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.

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.

Adaptive Ensemble Multi-Agent Based Intrusion Detection Model

2010 ◽  
pp. 36-48 ◽  
Author(s):  
Tarek Helmy
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Agent Based ◽  
Detection Model ◽  
Detection Analysis ◽  
Proposed Model ◽  
Multi Agent

The system that monitors the events occurring in a computer system or a network and analyzes the events for sign of intrusions is known as intrusion detection system. The performance of the intrusion detection system can be improved by combing anomaly and misuse analysis. This chapter proposes an ensemble multi-agent-based intrusion detection model. The proposed model combines anomaly, misuse, and host-based detection analysis. The agents in the proposed model use rules to check for intrusions, and adopt machine learning algorithms to recognize unknown actions, to update or create new rules automatically. Each agent in the proposed model encapsulates a specific classification technique, and gives its belief about any packet event in the network. These agents collaborate to determine the decision about any event, have the ability to generalize, and to detect novel attacks. Empirical results indicate that the proposed model is efficient, and outperforms other intrusion detection models.

Algebraic Properties of Rough Set on Two Universal Sets based on Multigranulation

2014 ◽  
Vol 1 (2) ◽  
pp. 49-61 ◽  
Author(s):  
Mary A. Geetha ◽  
D. P. Acharjya ◽  
N. Ch. S. N. Iyengar
Keyword(s):  
Information System ◽  
Rough Set ◽  
Equivalence Relation ◽  
Rough Sets ◽  
Granular Computing ◽  
Real Life ◽  
Equivalence Relations ◽  
Life Problems ◽  
Algebraic Properties ◽  
The Universe

The rough set philosophy is based on the concept that there is some information associated with each object of the universe. The set of all objects of the universe under consideration for particular discussion is considered as a universal set. So, there is a need to classify objects of the universe based on the indiscernibility relation (equivalence relation) among them. In the view of granular computing, rough set model is researched by single granulation. The granulation in general is carried out based on the equivalence relation defined over a universal set. It has been extended to multi-granular rough set model in which the set approximations are defined by using multiple equivalence relations on the universe simultaneously. But, in many real life scenarios, an information system establishes the relation with different universes. This gave the extension of multi-granulation rough set on single universal set to multi-granulation rough set on two universal sets. In this paper, we define multi-granulation rough set for two universal sets U and V. We study the algebraic properties that are interesting in the theory of multi-granular rough sets. This helps in describing and solving real life problems more accurately.

MODELLING OF AN INTRUSION DETECTION SYSTEM USING C4.5 MACHINE LEARNING ALGORITHM

2021 ◽  
Vol 4 (4) ◽  
pp. 454-459
Author(s):  
Oyenike Mary Olanrewaju ◽  
Faith Oluwatosin Echobu ◽  
Abubakar Mogaji
Keyword(s):  
Intrusion Detection ◽  
Security Policy ◽  
Learning Algorithm ◽  
Detection System ◽  
Knowledge Discovery In Databases ◽  
Protocol Violation ◽  
Detection Model ◽  
Software Applications ◽  
Detection Systems ◽  
Proposed Model

The increasing growth of wireless networking and new mobile computing devices has caused boundaries between trusted and malicious users to be blurred. The shift in security priorities from the network perimeter to information protection and user resources security is an open area for research which is concerned with the protection of user information’s confidentiality, integrity and availability. Intrusion detection systems are programs or software applications embedded in sophisticated devices to monitor the activities on networks or systems for security, policy or protocol violation or malicious activities detection. In this work, an intrusion detection model was proposed using C4.5 algorithm which was implemented with WEKA tool and RAPID MINER. The model showed good performance when trained and tested with validation techniques. Implementation of the proposed model was conducted on the Network Security Laboratory Knowledge Discovery in Databases (NSL-KDD) dataset, an improved version of KDD 99 dataset, which showed that the proposed model approach has an average detection rate of 99.62% and reduced false alarm rate of 0.38%.

A Network Intrusion Detection Method Inspired by Biological Immunology

2010 ◽  
Vol 121-122 ◽  
pp. 482-485
Author(s):  
Rong Deng ◽  
Xiu Yin Zhang
Keyword(s):  
Intrusion Detection ◽  
Detection Method ◽  
Network Intrusion Detection ◽  
Antibody Concentration ◽  
Traffic Patterns ◽  
Immune Systems ◽  
Detection Model ◽  
Network Intrusion ◽  
Proposed Model ◽  
Dynamic Relationships

In this article an Immune Based Intrusion Detection Model (IBIDM) was built to simulate the dynamic relationships between the intrusion antigen intensity and the antibody concentration in the biological immune systems. In IBIDM, traditional detection rules and network traffic patterns are mapped to antibodies and antigens respectively. The network security situation is presented in the form of detector numbers to help reduce false alarm rate. Computer simulations show that the proposed model is effective for intrusion detection.

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