A deep learning methods for intrusion detection systems based machine learning in MANET

Laqtib, Safaa; Yassini, Khalid El; Hasnaoui, Moulay Lahcen

doi:10.1145/3368756.3369021

Cited by 21 publications

(7 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Machine learning techniques are still extensively used in many recent work, and that given the great potential of these techniques to address the problems inherent in IDS and CIDS. The most used ML techniques are: Neural Networks [28,165,166], Bayesian Networks [152,64], SVM [177,188], Decision Trees [50,177], and Deep Learning [109].…”

Section: Discussion Open Issues and Recommendationsmentioning

confidence: 99%

“…In the last five years, deep learning techniques have been successfully used to propose more efficient IDSs with very high detection accuracy [9,68,164]. However, and according to the current state of the art, we found only few proposed approaches for DL-based CIDS [125], and the work of [109] have combined DL technique and MASbased CIDS. We would like to point that designing a DL-based CIDSs require very large and diverse datasets in to train a highly accurate detection model.…”

Section: Discussion Open Issues and Recommendationsmentioning

confidence: 99%

“…An other important work of Laqtib et al [109], considered the hierarchical collaborative IDS using deep learning techniques under MANETs, in which each node has an IDS agent running. The authors presented well-known deep learning models namely: CNN, Inception-CNN, Bi-LSTM, and GRU.…”

Section: E Deep Learning (Dl)mentioning

confidence: 99%

See 2 more Smart Citations

A Survey on Multi-Agent Based Collaborative Intrusion Detection Systems

Bougueroua

Mazouzi

Belaoued

et al. 2021

Journal of Artificial Intelligence and Soft Computing Research

View full text Add to dashboard Cite

Multi-Agent Systems (MAS) have been widely used in many areas like modeling and simulation of complex phenomena, and distributed problem solving. Likewise, MAS have been used in cyber-security, to build more efficient Intrusion Detection Systems (IDS), namely Collaborative Intrusion Detection Systems (CIDS). This work presents a taxonomy for classifying the methods used to design intrusion detection systems, and how such methods were used alongside with MAS in order to build IDS that are deployed in distributed environments, resulting in the emergence of CIDS. The proposed taxonomy, consists of three parts: 1) general architecture of CIDS, 2) the used agent technology, and 3) decision techniques, in which used technologies are presented. The proposed taxonomy reviews and classifies the most relevant works in this topic and highlights open research issues in view of recent and emerging threats. Thus, this work provides a good insight regarding past, current, and future solutions for CIDS, and helps both researchers and professionals design more effective solutions.

show abstract

Section: Discussion Open Issues and Recommendationsmentioning

confidence: 99%

Section: Discussion Open Issues and Recommendationsmentioning

confidence: 99%

See 1 more Smart Citation

A Survey on Multi-Agent Based Collaborative Intrusion Detection Systems

Bougueroua

Mazouzi

Belaoued

et al. 2021

Journal of Artificial Intelligence and Soft Computing Research

View full text Add to dashboard Cite

show abstract

“…It also includes the challenges and future research direction of DBF in electroencephalographic applications. Abdellaoui and Douik [53] suggested an optimal HAR system with a twophase DBN model that offers a better quality of classification prediction [54] (Table 3).…”

Section: Application Of Dbnmentioning

confidence: 99%

A Study on Different Deep Learning Algorithms Used in Deep Neural Nets: MLP SOM and DBN

Naskath¹,

Sivakamasundari²,

Begum

2022

Wireless Pers Commun

View full text Add to dashboard Cite

Deep learning is a wildly popular topic in machine learning and is structured as a series of nonlinear layers that learns various levels of data representations. Deep learning employs numerous layers to represent data abstractions to implement various computer models. Deep learning approaches like generative, discriminative models and model transfer have transformed information processing. This article proposes a comprehensive review of various deep learning algorithms Multi layer perception, Self-organizing map and deep belief networks algorithms. It first briefly introduces historical and recent state-of-the-art reviews with suitable architectures and implementation steps. Moreover, the various applications of those algorithms in various fields such as wireless networks, Adhoc networks, Mobile ad-hoc and vehicular ad-hoc networks, speech recognition engineering, medical applications, natural language processing, material science and remote sensing applications, etc. are classified.

show abstract

“…Deep learning methods have the distinct ability to continuously improve their performance with a continuous expansion of the dataset used for training (LAQTIB et al, 2019). Since no change in the training parameters improved the model's performance, the most logical step would be to expand the dataset with new images and samples rich in inertinite, expanding into non-coking coal samples that could have more inertinite.…”

Section: Comparative Discussion Of the Modelsmentioning

confidence: 99%

Use of Deep Convolutional Neural Networks in Automatic Recognition and Classification of Coal Macerals

SANTOS¹

View full text Add to dashboard Cite

Unlike most other rocks, coal is a sedimentary rock composed primarily of organic matter derived from plant debris that accumulated in peat mires during different geological periods. Coal is also an essential economic resource in many countries, having been the main driving force behind the industrial revolution. Coal is still widely used industrially for many different purposes: carbonization and coke production, iron/steel making, thermal coal to generate electricity, liquefaction, and gasification. The utility of the coal is dictated by its properties which are commonly referred to as its rank, type, and grade. Coal composition, in terms of its macerals, and its rank determination are determined manually by a petrographer due to its complex nature. This study aimed to develop an automatic method based on machine learning capable of maceral segmentation at group level followed by a module for rank reflectance determination on petrographic images of coal that can improve the efficiency of this process and decrease operator subjectivity. Firstly, a Mask R-CNN-based architecture deep learning approach was developed to identify and segment the vitrinite maceral group, which is fundamental for rank analysis, as rank is determined by collotelinite reflectance (one of its individual macerals). Secondly, an image processing method was developed to analyze the vitrinite segmented images and determine coal rank by associating the grey values with the reflectance. For the maceral (group) segmentation, five samples were used to train the network, 174 images were used for training, and 86 were used for testing, with the best results obtained for the vitrinite, inertinite, liptinite, and collotelinite models (89.23%, 68.81%, 37.00% and 84.77% F1-score, respectively). Those samples were used alongside another eight samples to determine the rank results utilizing collotelinite reflectance. The samples ranged from 0.97% to 1.8% reflectance. This method should help save time and labor for analysis if implemented into a production model. The root mean square calculated between the proposed method and the reference reflectance values was 0.0978.

show abstract

A deep learning methods for intrusion detection systems based machine learning in MANET

Cited by 21 publications

References 18 publications

A Survey on Multi-Agent Based Collaborative Intrusion Detection Systems

A Survey on Multi-Agent Based Collaborative Intrusion Detection Systems

A Study on Different Deep Learning Algorithms Used in Deep Neural Nets: MLP SOM and DBN

Use of Deep Convolutional Neural Networks in Automatic Recognition and Classification of Coal Macerals

Contact Info

Product

Resources

About