An Improved Intrusion Detection Method in Mobile AdHoc Network

2009 ◽  
Author(s):  
Fangchao Yin ◽  
Xin Feng ◽  
Yonglin Han ◽  
Libai He ◽  
Huan Wang
Keyword(s):  
Intrusion Detection ◽  
Detection Method ◽  
Mobile Adhoc Network ◽  
Adhoc Network

scholarly journals Probability Model for Intrusion Detection System in Mobile Adhoc Network

2018 ◽  
Vol 7 (2.20) ◽  
pp. 302
Author(s):  
Bharathisindhu P ◽  
Dr S.SelvaBrunda
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Probability Model ◽  
Generalized Solution ◽  
Malicious Nodes ◽  
Mobile Adhoc Network ◽  
Adhoc Network ◽  
Misbehaving Nodes ◽  
Speed And Accuracy

Wireless technologies increasingly exist everywhere in advanced networks; however, this new innovation accompanies its own set of challenges. The nodes in the wireless network are characteristically ‘open’ and viewable by all network scanners. Networks are protected using many firewalls and encryption software’s. Firewalls restrict access between networks to prevent intrusion and do not indicate an attack within the network. An Intrusion Detection System (IDS) is required which monitors the network, detects misbehavior or anomalies and notifies other nodes in the network to avoid or punish the misbehaving nodes. In this paper, we propose a probability based IDS model that provides generalized solution to detect set of malicious nodes to improve the detection speed and accuracy. 

A Framework for Various Attack Identification in MANET Using Multi-Granular Rough Set

2021 ◽  
pp. 119-143
Author(s):  
N. Syed Siraj Ahmed ◽  
Debi Prasanna Acharjya
Keyword(s):  
Intrusion Detection ◽  
Rough Set ◽  
Equivalence Relations ◽  
Detection Model ◽  
Mobile Adhoc Network ◽  
Adhoc Network ◽  
Proposed Model ◽  
Topology Changes ◽  
The Universe

The topology changes randomly and dynamically in a mobile adhoc network (MANET). The composite characteristics of MANETs makes it exposed to interior and exterior attacks. Avoidance support techniques like authentication and encryption are appropriate to prevent attacks in MANETs. Thus, an authoritative intrusion detection model is required to prevent from attacks. These attacks can be at either the layers present in the network or can be of a general attack. Many models have been developed for the detection of intrusion and detection. These models aim at any one of the layer present in the network. Therefore, effort has been made to consider either the layers for the detection of intrusion and detection. This article uses a multigranular rough set (MGRS) for the detection of intrusion and detection in MANET. The advantage of MGRS is that it can aim at either the layers present in the network simultaneously by using multiple equivalence relations on the universe. The proposed model is compared with many traditional models and attained higher accuracy.

A Framework for Various Attack Identification in MANET Using Multi-Granular Rough Set

2019 ◽  
Vol 13 (4) ◽  
pp. 28-52 ◽  
Author(s):  
N. Syed Siraj Ahmed ◽  
Debi Prasanna Acharjya
Keyword(s):  
Intrusion Detection ◽  
Rough Set ◽  
Equivalence Relations ◽  
Detection Model ◽  
Mobile Adhoc Network ◽  
Adhoc Network ◽  
Proposed Model ◽  
Topology Changes ◽  
The Universe

The topology changes randomly and dynamically in a mobile adhoc network (MANET). The composite characteristics of MANETs makes it exposed to interior and exterior attacks. Avoidance support techniques like authentication and encryption are appropriate to prevent attacks in MANETs. Thus, an authoritative intrusion detection model is required to prevent from attacks. These attacks can be at either the layers present in the network or can be of a general attack. Many models have been developed for the detection of intrusion and detection. These models aim at any one of the layer present in the network. Therefore, effort has been made to consider either the layers for the detection of intrusion and detection. This article uses a multigranular rough set (MGRS) for the detection of intrusion and detection in MANET. The advantage of MGRS is that it can aim at either the layers present in the network simultaneously by using multiple equivalence relations on the universe. The proposed model is compared with many traditional models and attained higher accuracy.

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