Classification Model for Bot-IoT Attack Detection Using Correlation and Analysis of Variance

Classification Model for Bot-IoT Attack Detection Using Correlation and Analysis of Variance

Aggregate Data; ANOVA; Bot-IoT; Pearson Correlation; Classification

Industry 4.0 requires secure networks as the advancements in IoT and AI exacerbate the challenges and vulnerabilities in data security. This research focuses on detecting Bot-IoT activity used the dataset Bot-IoT UNSW Canberra 2018. Bot-IoTdataset initially showed a significant imbalance, with 2,934,447 entries of attack activity and only 370 entries of normal activity. To address this imbalance, an innovative data aggregation technique was applied, effectively reducing similar patterns and trends. This approach resulted in a balanced dataset consisting of 8 attack activity points and 80 normal activity points. Feature selection using the ANOVA method identified 10 key features from a total of 17. The classification process utilized Random Forest(RF), k-Nearest Neighbors(kNN), Naïve Bayes(NB), and Decision Tree(DT)algorithms, with 100 iterations and an 80:20 training-testing split. Random Forest showed superior performance, achieving 97.5% accuracy, 97.4% precision, and 97.4% recall, with a total computation time of 11.54 seconds. N IN Conn P DstIP and seq had the highest positive correlation value (+0.937) according to Pearson correlation analysis, whereas N IN Conn P SrcIP and state number had the lowest negative correlation value (-0.224).This research focuses on the implementation of a data aggregation strategy to address class imbalance, greatly enhancing machine learning model performance and optimizing training time, is what makes this research distinctive. These results aid in the creation of strong cybersecurity systems that can identify dangers associated with the Internet of Things

Firgiawan Faira1*, Dandy Pramana Hostiadi2, Roy Rudolf Huizen

https://jurnal.iaii.or.id/index.php/RESTI/article/view/6332/1053

Magister Program, Department of Magister Information System, Institut Teknologi dan Bisnis STIKOM Bali, Denpasar, Indonesia

22-04-2025

FAJAR BAGUS W

PDF

ENGLISH

TEXT