A comprehensive study of distributed Denial-of-Service attack with the detection techniques

Ibrahim, Hazem; Hamzah, A. E.; Qasim, H. H.; Hamed, O. S.; Alkhalaf, Hussein Yahya; Hamza, Muhammad

doi:10.11591/ijece.v10i4.pp3685-3694

Cited by 4 publications

(3 citation statements)

References 17 publications

(17 reference statements)

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“…The world doesn't seem like a safe place these days, but a home security system is needed to deter hackers and thieves [8], [9]. Installing home security is very important and can be a way to give us the peace of mind we deserve.…”

Section: Figure 1 Types Of Locksmentioning

confidence: 99%

Door lock system based on internet of things and Bluetooth by using Raspberry Pi

Hashim,

Qasim,

Hamzah

et al. 2023

Bulletin EEI

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Recently, there are a large number of smart lock systems that can be used in the workplace or home, but many of them depend on finding an external key, password, fingerprint, and facial recognition, where these systems suffer from weakness as the physical key may be forgotten in a place, the password can be memorized which is hard for some peoples, and the fingerprint system sometimes does not work on people who suffer from chronic diseases, such as people with diabetes and for each of these systems the person needs a free hand so that he/her can open the lock which means that is if the person carries something he/her needs to be placed somewhere. The facial recognition system cannot recognize the person's face when the place has poor lighting. This paper proposes a home door lock system that relies on the internet of things (IoT) technology and portable electronic devices Bluetooth such as a smart mobile using the device Bluetooth media access control (MAC) address as a key and the IoT for monitoring, which leads to a door lock that does not require a free hand. The proposed lock system outcome can be a good candidate for home security applications.

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Section: Figure 1 Types Of Locksmentioning

confidence: 99%

Door lock system based on internet of things and Bluetooth by using Raspberry Pi

Hashim,

Qasim,

Hamzah

et al. 2023

Bulletin EEI

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“…The study [14] presented a DDOS attack detection method using a comprehensive simulation technique of DDOS attacks.…”

Section: Related Workmentioning

confidence: 99%

Detecting Unauthorized Network Intrusion based on Network Traffic using Behavior Analysis Techniques

Lam¹

2021

IJACSA

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Nowadays, network intrusion detection is an essential problem because cyber-attacks are increasing in both the number and extent of the danger. Network intrusion techniques often use various methods to bypass the oversight of anomaly detection and surveillance systems. This paper proposes to use behavior analysis techniques, machine learning, and deep learning algorithms for the task of detecting network intrusions. The practical and scientific significance of our paper includes two issues: (1) Regarding the process of selecting and extracting features: instead of using typical abnormal behaviors of attacks, this study will use statistical behaviors that are easy to calculate and extract while still ensuring the effectiveness of the method; (2) Regarding the detection process, this study proposes to use the Random Forest (RF) classification algorithm, the Multilayer Perceptron (MLP) and the Convolutional Neural Network (CNN) deep learning model. The experimental results in Section IV have proven that our proposal in this paper is completely correct and reasonable. Based on the results shown in Section IV, this study has provided network surveillance systems with a number of abnormal behaviors as the basis for detecting network intrusions.

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“…In addition, other studies also presented methods to detect attack components in the network using machine learning algorithms. The study [16] presented a method of detecting DDOS attacks using a technique that comprehensively simulates DDOS attacks. In their study [17], Narender et al proposed a method to detect DDOS attacks using machine learning algorithms such as Logistic Regression, Decision Tree, and K-Nearest Neighbors.…”

Section: Related Work 21 Cyber-attacks Detection Based On Unsw-nb15 Datasetmentioning

confidence: 99%

Optimization of network traffic anomaly detection using machine learning

Dam

Lam

2021

IJECE

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In this paper, to optimize the process of detecting cyber-attacks, we choose to propose 2 main optimization solutions: Optimizing the detection method and optimizing features. Both of these two optimization solutions are to ensure the aim is to increase accuracy and reduce the time for analysis and detection. Accordingly, for the detection method, we recommend using the Random Forest supervised classification algorithm. The experimental results in section 4.1 have proven that our proposal that use the Random Forest algorithm for abnormal behavior detection is completely correct because the results of this algorithm are much better than some other detection algorithms on all measures. For the feature optimization solution, we propose to use some data dimensional reduction techniques such as information gain, principal component analysis, and correlation coefficient method. The results of the research proposed in our paper have proven that to optimize the cyber-attack detection process, it is not necessary to use advanced algorithms with complex and cumbersome computational requirements, it must depend on the monitoring data for selecting the reasonable feature extraction and optimization algorithm as well as the appropriate attack classification and detection algorithms.

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A comprehensive study of distributed Denial-of-Service attack with the detection techniques

Cited by 4 publications

References 17 publications

Door lock system based on internet of things and Bluetooth by using Raspberry Pi

Door lock system based on internet of things and Bluetooth by using Raspberry Pi

Detecting Unauthorized Network Intrusion based on Network Traffic using Behavior Analysis Techniques

Optimization of network traffic anomaly detection using machine learning

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