Proposing a Detection and Mitigation Approach for DDoS Attacks on SDN-Based IoT Networks
Subject Areas : electrical and computer engineering
fatemeh
MotieShirazi
1
(Yazd University)
Seyedakbar
Mostafavi
2
(Yazd University)
Keywords: Software defined networks, Internet of Things, distributed denial of service attack, entropy,
Abstract :
Internet of Things (IoT) is a network of objects on which objects can communicate with other objects. The Internet of Things is currently constantly under numerous attacks due to technical, legal and human problems. One of the most important of these attacks is the Denial of Service (DoS) attack, in which normal network services are out of service and it is impossible for objects and users to access the server and other resources. Existing security solutions have not been able to effectively prevent interruption attacks in Internet of Things services. Software-oriented network (SDN) is a new architecture in the network based on the separation of the control and data plane of the network. Programmability and network management capability by SDN can be used in IoT services because some IoT devices send data periodically and in certain time intervals. SDN can help reduce or prevent the data flood caused by IoT if properly deployed in the data center. In this article, a method to detect DDoS attacks in Internet of Things based on SDN is presented and then an algorithm to reduce DDoS attacks is presented. The proposed method is based on the entropy criterion, which is one of the most important concepts in information theory and is calculated based on the characteristics of the flow. In this method, by using two new components on the controller to receive incoming packets and considering the time window and calculating entropy and flow rate, a possible attack is detected in the network, and then based on the statistics of the flow received from the switches, the certainty of the attack is determined. Compared to the existing methods, the proposed method has improved 12% in terms of attack detection time and 26% in terms of false positives/negatives.
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