University Sétif 1 FERHAT ABBAS Faculty of Sciences
Catégories
Ajouter le résultat dans votre panier Affiner la recherche
Titre : http flood dos attack detection using data mining Type de document : texte imprimé Auteurs : yasmina Abidi, Auteur ; yaakoub Derradji, Auteur ; Haddadi ,Mohamed, Directeur de thèse Année de publication : 2022 Importance : 1 vol (94 f .) Format : 29cm Langues : Français (fre) Catégories : Thèses & Mémoires:Informatique Mots-clés : Informatique Index. décimale : 004 Informatique Résumé :
Among the problems that the Internet is facing now is DOS ATTACKS. It aims
to deny services to legitimate users, and shut down a machine or network, making
it inaccessible to its intended users. It will be through flooding the target with
requests or sending information that triggers a crash. There are many types of DoS
attacks, including Slowloris Attack, RUDY Attack, Slow HTTP Post Attack, and
Slow Read Attack. To detect this attack we use Data Mining, and the two tools
WEKKA and TANAGRA. Data mining is the process to extract information from
a dataset to help to make a decision. In our case, the decision is to classify requests
as normal or abnormal (attack). To reach this goal; we used a classification
algorithm, we chose among them Multilayer Perceptron, Naive Bayes, Random
Tree, K-Nearest Neighbors, Decision Tree, and Support Vector Machine. We
have implemented these algorithms on http_csic_2010_full.arff dataset. We are
looking for a program that gives a high rate and the perfect classification of attacks.Côte titre : MAI/0583 En ligne : https://drive.google.com/file/d/1s-MQJt1dRtsvA15TT5YIKcO_g0Ki1TFq/view?usp=share [...] Format de la ressource électronique : http flood dos attack detection using data mining [texte imprimé] / yasmina Abidi, Auteur ; yaakoub Derradji, Auteur ; Haddadi ,Mohamed, Directeur de thèse . - 2022 . - 1 vol (94 f .) ; 29cm.
Langues : Français (fre)
Catégories : Thèses & Mémoires:Informatique Mots-clés : Informatique Index. décimale : 004 Informatique Résumé :
Among the problems that the Internet is facing now is DOS ATTACKS. It aims
to deny services to legitimate users, and shut down a machine or network, making
it inaccessible to its intended users. It will be through flooding the target with
requests or sending information that triggers a crash. There are many types of DoS
attacks, including Slowloris Attack, RUDY Attack, Slow HTTP Post Attack, and
Slow Read Attack. To detect this attack we use Data Mining, and the two tools
WEKKA and TANAGRA. Data mining is the process to extract information from
a dataset to help to make a decision. In our case, the decision is to classify requests
as normal or abnormal (attack). To reach this goal; we used a classification
algorithm, we chose among them Multilayer Perceptron, Naive Bayes, Random
Tree, K-Nearest Neighbors, Decision Tree, and Support Vector Machine. We
have implemented these algorithms on http_csic_2010_full.arff dataset. We are
looking for a program that gives a high rate and the perfect classification of attacks.Côte titre : MAI/0583 En ligne : https://drive.google.com/file/d/1s-MQJt1dRtsvA15TT5YIKcO_g0Ki1TFq/view?usp=share [...] Format de la ressource électronique : Exemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité MAI/0583 MAI/0583 Mémoire Bibliothéque des sciences Anglais Disponible
Disponible
Titre : HTTP-GET DoS Attack Detection Using Ontology Type de document : texte imprimé Auteurs : Bouaoud Hadil, Auteur ; Hadjer Djehiche, Auteur ; Mohamed Haddadi, Directeur de thèse Année de publication : 2022 Importance : 1 vol (54 f .) Format : 29cm Langues : Français (fre) Catégories : Thèses & Mémoires:Informatique Mots-clés : Informatique Index. décimale : 004 Informatique Côte titre : MAI/0569 En ligne : https://drive.google.com/file/d/1ETR8XYrnCYho691VmGBtFppqjGNlFveQ/view?usp=share [...] Format de la ressource électronique : HTTP-GET DoS Attack Detection Using Ontology [texte imprimé] / Bouaoud Hadil, Auteur ; Hadjer Djehiche, Auteur ; Mohamed Haddadi, Directeur de thèse . - 2022 . - 1 vol (54 f .) ; 29cm.
