An overview of incremental feature extraction methods based on linear subspaces

Diaz-Chito, Katerine; Ferri, Francesc J.; Hernàndez-Sabaté, Aura

doi:10.1016/j.knosys.2018.01.020

Cited by 18 publications

(4 citation statements)

References 62 publications

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“…S. Talukder [45] mention that static analysis consists of Opcode, N-gram, syntactic library, CFG, string signature, and others; dynamic analysis is a controlled environment such as virtual machines, simulators, emulators, sandboxes, and others. K. Diaz-Chito et al [46] shows that the extraction process can also incremental. Furthermore, research work in [47] shows that the extraction process can also use deep learning.…”

Section: Feature Extractionmentioning

confidence: 99%

The trend malware source of IoT network

Susanto¹,

Arifin

Stiawan

et al. 2021

IJEECS

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<span>Malware may disrupt the internet of thing (IoT) system/network when it resides in the network, or even harm the network operation. Therefore, malware detection in the IoT system/network becomes an important issue. Research works related to the development of IoT malware detection have been carried out with various methods and algorithms to increase detection accuracy. The majority of papers on malware literature studies discuss mobile networks, and very few consider malware on IoT networks. This paper attempts to identify problems and issues in IoT malware detection presents an analysis of each step in the malware detection as well as provides alternative taxonomy of literature related to IoT malware detection. The focuses of the discussions include malware repository dataset, feature extraction methods, the detection method itself, and the output of each conducted research. Furthermore, a comparison of malware classification approaches accuracy used by researchers in detecting malware in IoT is presented.</span>

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Section: Feature Extractionmentioning

confidence: 99%

The trend malware source of IoT network

Susanto¹,

Arifin

Stiawan

et al. 2021

IJEECS

View full text Add to dashboard Cite

show abstract

“…Full extraction is employed when replicating data from a source for the first time or when some sources cannot identify changed data, necessitating a complete reload for the entire table. Incremental extraction is utilized when some data sources cannot provide notifications about updates but can identify modified records and extract them [4]. Cleaning is essential for data warehouses before data are stored; for example, erroneous or misleading information will result from duplicated, inaccurate, or missing data.…”

Section: Introductionmentioning

confidence: 99%

A Model for Enhancing Unstructured Big Data Warehouse Execution Time

Farhan,

Youssef,

Abdelhamid

2024

BDCC

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Traditional data warehouses (DWs) have played a key role in business intelligence and decision support systems. However, the rapid growth of the data generated by the current applications requires new data warehousing systems. In big data, it is important to adapt the existing warehouse systems to overcome new issues and limitations. The main drawbacks of traditional Extract–Transform–Load (ETL) are that a huge amount of data cannot be processed over ETL and that the execution time is very high when the data are unstructured. This paper focuses on a new model consisting of four layers: Extract–Clean–Load–Transform (ECLT), designed for processing unstructured big data, with specific emphasis on text. The model aims to reduce execution time through experimental procedures. ECLT is applied and tested using Spark, which is a framework employed in Python. Finally, this paper compares the execution time of ECLT with different models by applying two datasets. Experimental results showed that for a data size of 1 TB, the execution time of ECLT is 41.8 s. When the data size increases to 1 million articles, the execution time is 119.6 s. These findings demonstrate that ECLT outperforms ETL, ELT, DELT, ELTL, and ELTA in terms of execution time.

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“…To accurately extract the spatial frequency of the interference patterns of an atom cloud in such an interferometer, we have applied specific methods based on principal component analysis (PCA) [13] in processing raw atomic fluorescence images. This technique effectively filters out influences of atomic envelops, stray background light, and other detection noises.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Readout from Spatial Interference Fringes in a Point-Source Cold Atom Inertial Sensor

Jing

Tong

Xie

et al. 2023

Sensors

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When the initial size of an atom cloud in a cold atom interferometer is negligible compared to its size after free expansion, the interferometer is approximated to a point-source interferometer and is sensitive to rotational movements by introducing an additional phase shear in the interference sequence. This sensitivity on rotation enables a vertical atom-fountain interferometer to measure angular velocity in addition to gravitational acceleration, which it is conventionally used to measure. The accuracy and precision of the angular velocity measurement depends on proper extraction of frequency and phase from spatial interference patterns detected via the imaging of the atom cloud, which is usually affected by various systematic biases and noise. To improve the measurement, a pre-fitting process based on principal component analysis is applied to the recorded raw images. The contrast of interference patterns are enhanced by 7–12 dB when the processing is present, which leads to an enhancement in the precision of angular velocity measurements from 6.3 μrad/s to 3.3 μrad/s. This technique is applicable in various instruments that involve precise extraction of frequency and phase from a spatial interference pattern.

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An overview of incremental feature extraction methods based on linear subspaces

Cited by 18 publications

References 62 publications

The trend malware source of IoT network

The trend malware source of IoT network

A Model for Enhancing Unstructured Big Data Warehouse Execution Time

Enhanced Readout from Spatial Interference Fringes in a Point-Source Cold Atom Inertial Sensor

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