Reflection Paper

Running Head: NETWORK 1

NETWORK 4

Network Activity

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It is possible to differentiate in between suspicious, malicious as well as normal activities through: studying the behavior of the users that are normal, the resources in addition to the interactions with the networking systems. After that, what is supposed to be done next is creating patterns for every single activity, any of the behavior that is observed that happens to deviate from the drawn sequences is perceived to be suspicious, and therefore, it turns out to be malicious (Radford, et.al, 2018).

For instance, it is possible to utilize these:

Signature scan mechanisms which is a technique that utilizes a database in keeping signatures. The passive scan of the network traffic tend to be carried out, the any of the sequences that happens to match the signatures that are stored are considered to be malicious as well as suspicious. There is also the intrusion detection as well as prevention systems that is abbreviated as IDPS. This is well known for carrying out detection as well as the prevention of attacks that are malicious in addition to any activity that might be suspicious on the specific networks. This technique is one that has the capacity to monitor the traffic of the network in addition to analyzing the events that are suspicious.

Examples of these activities that are perceived to be suspicious mainly include:

Modem activity that is unusual or the presence of connection in addition to deviation that is not expected from network traffic that is authoritative. The example of activities that are malicious include: violations of protocols like invalid option bits within a TCP packet, connections that are made during times that are unusual as well as network connection that is from places in addition to locations that are unusual (Fernandes, et.al, 2019).

References

Fernandes, G., Rodrigues, J. J., Carvalho, L. F., Al-Muhtadi, J. F., & Proença, M. L. (2019). A comprehensive survey on network anomaly detection. Telecommunication Systems, 70(3), 447-489. https://link.springer.com/article/10.1007/s11235-018-0475-8

Radford, B. J., Apolonio, L. M., Trias, A. J., & Simpson, J. A. (2018). Network traffic anomaly detection using recurrent neural networks. arXiv preprint arXiv:1803.10769. https://arxiv.org/abs/1803.10769