| Title | MACHINE LEARNING ALGORITHMS AND THEIR APPLICATIONS IN CLASSIFYING CYBER-ATTACKS ON A SMART GRID NETWORK |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Aribisala, Adedayo, Khan, Mohammad S., Husari, Ghaith |
| Conference Name | 2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) |
| Keywords | composability, cryptography, cyber physical systems, denial of service attack, Detecting Cyberat-tacks, Distributed databases, FDIA, Hybrid SVM, machine learning algorithms, NSL-KDD data set, pubcrawl, Radio frequency, Real-time Systems, resilience, Resiliency, Support vector machines, SVM, telecommunication traffic, virtual machine security, Virtual machining |
| Abstract | Smart grid architecture and Software-defined Networking (SDN) have evolved into a centrally controlled infrastructure that captures and extracts data in real-time through sensors, smart-meters, and virtual machines. These advances pose a risk and increase the vulnerabilities of these infrastructures to sophisticated cyberattacks like distributed denial of service (DDoS), false data injection attack (FDIA), and Data replay. Integrating machine learning with a network intrusion detection system (NIDS) can improve the system's accuracy and precision when detecting suspicious signatures and network anomalies. Analyzing data in real-time using trained and tested hyperparameters on a network traffic dataset applies to most network infrastructures. The NSL-KDD dataset implemented holds various classes, attack types, protocol suites like TCP, HTTP, and POP, which are critical to packet transmission on a smart grid network. In this paper, we leveraged existing machine learning (ML) algorithms, Support vector machine (SVM), K-nearest neighbor (KNN), Random Forest (RF), Naive Bayes (NB), and Bagging; to perform a detailed performance comparison of selected classifiers. We propose a multi-level hybrid model of SVM integrated with RF for improved accuracy and precision during network filtering. The hybrid model SVM-RF returned an average accuracy of 94% in 10-fold cross-validation and 92.75%in an 80-20% split during class classification. |
| DOI | 10.1109/IEMCON53756.2021.9623067 |
| Citation Key | aribisala_machine_2021 |
MACHINE LEARNING ALGORITHMS AND THEIR APPLICATIONS IN CLASSIFYING CYBER-ATTACKS ON A SMART GRID NETWORK