TY  - JOUR
AU  - T Sai Lalith Prasad
AU  - Beeram Aditya
AU  - Bodige Likhitha
AU  - G A Asta Govardhan Reddy
PY  - 2025
DA  - 2025/06/27
TI  - Anomaly Detection in Network Traffic Using Machine Learning Techniques
JO  - Global Journal of Engineering Innovations and Interdisciplinary Research
VL  - 5
IS  - 4
AB  - It has been nothing less than exponential growth in the last two decades, and with this growing Internet
has come unprecedented connectivity, significantly increasing the number of cyberattacks. Zeroday attacks have always challenged traditional signature-based detection techniques, which is why
anomaly-based detection techniques have become increasingly important for identifying any anomalies
in normal network behavior. Key features were selected using the Random Forest Regressor. Seven
machine learning algorithms are tested in this experiment.
SN  - 3066-1226
UR  - https://dx.doi.org/10.33425/3066-1226.1129
DO  - 10.33425/3066-1226.1129