Denial of Service Attacks and Detection Methods in Wireless Mesh Networks

2010 ◽  
Author(s):  
Sahil Seth ◽  
Anil Gankotiya
Keyword(s):  
Wireless Mesh Networks ◽  
Mesh Networks ◽  
Denial Of Service ◽  
Detection Methods ◽  
Denial Of Service Attacks ◽  
Wireless Mesh

scholarly journals Preventing & Isolating Distributed Denial of Service (DDOS) attack in Wireless Mesh Networks (WMN's)

2019 ◽  
Vol 8 (9S) ◽  
pp. 447-454
Keyword(s):  
Wireless Mesh Networks ◽  
Shortest Path ◽  
Mesh Networks ◽  
Denial Of Service ◽  
The Internet ◽  
Covert Channel ◽  
Distributed Denial Of Service ◽  
Application Layer ◽  
Ddos Attack ◽  
Wireless Mesh

Wireless Mesh networks (WMN’s) are prone to a number of attacks & these attacks compromise the security of these networks. Attaining security in these networks is a challenging task. It is logical to consider that there are many types of scripts in the internet. The virus can either be a key logger or somebody else's mischief. With this script we can steal any information. Since the existence of virus cannot be ignored, therefore the authors have tried to present their work on first detecting it and later on fixing it. With the help of different protocols present in the Application Layer, a hacker takes information out of the script. The authors have used Covert Channel, which has been mentioned in many essays. Now with the help of this channel, the information will go to all and it will not go to any of the informatics. This research proposal envisions a methodology to first detect the selfish node in the network & later on provides a technique for mitigation of the same.NS2 simulator has been used to simulate & analyze the performance of our proposed methodology for Open Shortest Path First (OSPF) protocol in WMN’s.

scholarly journals SDToW: A Slowloris Detecting Tool for WMNs

Information ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 544
Author(s):  
Vinicius da Silva Faria ◽  
Jéssica Alcântara Gonçalves ◽  
Camilla Alves Mariano da Silva ◽  
Gabriele de Brito Vieira ◽  
Dalbert Matos Mascarenhas
Keyword(s):  
Wireless Mesh Networks ◽  
Lower Incidence ◽  
False Positive ◽  
Mesh Networks ◽  
Denial Of Service ◽  
Memory Consumption ◽  
Dos Attacks ◽  
Wireless Mesh ◽  
Comparison Results ◽  
Parallel Connections

Denial of service (DoS) attacks play a significant role in contemporary cyberspace scenarios. A variety of different DoS attacks pollute networks by exploring various vulnerabilities. A group of DoS called application DoS attacks explore application vulnerabilities. This work presents a tool that detects and blocks an application DoS called Slowloris on wireless mesh networks (WMNs). Our tool, called SDToW, is designed to effectively use the structure of the WMNs to block the Slowloris attack. SDToW uses three different modules to detect and block the attack. Each module has its specific tasks and thus optimizes the overall detection and block efficiency. Our solution blocks the attacker on its first WMN hop, reducing the malicious traffic on the network and avoiding further attacks from the blocked user. The comparison results show that SDToW performs with 66.7% less processing consumption and 89.1% less memory consumption than Snort. Our solution does not limit the number of parallel connections per user. Hence, by avoiding this limitation, SDToW has a lower incidence of false positive errors than Snort.

DETECTION AND IDENTIFICATION OF ANOMALIES IN WIRELESS MESH NETWORKS USING PRINCIPAL COMPONENT ANALYSIS (PCA)

2009 ◽  
Vol 10 (04) ◽  
pp. 517-534 ◽  
Author(s):  
ZAINAB R. ZAIDI ◽  
SARA HAKAMI ◽  
TIM MOORS ◽  
BJORN LANDFELDT
Keyword(s):  
Wireless Networks ◽  
Anomaly Detection ◽  
Wireless Mesh Networks ◽  
Mesh Networks ◽  
Denial Of Service ◽  
Principal Component ◽  
False Alarms ◽  
Identification Scheme ◽  
Number Of False Alarms ◽  
Wireless Mesh

Anomaly detection is becoming a powerful and necessary component as wireless networks gain popularity. In this paper, we evaluate the efficacy of PCA based anomaly detection for wireless mesh networks (WMN). PCA based method [1] was originally developed for wired networks. Our experiments show that it is possible to detect different types of anomalies, such as Denial-of-service (DoS) attack, port scan attack [1], etc., in an interference prone wireless environment. However, the PCA based method is found to be very sensitive to small changes in flows causing non-negligible number of false alarms. This problem prompted us to develop an anomaly identification scheme which automatically identifies the flow(s) causing the detected anomaly and their contributions in terms of number of packets. Our results show that the identification scheme is able to differentiate false alarms from real anomalies and pinpoint the culprit(s) in case of a real fault or threat. Moreover, we also found that the threshold value used in [1] for distinguishing normal and abnormal traffic conditions is based on assumption of normally distributed traffic which is not accurate for current network traffic which is mostly self-similar in nature. Adjusting the threshold also reduced the number of false alarms considerably. The experiments were performed over an 8 node mesh testbed deployed in a suburban area, under different realistic traffic scenarios. Our identification scheme facilitates the use of PCA based method for real-time anomaly detection in wireless networks as it can filter the false alarms locally at the monitoring nodes without excessive computational overhead.

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