Langues : Français (fre)
Catégories : Thèses & Mémoires:Informatique Mots-clés : Informatique Index. décimale : 004 Informatique Côte titre : MAI/0569 En ligne : https://drive.google.com/file/d/1ETR8XYrnCYho691VmGBtFppqjGNlFveQ/view?usp=share [...] Format de la ressource électronique : Exemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité MAI/0569 MAI/0569 Mémoire Bibliothéque des sciences Anglais Disponible
Disponible
Titre : A Hybrid recommendation algorithm for learning resources Type de document : texte imprimé Auteurs : elmahdi Benzaoui, Auteur ; Adel Guessas, Auteur ; Chahrazed Mediani, Directeur de thèse Année de publication : 2022 Importance : 1 vol (58 f .) Format : 29cm Langues : Français (fre) Catégories : Thèses & Mémoires:Informatique Mots-clés : Informatique Index. décimale : 004 Informatique Côte titre : MAI/0599 En ligne : https://drive.google.com/file/d/1UbOGvhbbO5wGCF3Yfnxh5-AGz1QiwVWo/view?usp=share [...] Format de la ressource électronique : A Hybrid recommendation algorithm for learning resources [texte imprimé] / elmahdi Benzaoui, Auteur ; Adel Guessas, Auteur ; Chahrazed Mediani, Directeur de thèse . - 2022 . - 1 vol (58 f .) ; 29cm.
Langues : Français (fre)
Catégories : Thèses & Mémoires:Informatique Mots-clés : Informatique Index. décimale : 004 Informatique Côte titre : MAI/0599 En ligne : https://drive.google.com/file/d/1UbOGvhbbO5wGCF3Yfnxh5-AGz1QiwVWo/view?usp=share [...] Format de la ressource électronique : Exemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité MAI/0599 MAI/0599 Mémoire Bibliothéque des sciences Anglais Disponible
DisponibleHybridization of Deep and Generative Models for Accurate Generation and Classification Chest X-ray Abnormalities / Mohamed Chakib Zaghlaoui
Titre : Hybridization of Deep and Generative Models for Accurate Generation and Classification Chest X-ray Abnormalities Type de document : texte imprimé Auteurs : Mohamed Chakib Zaghlaoui, Auteur ; Tarek Laouamri ; Abdelouahab Moussaoui, Directeur de thèse Editeur : Sétif:UFS Année de publication : 2023 Importance : 1 vol (66 f.) Format : 29 cm Langues : Anglais (eng) Catégories : Thèses & Mémoires:Informatique Mots-clés : Deep learning, generative models
Chest X-ray abnormalities
Classification
Augmentation
Convolutional neural networks
Generative
Adversarial networksIndex. décimale : 004 - Informatique Résumé : Medical imaging plays a crucial role in the diagnosis and treatment of various medical conditions. In recent years, deep learning
models have shown remarkable success in automating the analysis
of medical images, particularly in the field of chest X-ray examination. However, the accurate generation and classification of chest
X-ray abnormalities still pose significant challenges.
This master thesis aims to address these challenges by proposing
a novel hybrid approach that combines deep learning and generative
models. The primary objective is to leverage the strengths of both
model types to achieve more accurate and reliable results in the
generation and classification of chest X-ray abnormalities.
The proposed hybrid model architecture consists of a deep convolutional neural network (CNN) as the primary classifier, and a
generative adversarial network (GAN) for generating realistic and
diverse chest X-ray abnormality samples. By training the hybrid
model on a large dataset of labeled chest X-ray images, it learns to
generate synthetic abnormality samples that closely resemble real
abnormalities.
The generated abnormality samples are then used to augment the
original dataset, creating an augmented dataset with increased diversity and representation of abnormalities. This augmented dataset
is then utilized to retrain and fine-tune the deep CNN classifier, enhancing its ability to accurately classify chest X-ray abnormalities.
