Multi-Stage Learning Framework Using Convolutional Neural Network and Decision Tree-Based Classification for Detection of DDoS Pandemic Attacks in SDN-Based SCADA Systems

被引:9
作者
Polat, Onur [1 ]
Turkoglu, Muammer [2 ]
Polat, Huseyin [3 ]
Oyucu, Saadin [4 ]
Uzen, Huseyin [1 ]
Yardimci, Fahri [5 ]
Aksoz, Ahmet [5 ]
机构
[1] Bingol Univ, Dept Comp Engn, TR-12000 Bingol, Turkiye
[2] Samsun Univ, Dept Software Engn, TR-55000 Samsun, Turkiye
[3] Gazi Univ, Fac Technol, Dept Comp Engn, TR-06500 Ankara, Turkiye
[4] Adiyaman Univ, Dept Comp Engn, TR-02040 Adiyaman, Turkiye
[5] Sivas Cumhuriyet Univ, MOBILERS, TR-58580 Sivas, Turkiye
关键词
SCADA; SDN; cyber pandemic; DDoS attacks; CNN; machine learning; critical infrastructures;
D O I
10.3390/s24031040
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Supervisory Control and Data Acquisition (SCADA) systems, which play a critical role in monitoring, managing, and controlling industrial processes, face flexibility, scalability, and management difficulties arising from traditional network structures. Software-defined networking (SDN) offers a new opportunity to overcome the challenges traditional SCADA networks face, based on the concept of separating the control and data plane. Although integrating the SDN architecture into SCADA systems offers many advantages, it cannot address security concerns against cyber-attacks such as a distributed denial of service (DDoS). The fact that SDN has centralized management and programmability features causes attackers to carry out attacks that specifically target the SDN controller and data plane. If DDoS attacks against the SDN-based SCADA network are not detected and precautions are not taken, they can cause chaos and have terrible consequences. By detecting a possible DDoS attack at an early stage, security measures that can reduce the impact of the attack can be taken immediately, and the likelihood of being a direct victim of the attack decreases. This study proposes a multi-stage learning model using a 1-dimensional convolutional neural network (1D-CNN) and decision tree-based classification to detect DDoS attacks in SDN-based SCADA systems effectively. A new dataset containing various attack scenarios on a specific experimental network topology was created to be used in the training and testing phases of this model. According to the experimental results of this study, the proposed model achieved a 97.8% accuracy rate in DDoS-attack detection. The proposed multi-stage learning model shows that high-performance results can be achieved in detecting DDoS attacks against SDN-based SCADA systems.
引用
收藏
页数:18
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