A performance evaluation of data streams sampling algorithms over a sliding window

et al. 2021

IEEE Access

Self Cite

Given that the Internet traffic speed and volume are growing at a rapid pace, monitoring the network in a real-time manner has introduced several issues in terms of computing and storage capabilities. Fast processing of traffic data and early warnings on the detected attacks are required while maintaining a single pass over the traffic measurements. To palliate these problems, one can reduce the amount of traffic to be processed by using a sampling technique and detect the attacks based on the sampled traffic. Different parameters have an impact on the efficiency of this process, mainly, the applied sampling policy and sampling ratio. In this paper, we investigate the statistical impact of sampling the network traffic and we quantify the amount of deterioration that the sampling process introduces. In this context, an experimental comparison of existing sampling techniques is performed based on their impact on several well-known statistical measures.

Section: B Problem Defintion and Motivationmentioning

confidence: 99%

“…To sample k packets from a window of size n, each packet is selected with a probability p equal to the sampling ratio k/n. This step must be repeated until the selection of k distinct packets [20].…”

Section: B Simple Random Sampling (Srs) Over a Fixed Window (Srsfw)mentioning

confidence: 99%

A Critical Review on the Implementation of Static Data Sampling Techniques to Detect Network Attacks

Hajj

Sibai²,

et al. 2021

IEEE Access

Self Cite

“…The challenge is to decide what to store in this summary and how to ensure that the summary can meet the requirements of the application while respecting the available system resources. El Sibai et al [33, 34] studied the performance of several sampling algorithms in terms of their execution time and accuracy of the queries answers.…”

Section: Related Workmentioning

confidence: 99%

“…It consists of selecting, at any time, exactly k items among the w most recent items in the jumping window. Assuming that the data recorded by each sensor have an always‐increasing index, each incoming record will be sampled if its index is equal to

x \times n / k

, where

x > 0

[34].…”

Section: Related Workmentioning

confidence: 99%

Toward fast and accurate emergency cases detection in BSNs

Boudargham

Sibai

et al. 2020

IET wirel. sens. syst.

Self Cite

In body sensor networks (BSNs), medical sensors capture physiological data from the human body and send them to the coordinator who act as a gateway to health care. The main aim of BSNs is to save peoples' lives. Therefore, fast and correct detection of emergencies while maintaining low-energy consumption of sensors is essential requirement of BSNs. In this study, the authors propose a new adaptive data sampling approach, where the sampling ratio is adapted based on the sensed data variation. The idea is to use the modified version of the cumulative sum (CUSUM) algorithm (modified CUSUM) that they previously proposed for wireless sensor networks to monitor the data variability, and adapt the sampling rate accordingly. Modified CUSUM is then applied to the adaptively sampled data to detect anomalies, and the correlation property between physiological parameters will be used to identify emergency cases from false alarms. Several experiments are performed and compared to evaluate the efficiency of their approach, and different parameters are considered.

“…To sample the data stream, four algorithms are used: Simple Random Sampling (SRS), Deterministic sampling, Chain‐sample, and Weighted Random Sampling (WRS). More details about these techniques can be found in Reference 16. The dataset we are going to explore is issued from 54 sensors deployed at the Intel Berkeley Research lab.…”

Section: Introductionmentioning

confidence: 99%

Efficient anomaly detection on sampled data streams with contaminated phase I data

Sibai

Internet Technology Letters

Jaoude

et al. 2020

Self Cite

Control chart algorithms aim to monitor a process over time. This process consists of two phases. Phase I, also called the learning phase, estimates the normal process parameters, then in Phase II, anomalies are detected. However, the learning phase itself can contain contaminated data such as outliers. If left undetected, they can jeopardize the accuracy of the whole chart by affecting the computed parameters, which leads to faulty classifications and defective data analysis results. This problem becomes more severe when the analysis is done on a sample of the data rather than the whole data. To avoid such a situation, Phase I quality must be guaranteed. The purpose of this paper is to introduce a new approach for applying EWMA chart to obtain accurate anomaly detection results over sampled data even if contaminations exist in Phase I. The new chart is applied to a real dataset, and its performance is evaluated on both sampled and not sampled data according to several criteria.