Experimental evaluations conducted on a comprehensive dataset
of chest X-ray images demonstrate the effectiveness of the proposed
hybrid model. The results indicate significant improvements in both
the generation of realistic abnormality samples and the accuracy
iv
of abnormality classification compared to traditional deep learning
approaches.
In conclusion, the hybridization of deep and generative models
presented in this thesis provides a promising avenue for enhancing the generation and classification of chest X-ray abnormalities.
The ability to generate diverse and realistic abnormality samples,
coupled with improved classification accuracy, can greatly benefit medical professionals in diagnosing and treating patients with
chest-related medical conditions.
Côte titre : MAI/0820
En ligne : https://drive.google.com/file/d/1aSvvc1K6fCXHxQLUAEct48J0zfamK7xO/view?usp=drive [...] Format de la ressource électronique : Hybridization of Deep and Generative Models for Accurate Generation and Classification Chest X-ray Abnormalities [texte imprimé] / Mohamed Chakib Zaghlaoui, Auteur ; Tarek Laouamri ; Abdelouahab Moussaoui, Directeur de thèse . - [S.l.] : Sétif:UFS, 2023 . - 1 vol (66 f.) ; 29 cm.
Langues : Anglais (eng)
Catégories : Thèses & Mémoires:Informatique Mots-clés : Deep learning, generative models
Chest X-ray abnormalities
Classification
Augmentation
Convolutional neural networks
Generative
Adversarial networksIndex. décimale : 004 - Informatique Résumé : Medical imaging plays a crucial role in the diagnosis and treatment of various medical conditions. In recent years, deep learning
models have shown remarkable success in automating the analysis
of medical images, particularly in the field of chest X-ray examination. However, the accurate generation and classification of chest
X-ray abnormalities still pose significant challenges.
This master thesis aims to address these challenges by proposing
a novel hybrid approach that combines deep learning and generative
models. The primary objective is to leverage the strengths of both
model types to achieve more accurate and reliable results in the
generation and classification of chest X-ray abnormalities.
The proposed hybrid model architecture consists of a deep convolutional neural network (CNN) as the primary classifier, and a
generative adversarial network (GAN) for generating realistic and
diverse chest X-ray abnormality samples. By training the hybrid
model on a large dataset of labeled chest X-ray images, it learns to
generate synthetic abnormality samples that closely resemble real
abnormalities.
The generated abnormality samples are then used to augment the
original dataset, creating an augmented dataset with increased diversity and representation of abnormalities. This augmented dataset
is then utilized to retrain and fine-tune the deep CNN classifier, enhancing its ability to accurately classify chest X-ray abnormalities.
Experimental evaluations conducted on a comprehensive dataset
of chest X-ray images demonstrate the effectiveness of the proposed
hybrid model. The results indicate significant improvements in both
the generation of realistic abnormality samples and the accuracy
iv
of abnormality classification compared to traditional deep learning
approaches.
In conclusion, the hybridization of deep and generative models
presented in this thesis provides a promising avenue for enhancing the generation and classification of chest X-ray abnormalities.
The ability to generate diverse and realistic abnormality samples,
coupled with improved classification accuracy, can greatly benefit medical professionals in diagnosing and treating patients with
chest-related medical conditions.
Côte titre : MAI/0820
En ligne : https://drive.google.com/file/d/1aSvvc1K6fCXHxQLUAEct48J0zfamK7xO/view?usp=drive [...] Format de la ressource électronique : Exemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité MAI/0820 MAI/0820 Mémoire Bibliothéque des sciences Anglais Disponible
Disponible
Titre : Identification of falsification of multimedia data Type de document : texte imprimé Auteurs : Feriel Khedidja Haddad, Auteur ; El Houes Benali ; Bilal Benmessahel, Directeur de thèse Editeur : Sétif:UFS Année de publication : 2023 Importance : 1 vol (59 f.) Format : 29 cm Langues : Anglais (eng) Catégories : Thèses & Mémoires:Informatique Mots-clés : Image forgery detection
Block Based Approach
PatchMatch algorithmIndex. décimale : 004 - Informatique Résumé : Image forgery detection is a complex and challenging problem, with many different approaches
to solving it. However, all of these approaches share the same goal: to detect and localize any
forgeries that may be present in an image.
