| Titre : | Detecting and Predicting Massive Cyber Attacks Using Adaptive EWMA Based Models |
| Auteurs : | BENAISSA Mohamed El Mehdi Seif Eddine, Auteur ; HAMRI Abd Elkader Iheb, Auteur ; BOUYEDDOU Benamar, Directeur de thèse |
| Type de document : | texte imprimé |
| Editeur : | Université Saïda– Dr. Tahar Moulay – Faculté de Technologie DBR.Télécommunications, 2023/2024 |
| Format : | 80 p |
| Accompagnement : | CD |
| Langues: | Anglais |
| Catégories : | |
| Mots-clés: | TCP/IP ; Cyber-Attacks ; DoS/DDoS attacks ; TCP SYN Attacks ; Smurf Attacks ; Control charts ; EWMA chart ; adaptive EWMA chart ; DARPA99 dataset. |
| Résumé : |
In our digitally interconnected world, the prevalence of cyber-attacks poses significant
threats to network security and integrity. Among these, Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks stand out for their potential to cause substantial financial harm and reputational damage. Control charts, statistical tools designed to monitor process variations, offer a promising way for detecting and mitigating such attacks. This dissertation investigates control chart’s efficiency in identifying DoS and DDoS attacks through network traffic analysis. Here, we examine the performance of EWMA, VSS- EWMA, VSSILF-EWMA, and ASW-EWMA under various attack’s scenarios when evaluating their capabilities using the DARPA99 dataset. Our findings indicate that these control charts can effectively detect DoS and DDoS attacks, provide enhanced sensitivity and reduce false alarms rates. |
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Documents numériques (1)
Detecting and Predicting Massive Cyber Attacks Using Adaptive EWMA Based Models Adobe Acrobat PDF |
