A Method to Detect DOS and DDOS Attacks based on Generalized Likelihood Ratio Test

Harrou, Fouzi; Bouyeddou, Benamar; Sun, Ying; Kadri, Benamar

doi:10.1109/icass.2018.8652030

Cited by 9 publications

(4 citation statements)

References 19 publications

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“…The objective of the paper is to bring attention to the most vulnerable DDoS attack and its consequences in the m-healthcare system. Several researchers are carried out their researches to overcome the problem and to provide a unique solution [23][24][25]. Aditya Rajagopalan et.…”

Section: Related Workmentioning

confidence: 99%

DDoS Detection and Prevention of Attacks on M-Health Sensitive Data: A novel approach

Ray

Mishra

Dutta

2022

Preprint

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In today's world m-Health (also termed as Mobile Health) is an integral part of the healthcare industry. M-health uses various kinds of mobile devices such as mobile phones, personal digital assistants, tablets, laptops, and wireless-based arrangements to collect and transfer run-time medical data between patients and healthcare institutions. The entire process of the m-healthcare system is dependent on a cloud system. As a result of services provided through the cloud platform, it is observed that network equipment is likely prone to different types of attacks in the system. The most extensively used of these attacks are Distributed Denial of Services (DDoS) attack which can halt the network service instantly and prevent the accessing of sensitive data. The attack can destroy the privacy and security of the data by injecting wrong information into it. The most crucial way in the battle against DDoS attacks is the fast detection and taking apart of network traffic. In this research work, four layers of m-healthcare architecture and the specific reasons for DDoS attacks in the system architecture are explained properly. A novel DDoS detection algorithm is proposed for the early detection of the attack in the system. The DDoS preventive algorithms are designed efficiently to restrict the access of attackers in the system. Further, the effect of the different types of DDoS attacks with their preventive approaches experimented in the cloud-based simulation environment. The maximum success and average success of the attacker concerning time are also analyzed as a part of the research paper.

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Section: Related Workmentioning

confidence: 99%

DDoS Detection and Prevention of Attacks on M-Health Sensitive Data: A novel approach

Ray

Mishra

Dutta

2022

Preprint

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“…This could result in a significant financial loss for some companies and organizations and a loss of client trust. As a result, intrusion detection systems capable of detecting intruders against these types of attacks become critical for ensuring network security [1], [3], [4], [5], [6].…”

Section: Introductionmentioning

confidence: 99%

DDOS attacks detection based on attention-deep learning and local outlier factor

Dairi

Khaldi

Harrou

et al. 2022

2022 Seventh International Conference on Fog and Mobile Edge Computing (FMEC)

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One of the most significant security concerns confronting network technology is the detection of distributed denial of service (DDOS). This paper introduces a semi-supervised datadriven approach to the detection of DDOS attacks. The proposed method employs normal events data without labeling to train the detection model. Specifically, this approach introduces an improved autoencoder (AE) model by incorporating a Gated Recurrent Unit (GRU) based on the attention mechanism (AM) at the encoder and decoder sides of the AE model. GRU enhances the AE's ability to learn temporal dependencies, and the AM enables the selection of relevant features. For DDOS attacks detection, the local outlier factor (LOF) anomaly detection algorithm is applied to extracted features from the improved AE model. The performance of the proposed approach has been verified using DDOS publically available datasets.

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“…Notably, this mechanism considers only normally distributed traffic to discriminate attacks. In [23], the detection of ICMPv6-based DOS and DDOS attacks is addressed as a binary hypothesis testing problem based on the Generalized likelihood ratio (GLR) test. The GLR is employed to decide between the null hypothesis (normal traffic) and the alternative hypothesis (abnormal traffic).…”

Section: Introductionmentioning

confidence: 99%

Detecting network cyber-attacks using an integrated statistical approach

et al. 2020

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Anomaly detection in the Internet of Things (IoT) is imperative to improve its reliability and safety. Detecting denial of service (DOS) and distributed DOS (DDOS) is one of the critical security challenges facing network technologies. This paper presents an anomaly detection mechanism using the Kullback-Leibler distance (KLD) to detect DOS and DDOS flooding attacks, including transmission control protocol (TCP) SYN flood, UDP flood, and ICMP-based attacks. This mechanism integrates the desirable properties of KLD, the capacity to quantitatively discriminate between two distributions, with the sensitivity of an exponential smoothing scheme. The primary reason for exponentially smoothing KLD measurements (ES-KLD) is to aggregate all of the information from past and actual samples in the decision rule, making the detector sensitive to small anomalies. Furthermore, a nonparametric approach using kernel density estimation has been used to set a threshold for ES-KLD decision statistic to uncover the presence of attacks. Tests on three publicly available datasets show improved performances of the proposed mechanism in detecting cyber-attacks compared to other conventional monitoring procedures.

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A Method to Detect DOS and DDOS Attacks based on Generalized Likelihood Ratio Test

Cited by 9 publications

References 19 publications

DDoS Detection and Prevention of Attacks on M-Health Sensitive Data: A novel approach

DDoS Detection and Prevention of Attacks on M-Health Sensitive Data: A novel approach

DDOS attacks detection based on attention-deep learning and local outlier factor

Detecting network cyber-attacks using an integrated statistical approach

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