

Mostafavi

2023

Engineering Reports

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Distributed denial of service (DDoS) attacks represent a significant cybersecurity challenge, posing a critical risk to computer networks. Developing an effective defense mechanism against these attacks is crucial but challenging, given their diverse attack types, network and computing platform heterogeneity, and complex communication protocols. Moreover, the emergence of innovative DDoS attack methods presents a formidable threat to existing countermeasures. Various machine learning techniques have shown promise in detecting DDoS attacks with low false‐positive rates and high detection rates. This survey paper offers a comprehensive taxonomy of machine learning‐based methods for detecting DDoS attacks, reviewing supervised, unsupervised, hybrid approaches, and analyzing the related challenges. Further, we explore relevant datasets, highlighting their strengths and limitations, and propose future research directions to address the current gaps in this domain. This paper aims to provide a profound understanding of DDoS attack detection mechanisms, aiding researchers, and practitioners in developing effective cybersecurity approaches against such attacks. This research is essential because DDoS attacks are diverse and pose a formidable threat to computer networks, and various machine learning techniques have shown promise in detecting them. Its implications include providing insights that can inform the development of robust defense mechanisms against DDoS attacks.

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Section: Ml‐based Ddos Detection Methodsmentioning

confidence: 99%

DDoS attacks and machine‐learning‐based detection methods: A survey and taxonomy

Najafimehr

Zarifzadeh

Mostafavi

2023

Engineering Reports

View full text Add to dashboard Cite

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“…Their model included data collection and preprocessing stages, which employed ML algorithms such as LR, SVM, DT, RF, and Artificial Neural Network (ANN). Malik et al [21] proposed a framework for feature engineering and ML to detect DDoS attacks in IoT networks. It introduced the IoT-CIDDS dataset, focusing on feature engineering and statistical analysis for effective attack detection.…”

Section: Related Workmentioning

confidence: 99%

A Genetic Algorithm- and t-Test-Based System for DDoS Attack Detection in IoT Networks

Saiyed,

Al-Anbagi

2024

IEEE Access

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Internet and cloud-based technologies have facilitated the implementation of large-scale Internet of Things (IoT) networks. However, these networks are susceptible to emerging attacks. This paper proposes a novel lightweight system for detecting both high-and low-volume Distributed Denial of Service (DDoS) attacks in IoT networks, namely Genetic Algorithm (GA) and t-Test for DDoS Attack Detection (GADAD). The GADAD system employs edge-based technologies and has three phases. In the first phase, it creates and preprocesses an HL-IoT (High-and Low-volume attacks in IoT networks) dataset, which includes both high-and low-volume DDoS attacks. The second phase introduces a novel and lightweight method, called GAStats, for optimal feature selection using the GA and statistical parameters (Stats.). In the third phase, the system trains three tree-based Machine Learning (ML) models: Random Forest (RF), Extra-Tree (ET), and Adaptive Boosting (AdaBoost), along with other ML models, using both the selfgenerated HL-IoT dataset and the publicly available ToN-IoT dataset. The evaluation includes the assessment of key performance metrics such as accuracy, precision, recall, F1-score, Receiver Operating Characteristic Curve (ROC), computation time, and scalability analysis with overall system performance. The experimental results illustrate the efficacy of the feature selection method in optimizing the system's efficiency in detecting DDoS attacks in IoT networks, along with a reduction in computation time compared to existing state-ofthe-art techniques.

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“…To identify DDoS bouts in the IoT-CIDDS dataset, Malik et al [19] suggest a feature engineering and machine learning outline. There are two stages to the framework: Our initial step is to create algorithms for dataset enrichment, with a focus on using cutting-edge feature engineering for statistical analysis of the dataset's probability distribution and feature correlations.…”

Section: Related Workmentioning

confidence: 99%

“…Combine the cats in both the searching and tracing modes, making sure that no spots are beyond the range [0,1]. It is necessary to change the solution by means of Equation if it is larger than the search space (19).…”

mentioning

confidence: 99%

Routing Attack Detection Using Ensemble Deep Learning Model for IIoT

Vatambeti¹,

Mamidisetti²

2023

IDA

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Smart cities, ITS, supply chains, and smart industries may all be developed with minimal human interaction thanks to the increasing prevalence of automation enabled by machine-type communication (MTC). Yet, MTC has substantial security difficulties because of diverse data, public network access, and an insufficient security mechanism. In this study, we develop a novel IIOT attack detection basis by joining the following four main steps: (a) data collection, (b) pre-processing, (c) attack detection, and (d) optimisation for high classification accuracy. At the initial stage of processing, known as "pre-processing," the collected raw data (input) is normalised. Attack detection requires the creation of an intelligent security architecture for IIoT networks. In this work, we present a learning model that can recognise previously unrecognised attacks on an IIoT network without the use of a labelled training set. An IoT network intrusion detection system-generated labelled dataset. The study also introduces a hybrid optimisation algorithm for pinpointing the optimal LSTM weight when it comes to intrusion detection. When trained on the labelled dataset provided by the proposed method, the improved LSTM outperforms the other models with a finding accuracy of 95%, as exposed in the research.

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Feature Engineering and Machine Learning Framework for DDoS Attack Detection in the Standardized Internet of Things

Cited by 30 publications

References 32 publications

DDoS attacks and machine‐learning‐based detection methods: A survey and taxonomy

DDoS attacks and machine‐learning‐based detection methods: A survey and taxonomy

A Genetic Algorithm- and t-Test-Based System for DDoS Attack Detection in IoT Networks

Routing Attack Detection Using Ensemble Deep Learning Model for IIoT

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