Denial of Service attack in AODV & friend features extraction to design detection engine for intrusion detection system in Mobile Adhoc Network

2011 ◽  
Author(s):  
Husain Shahnawaz ◽  
S. C. Gupta ◽  
Chand Mukesh
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Denial Of Service ◽  
Features Extraction ◽  
Denial Of Service Attack ◽  
Mobile Adhoc Network ◽  
Adhoc Network ◽  
Service Attack

Multilevel Intrusion Detection System to Mitigate Denial-of-Service Attack in Wireless Sensor Networks

2020 ◽  
Vol 17 (6) ◽  
pp. 2451-2458
Author(s):  
Shilpy Ghai ◽  
Vijay Kumar
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Denial Of Service ◽  
Data Encryption ◽  
Denial Of Service Attack ◽  
Malicious Behavior ◽  
Result Show ◽  
Service Attack ◽  
Open Issue

Malicious activities over WSN is quite hard to detect as sensors operate in an open network environment. Researchers have offered several solutions but still intrusion detection/prevention is an open issue. In this paper, a scheme is introduced that can analyze the malicious behavior of the nodes over multiple layers. It uses AES algorithm for data encryption and its integrity is insured using SHA512 method. Simulation results show that it outperforms as compared to traditional WatchDog method under QoS constraints. Simulation result show that it outperforms as compared to traditional watchdog scheme.

Machine Learning Based Intrusion Detection System for Denial of Service Attack

2021 ◽  
pp. 29-47
Author(s):  
Ashish Pandey ◽  
Neelendra Badal
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Denial Of Service ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Dos Attack ◽  
Denial Of Service Attack ◽  
Service Attack

Machine learning-based intrusion detection system (IDS) is a research field of network security which depends on the effective and accurate training of models. The models of IDS must be trained with new attacks periodically; therefore, it can detect any security violations in the network. One of most frequent security violations that occurs in the network is denial of service (DoS) attack. Therefore, training of IDS models with latest DoS attack instances is required. The training of IDS models can be more effective when it is performed with the help of machine learning algorithms because the processing capabilities of machine learning algorithms are very fast. Therefore, the work presented in this chapter focuses on building a model of machine learning-based intrusion detection system for denial of service attack. Building a model of IDS requires sample dataset and tools. The sample dataset which is used in this research is NSL-KDD, while WEKA is used as a tool to perform all the experiments.

scholarly journals Anomaly Detection in Distributed Denial of Service Attack using Map Reduce Improvised Counter Based Algorithm in Hadoop

2019 ◽  
Vol 8 (4) ◽  
pp. 4668-4671
Keyword(s):  
Intrusion Detection ◽  
Anomaly Detection ◽  
Network Traffic ◽  
Intrusion Detection System ◽  
Detection System ◽  
Denial Of Service ◽  
Map Reduce ◽  
Distributed Denial Of Service ◽  
Unusual Pattern ◽  
Denial Of Service Attack

A Distributed denial of Service attacks(DDoS) is one of the major threats in the cyber network and it attacks the computers flooded with the Users Data Gram packet. These types of attacks causes major problem in the network in the form of crashing the system with large volume of traffic to attack the victim and make the victim idle in which not responding the requests. To detect this DDOS attack traditional intrusion detection system is not suitable to handle huge volume of data. Hadoop is a frame work which handles huge volume of data and is used to process the data to find any malicious activity in the data. In this research paper anomaly detection technique is implemented in Map Reduce Algorithm which detects the unusual pattern of data in the network traffic. To design a proposed model, Map Reduce platform is used to hold the improvised algorithm which detects the (DDoS) attacks by filtering and sorting the network traffic and detects the unusual pattern from the network. Improvised Map reduce algorithm is implemented with Map Reduce functionalities at the stage of verifying the network IPS. This Proposed algorithm focuses on the UDP flooding attack using Anomaly based Intrusion detection system technique which detects kind of pattern and flow of packets in the node is more than the threshold and also identifies the source code causing UDP Flood Attack.

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