Using Correlating data in detection of worms and botnet attacks
Correlating data is very important when it comes to the detection of worms and botnet attacks (Malik & Alankar, 2019). Correlating data, in this case, refers to the data that is related to the worm or the botnet attacks. The process of detecting worms and botnet attacks may be easy or difficult, depending on the approaches that one uses. It is the desire of every person to detect the worms and botnet attacks easily without any significant challenges. There is a need to make use of correlating data to ensure that this happens.
The data will help the person in charge to gain some knowledge, which will assist him or her in detecting the worms and the botnet attacks (Yin, Yang & Wang, 2013). If the cybersecurity specialist in charge of dealing with worms and other attacks doesn’t understand the data concerning worms, then it will be challenging to deal with them. The data may be from a previous worm or botnet attack, which happened to a particular organization. By analyzing the correlating data, then it will be easy to identify some signs which show worm intrusion.
The data will also help the cybersecurity specialist in coming up with some measures to prevent further effects of the worm or botnet attack. By coming up with these measures, it will be easy to deal with the worms since not many devices will be affected. It is good to record the data surrounding worms or any other attacks that have affected an organization. This correlating data will help in dealing with similar threats in the future. It is worth noting that correlating data can be compromised by malicious people. Therefore, organizations should be keen to ensure that they protect this data.
References
Malik, R., & Alankar, B. (2019). Botnet and Botnet Detection Techniques. International Journal Of Computer Applications, 178(17), 8-11. doi: 10.5120/ijca2019918967
Yin, C., Yang, L., & Wang, J. (2013). Botnet Detection Based on Degree Distributions of Node Using Data Mining Scheme. International Journal Of Future Generation Communication And Networking, 6(6), 81-90. doi: 10.14257/ijfgcn.2013.6.6.09