Abstract:
This research is a finding and recognition type of research. That paper focuses on exploratory data analysis and YOLOv3, you only look once at version three, a real-time recognizing model. The hypothesis of this research are, a cyber-attack gives the same pattern in all the different, different IP addresses, and the research model, the YOLOv3 model can recognize the cyber-attack by seeing its pattern. First, the researcher collects more than one hundred seventy-eight thousand data from the cyber department of some companies. Then, the researcher does exploratory data analysis of that data in the jupyter notebook. Then, the researcher finds all the important information about cyber-attacks. Then, the researcher finds the patterns of some cyber-attacks from the information of the data collection. Then, the researcher collects pictures of the patterns. Then, with pictures of those patterns, the researcher labeled those pictures by labellmg software and create a zip file of them. Then the researcher use Google colab and trained, "you only look once version three", the YOLOv3 model to detect the name of the pattern. The machine can detect the pattern and can tell us about what cyber-attack it is by only seeing the picture of the pattern. The researcher finds the patterns for eight IP addresses and in all the IP addresses, the attacks give the same patterns. So, the hypothesis is true in all that cases. By this research model, we can easily know about any kind of cyber-attack in detail and also can find the cyber-attacks by their patterns. So, the researcher use jupyter notebook, Google Colab, and pycharm environment. Pattern study always plays an important role to know about anything. By knowing and learning about anything’s pattern, we can easily understand anything. This research does that thing greatly. The researcher first finds the patterns of some cyber-attacks, then make a model which can recognize the cyber-attack by just seeing the pattern.