Copy-move forgery detection (CMFD) is one of the most widely used approaches to image
forgery detection,it is a common type of image manipulation where a portion of an image is copied
and pasted into another location in the same image. This type of forgery can be used to hide
or add objects to an image, or to change the overall content of the image. In this thesis, We
first inves-tigate the problems and the challenges of the existing algorithms to detect copy-move
forgery in digital images , and as we know that Digital image forgery is a growing problem because
the technology to forge images is becoming more accessible. Forgers can now easily copy and
paste sections of images, or even create entirely fake images. This makes it difficult to determine
whether an image is authentic or forged.We propose strategies and applications such Block Based
Approach or Sensor Pattern noise or the PatchMatch algorithm .We further focus on the convolutional neural network (CNN) , we talked about CNN architecture and its model. In the other
part we have used the COMOFOD and CASIA datasets for our research.That means that we have
used different datasets to train and test our model for copy-move forgery detection.We believe that
using different datasets is important for ensuring the accuracy and robustness of our model.Côte titre : MAI/0813
En ligne : https://drive.google.com/file/d/1Ao-vU8gSbOjfaerTW12jOTaL7a7O-YjF/view?usp=drive [...] Format de la ressource électronique : Identification of falsification of multimedia data [texte imprimé] / Feriel Khedidja Haddad, Auteur ; El Houes Benali ; Bilal Benmessahel, Directeur de thèse . - [S.l.] : Sétif:UFS, 2023 . - 1 vol (59 f.) ; 29 cm.
Langues : Anglais (eng)
Catégories : Thèses & Mémoires:Informatique Mots-clés : Image forgery detection
Block Based Approach
PatchMatch algorithmIndex. décimale : 004 - Informatique Résumé : Image forgery detection is a complex and challenging problem, with many different approaches
to solving it. However, all of these approaches share the same goal: to detect and localize any
forgeries that may be present in an image.
Copy-move forgery detection (CMFD) is one of the most widely used approaches to image
forgery detection,it is a common type of image manipulation where a portion of an image is copied
and pasted into another location in the same image. This type of forgery can be used to hide
or add objects to an image, or to change the overall content of the image. In this thesis, We
first inves-tigate the problems and the challenges of the existing algorithms to detect copy-move
forgery in digital images , and as we know that Digital image forgery is a growing problem because
the technology to forge images is becoming more accessible. Forgers can now easily copy and
paste sections of images, or even create entirely fake images. This makes it difficult to determine
whether an image is authentic or forged.We propose strategies and applications such Block Based
Approach or Sensor Pattern noise or the PatchMatch algorithm .We further focus on the convolutional neural network (CNN) , we talked about CNN architecture and its model. In the other
part we have used the COMOFOD and CASIA datasets for our research.That means that we have
used different datasets to train and test our model for copy-move forgery detection.We believe that
using different datasets is important for ensuring the accuracy and robustness of our model.Côte titre : MAI/0813
En ligne : https://drive.google.com/file/d/1Ao-vU8gSbOjfaerTW12jOTaL7a7O-YjF/view?usp=drive [...] Format de la ressource électronique : Exemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité MAI/0813 MAI/0813 Mémoire Bibliothéque des sciences Anglais Disponible
DisponiblePermalinkPermalinkPermalinkPermalinkImplémentation d'un algorithme auto-stabilisant pour le calcul d'un arbre couvrant / Bensedira,meriem
PermalinkPermalinkImplémentation d'un algorithme auto-stabilisant pour le calcul d'un ensemble dominant (algorithme de Neggazi) / Ratiba Boubaaya
PermalinkImplémentation d’un algorithme auto-stabilisant pour le calcul d’un ensemble indépendant(Algorithme Srimani) / Rebiha Rahma Bouima
PermalinkL’implémentation d’un algorithme auto-stabilisant pour le calcul d’un ensemble indépendant (algorithme de Turau) / Feriel Bourioune
PermalinkImplémentation d'un algorithme auto-stabilisant pour le calcul d'un ensemble indépendant en utilisant la communication par messages / Bessou, mouhamed
Permalink