Titre :	Intrusion detection system for wireless sensor network
Type de document :	texte manuscrit
Auteurs :	Mohamed Riadh Ben Abdelkarim, Auteur ; Hadda Cherroun, Directeur de thèse ; Khaled Bekkar, Directeur de thèse
Editeur :	Laghouat : Université Amar Telidji - Département d'informatique
Année de publication :	2017
Importance :	60 p.
Format :	30 cm.
Accompagnement :	1 disque optique numérique (CD-ROM)
Note générale :	Option : Réseaux,systèmes et applications réparties
Langues :	Anglais
Mots-clés :	Wireless Sensor Network (WSN)  Intrusion  Detection System (IDS)  Simulations  Machine learning  Features  Denial of Service  Blackhole  Hello-Flood  Random Forest
Résumé :	Wireless sensor networks (WSNs) have numerous application in almost every domain. WSNs are composed of cheap tiny devices that are deployed in open and unsafe environments making them exposed to all sorts of attacks. The security of such networks is of great importance. Hence, securely operating WSNs, any intrusions should be recognized before attackers can harm the network. Among the IDS solution, Machine learning-based IDSs have proved their e?ciency. However, Machine learning-based IDS for WSN needs a set of features to characterize attacks while respecting WSN speci?cities.In this work, we investigate how to sub-optimally characterize attacks in WSN. Based on 6,568 deployed simulations, we have got a large dataset of 94,426 records that captures di?erent behaviors at the connection level. The targeted behaviors are Normal,Blackhole, Hello-Flood, and DoS. Based on classi?cation performances, our results prove that at the connection level, attacks can be characterized by just four attributes. Using Random Forest classi?er, we reached 91:8% of precision in the low-loaded scenario and 78:1% in the high-loaded scenarios.
note de thèses :	Mémoire de master en informatique

Intrusion detection system for wireless sensor network [texte manuscrit] / Mohamed Riadh Ben Abdelkarim, Auteur ; Hadda Cherroun, Directeur de thèse ; Khaled Bekkar, Directeur de thèse . - Laghouat : Université Amar Telidji - Département d'informatique, 2017 . - 60 p. ; 30 cm. + 1 disque optique numérique (CD-ROM).
Option : Réseaux,systèmes et applications réparties
Langues : Anglais

Mots-clés :	Wireless Sensor Network (WSN)  Intrusion  Detection System (IDS)  Simulations  Machine learning  Features  Denial of Service  Blackhole  Hello-Flood  Random Forest
Résumé :	Wireless sensor networks (WSNs) have numerous application in almost every domain. WSNs are composed of cheap tiny devices that are deployed in open and unsafe environments making them exposed to all sorts of attacks. The security of such networks is of great importance. Hence, securely operating WSNs, any intrusions should be recognized before attackers can harm the network. Among the IDS solution, Machine learning-based IDSs have proved their e?ciency. However, Machine learning-based IDS for WSN needs a set of features to characterize attacks while respecting WSN speci?cities.In this work, we investigate how to sub-optimally characterize attacks in WSN. Based on 6,568 deployed simulations, we have got a large dataset of 94,426 records that captures di?erent behaviors at the connection level. The targeted behaviors are Normal,Blackhole, Hello-Flood, and DoS. Based on classi?cation performances, our results prove that at the connection level, attacks can be characterized by just four attributes. Using Random Forest classi?er, we reached 91:8% of precision in the low-loaded scenario and 78:1% in the high-loaded scenarios.
note de thèses :	Mémoire de master en informatique