Computer Science Collection, Forsyth Library Database

I searched “ai” AND “cybersecurity”  with a limiter of peer-reviewed  and articles only.
Results: 325
Two articles from the results were useful as they provided me with new information.  However most of the information was useless for what I was looking for.

Alrajhi, A. M. (2020). A Survey of Artificial Intelligence Techniques for Cybersecurity Improvement. International Journal of Cyber-Security and Digital Forensics, 9(1), 34+. https://link.gale.com/apps/doc/A652011405/CDB?u=klnb_fhsuniv&sid=bookmark-CDB&xid=9c0dea3c

Annotated Bibliography:
The article discusses how artificial intelligence is being used to improve cybersecurity for businesses. Intrusion detection systems are the most prominent sources of artificial intelligence and machine learning.  AI intrusion detection systems establish baselines to determine what abnormal traffic will look like and be able to ignore what is normal, non-malicious traffic. Another prominent development is that of neural networks. These networks enable devices to access a multitude of different patterns of information.  The use of artificial intelligence is described as a “double-edged sword” because it can miss critical flows of information without proper configurations.

Computer Science Collection, Forsyth Library Database

I searched for “ai” AND “cybersecurity” with a limiter of peer-reviewed and articles only.
Results: 5
Multiple articles from the results seemed to be directly relevant to my research.

KALOUDI, N., & JINGYUE LI. (2020). The AI-Based Cyber Threat Landscape: A Survey. ACM Computing Surveys, 53(1), 1–34. https://doi.org/10.1145/3372823

Annotated Bibliography:
Artificial intelligence has enabled more systems to be automated which has both been a positive and a negative in the areas of cybersecurity. Automation has provided the attackers with a means to attack businesses more efficiently and at a quicker pace. Cybersecurity has improved with ai, however, so has the threat actors methods of attack against businesses.

Computer Source, Forsyth Library Database

I searched for “machine learning” AND “network intrusion” with a limiter of peer-reviewed and articles only.
Results: 14
Two of the articles from this search were very useful because they provided information on the mobile aspect of cybersecurity in parallel with machine learning.

Zhao, F., Zhang, H., Peng, J., Zhuang, X., & Na, S.-G. (2020). A semi-self-taught network intrusion detection system. Neural Computing & Applications, 32(23), 17169–17179. https://doi.org/10.1007/s00521-020-04914-7

Annotated Bibliography:
The article discusses the development of a semi self-taught intrusion detection system. It is primarily important because it discusses the short-comings of intrusion detection systems in use today. Increased complexities involved in todays types of attacks require the need for more robust security systems to counteract these attacks. Their intrusion detection development enabled precise identifications in real-time. The ability to compare a large amount of information against traffic enables for high efficiency and it ensures that malicious traffic will not enter devices.

Computer Source, Forsyth Library Database

I searched for “machine learning security” AND “cybersecurity” with a limiter of peer-reviewed and articles only.
Results: 18
One of the articles was useful because it describe how machine learning is function at the cybersecurity level in enterprises.

JAWED, H., ZIAD, Z., KHAN, M. M., & ASRAR, M. (2018). Anomaly detection through keystroke and tap dynamics implemented via machine learning algorithms. Turkish Journal of Electrical Engineering & Computer Sciences, 26(4), 1698–1709. https://doi.org/10.3906/elk-1711-410

Annotated Bibliography:
This interesting article describes the use of tracking typing patterns by users. Tracking typing patterns can provide a surfeit about of information when it is put through complex algorithms that perform sorting and searching. It is required that a database of previous known patterns are kept to compare new results against. A technology of this type can provide new ways of handling intrusion responses.

Computer Source, Forsyth Library Database

I searched for “network security” AND “machine learning” with a limiter of peer-reviewed and articles only.
Results: 20
Two of the results were directly helpful to understanding the tie between machine learning and intrusion detection.

Kaur, S., & Singh, M. (2020). Hybrid intrusion detection and signature generation using Deep Recurrent Neural Networks. Neural Computing & Applications, 32(12), 7859–7877. https://doi.org/10.1007/s00521-019-04187-9

Annotated Bibliography:
Kaur and Singh investigate the use of automation systems in signature-based detections. The use of neural networks is prominent in these uses. Neural networks provide an ideal advantage to detecting malicious information. It also ensures a low false positive rate with is critical to any such system.

Saturation:

I have ensured uses of different search terms and databases. As I have conducted my research and reviews of various articles I have kept in mind the importance of diversity and searching multiple sources. I have only made use of three databases but they hold a plethora of information and my use of different search terms have allowed me to find everything I can regarding cybersecurity and machine learning and their connections.