Isolation Forest Based Interest Flooding Attack Detection Mechanism in NDN

Author(s):  
Jing Chen ◽  
Guanglin Xing ◽  
Mengtian Cui ◽  
Hong Huo ◽  
Rui Hou
2021 ◽  
Author(s):  
Sicheng Gong

This paper proposes a novel event-triggered attack detection mechanism for converter-based DC microgrid system. Under a distributive network framework, each node collects its neighbours' relative data to regulate its own output for local stabilization. Without power line current data, hardly can an agent directly identify the source of unexpected power flow, especially under an organized attack composed of voltage variations and corresponding deceptive messages. In order to recognize traitors who broadcast wrong data, target at system distortion and even splitting, an efficient attack detection and identification strategy is mandatory. After the attack detector is triggered, each relative agent refuses to trust any received data directly before authentication. Through proposed two-step verification by comparing theoretical estimated signals with received ones, both self sensors and neighbour nodes would be inspected, and the attacker was difficult to hide himself. Through simulation on SIMULINK/PLECS and hardware experiments on dSpace Platform, the effectiveness of proposed detection algorithm has been proved.


2009 ◽  
Vol 16C (1) ◽  
pp. 13-20 ◽  
Author(s):  
Jun-Sang Park ◽  
Sung-Yun Kim ◽  
Dai-Hee Park ◽  
Mi-Jung Choi ◽  
Myung-Sup Kim

Author(s):  
Dhanapal A ◽  
Nithyanandam P

Cloud computing is the cutting edge and has become inevitable in all forms of computing. This is due to its nature of elasticity, cost-effectiveness, availability, etc. The online applications like e-commerce, and e-healthcare applications are moving to the cloud to reduce their operational cost. These applications have the vulnerability of a HTTP flooding Distributed Denial of Service attack in the cloud. This flooding attack aims to overload the application, making it unable to process genuine requests and bring it down. So, these applications need to be secured and safeguarded against such attacks. This HTTP flooding attack is one of the key challenging issues as it shows normal behaviour with regard to all lower networking layers like TCP 3-way handshaking by mimicking genuine requests and it is even harder in the cloud due to the cloud properties. This article offers a solution for detecting a HTTP flooding attack in the cloud by using the novel TriZonal Linear Prediction (TLP) model. The solution was implemented using OpenStack and the FIFA Worldcup '98 data set for experimentation.


Information ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 198
Author(s):  
Junhyeok Yun ◽  
Mihui Kim

Mobile crowdsensing is a data collection system using widespread mobile devices with various sensors. The data processor cannot manage all mobile devices participating in mobile crowdsensing. A malicious user can conduct a Sybil attack (e.g., achieve a significant influence through extortion or the generation of fake IDs) to receive an incentive or destroy a system. A mobile crowdsensing system should, thus, be able to detect and block a Sybil attack. Existing Sybil attack detection mechanisms for wireless sensor networks cannot apply directly to mobile crowdsensing owing to the privacy issues of the participants and detection overhead. In this paper, we propose an effective privacy-preserving Sybil attack detection mechanism that distributes observer role to the users. To demonstrate the performance of our mechanism, we implement a Wi-Fi-connection-based Sybil attack detection model and show its feasibility by evaluating the detection performance.


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