Selfish Attack detection mechanism using COOPON in Cognitive Radio

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.

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DDoS attack detection mechanism in the application layer using user features

2018 International Conference on Information and Computer Technologies (ICICT) ◽

10.1109/infoct.2018.8356848 ◽

2018 ◽

Author(s):

Silvia Bravo ◽

David Mauricio

Keyword(s):

Attack Detection ◽

Application Layer ◽

Detection Mechanism ◽

Ddos Attack ◽

Ddos Attack Detection

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Semi-supervised machine learning for primary user emulation attack detection and prevention through core-based analytics for cognitive radio networks

International Journal of Distributed Sensor Networks ◽

10.1177/1550147719860365 ◽

2019 ◽

Vol 15 (9) ◽

pp. 155014771986036 ◽

Cited By ~ 4

Author(s):

Sundar Srinivasan ◽

KB Shivakumar ◽

Muazzam Mohammad

Keyword(s):

Machine Learning ◽

Cognitive Radio ◽

Cognitive Radio Networks ◽

Primary User ◽

Radio Networks ◽

Attack Detection ◽

Supervised Machine Learning ◽

Machine Language ◽

Detection Accuracy ◽

Primary User Emulation

Cognitive radio networks are software controlled radios with the ability to allocate and reallocate spectrum depending upon the demand. Although they promise an extremely optimal use of the spectrum, they also bring in the challenges of misuse and attacks. Selfish attacks among other attacks are the most challenging, in which a secondary user or an unauthorized user with unlicensed spectrum pretends to be a primary user by altering the signal characteristics. Proposed methods leverage advancement to efficiently detect and prevent primary user emulation future attack in cognitive radio using machine language techniques. In this paper novel method is proposed to leverage unique methodology which can efficiently handle during various dynamic changes includes varying bandwidth, signature changes etc… performing learning and classification at edge nodes followed by core nodes using deep learning convolution network. The proposed method is compared with that of two other state-of-art machine learning-based attack detection protocols and has found to significantly reduce the false alarm to secondary network, at the same time improve the overall detection accuracy at the primary network.

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SybilEye: Observer-Assisted Privacy-Preserving Sybil Attack Detection on Mobile Crowdsensing

Information ◽

10.3390/info11040198 ◽

2020 ◽

Vol 11 (4) ◽

pp. 198

Author(s):

Junhyeok Yun ◽

Mihui Kim

Keyword(s):

Mobile Devices ◽

Privacy Preserving ◽

Attack Detection ◽

Sybil Attack ◽

Detection Mechanism ◽

Mobile Crowdsensing ◽

Detection Model ◽

System A ◽

Data Processor ◽

Detection Mechanisms

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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Broadcast Message Authentication Mechanism to Detect Clone and Sybil attacks in VANET’s based on ID-Based Signature Scheme

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i3.12.17770 ◽

2018 ◽

Vol 7 (3.12) ◽

pp. 1102

Author(s):

Kareemulla Shaik ◽

Md. Ali Hussain

Keyword(s):

Attack Detection ◽

Message Authentication ◽

Limited Data ◽

Detection Mechanism ◽

Packet Delivery ◽

Authentication Mechanism ◽

Identity Based ◽

Broadcast Message ◽

Secure Authentication ◽

Sybil Attacks

Broadcast Communication is crucial in VANET communication, to send and receive safety messages within network. Securing these beacon message is a challenge, since they are very prone to clone and Sybil attacks. Many works have been proposed to address this problem but they failed to address how to detect and protect these messages from clone attacks and also limited to static networks with limited data sizes. To achieve this a secure authentication and attack detection mechanism can be designed. In this paper we propose a secure broadcast message authentication and attack detection mechanism with Identity – Based Signatures. Experimental results proved that it can be used in both V2V and V2RSU c communications. Our scheme shown best performance compared to existing schemes in terms of packet delivery ration, detection rate and detection time.

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