ZYELL-NCTU NetTraffic-1.0: A Large-Scale Dataset for Real-World Network Anomaly Detection

Chen, Lei; Weng, Shao-En; Peng, Chu-Jun; Shuai, Hong-Han; Cheng, Wen-Huang

doi:10.1109/icce-tw52618.2021.9602909

Cited by 7 publications

(5 citation statements)

References 2 publications

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“…Figure (1) illustrate the analysis of attack types within the dataset (KDD99). (1) The main steps of the proposed system are summarized as follows:…”

Section: The Types Of Network Attack Arementioning

confidence: 99%

“…The issue of network security has become increasingly significant due to the exponential rise of computer network usage and the proliferation of billions of applications operating within this framework. Irrespective of individual or organizational users, contemporary hackers have identified more avenues to exploit vital information [1]. Hence, the reliable detection of assaults and anomalies in networks becomes increasingly crucial for intrusion detection systems (IDSs).…”

Section: Introductionmentioning

confidence: 99%

“…Hence, the reliable detection of assaults and anomalies in networks becomes increasingly crucial for intrusion detection systems (IDSs). It is crucial to priorities developing resilient detection techniques on Intrusion Detection Systems (IDSs) to effectively safeguard against the continuously expanding range of harmful behaviors [1,2]. Network anomalies can arise from various factors, including network overload, device malfunctions, misconfigurations, malicious actions, or n e t w o r k a t t a c k s t h a t i n t e r c e p t a n d a n a l y z e s t a n d a r d n e t w o r k s e r v i c e s [ 3 , 4 ] .…”

Section: Introductionmentioning

confidence: 99%

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Multi Level Deep Learning Model for Network Anomaly Detection

S. Derweesh,

A. Hameed Alazawi,

H. Al-Saleh

2024

J. Al-Qadisiyah Comp. Sci. Math.

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The increasing use of internet-based solutions and services in private and corporate sectors has resulted in a significant increase in personal internet involvement. This shift, concurrently exposed a heightened susceptibility to potential vulnerabilities, given the ability of malicious actors to exploit external networks, network services, or corporate infrastructures utilized for personal purposes. In recent times, there has been considerable interest in harnessing deep learning methodologies for enhancing cybersecurity, owing to their utilization of sophisticated learning algorithms for addressing pertinent online security challenges. Machine Learning (ML) and Deep Learning (DL) paradigms have been extensively applied across diverse dimensions of cybersecurity, encompassing tasks such as vulnerability assessment, malware classification, spam detection, and spoofing identification. In this paper, for hierarchical intrusion detection is proposed a novel multi-stage approach, The proposed system comprises two distinct classification modalities: multi-class classification and binary classification, contingent upon the nature of the attack within the dataset. the KDD99 dataset was leveraged to assess the classification performance of the proposed model. Both classification approaches involve main preprocessing steps, such as feature selection, feature normalization, building a Convolutional Neural Network (CNN) classifier apply on KDD99 dataset, deploying the CNN classifier for anomaly detection.

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“…Figure (1) illustrate the analysis of attack types within the dataset (KDD99). (1) The main steps of the proposed system are summarized as follows:…”

Section: The Types Of Network Attack Arementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multi Level Deep Learning Model for Network Anomaly Detection

S. Derweesh,

A. Hameed Alazawi,

H. Al-Saleh

2024

J. Al-Qadisiyah Comp. Sci. Math.

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“…In [19], the researchers created a detection and prevention system for DDoS attacks based on a multiple-layer approach for an IoT-based environment. The detection of attacks can be performed online (cloud layer) or offline.…”

Section: Related Workmentioning

confidence: 99%

“…There are multiple computer programs that can perform ping floods, such as hping3, Tribe Flood Network 2k edition (TFN2K), and Scapy [16]- [19]. However, this work presents an attack program for embedded systems.…”

mentioning

confidence: 99%

IoT Network-Based Intrusion Detection Framework: A Solution to Process Ping Floods Originating From Embedded Devices

Almorabea,

Khanzada,

Aslam

et al. 2023

IEEE Access

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Internet of things (IoT) devices are gaining traction around the globe. These devices are sometimes hijacked and turned into zombies or botnets. One risk posed by hijacked devices is a ping flood attack, also known as an internet control message protocol echo request flood. Current literature lacks a ping flood attack dataset generated from an IoT device. This paper contributes by developing an IoT network intrusion detection framework for ping flood attacks. This framework deploys an IoT testbed using embedded devices to emulate two datasets, normal ping traffic and malicious ping flood attack traffic. Features are extracted from the captured traffic using the Zeek tool. Attacks are detected using three machine learning algorithms: logistic regression, K-nearest neighbor, and support vector machine. These models are compared using evaluations such as the confusion matrix, accuracy, precision, recall, F1-score, and misclassification (error rate). The models are validated using split validation and cross-fold validation. The time consumed in training and testing the models across various data levels is also analyzed, along with the time required for feature extraction. The discrepancies between capturing tools are discussed. The use of criteria based on the time difference between requests to detect malicious traffic is considered, as is the impact of machine learning models on memory usage. Our work is compared with similar research. The testbed concluded that the K-nearest neighbor algorithm achieved 99.67% detection accuracy, with an error rate of 0.33% and an F1-score of 99.67%, which is the best amongst the three algorithms.

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