Assignment 8

.docx

School

American Public University *

*We aren’t endorsed by this school

Course

ISSC452

Subject

Computer Science

Date

Feb 20, 2024

Type

docx

Pages

4

Uploaded by AmbassadorHummingbirdPerson531

Report
Reflection on Class Discussions Kyle Namen American Public University Cybersecurity ISSC452 Dr. Ron L. Booth 12/26/2023
2 During this class, we covered many interesting topics during the weekly discussions, but to me, week two was the most interesting. Learning about and discussing Intrusion Detection Systems (IDS) and how they play a crucial role in safeguarding computer networks by identifying and mitigating malicious activity was my favorite topic because it provided a peek behind the curtain of what the Information Technology departments or digital forensic analysts are using to fight against criminals. Among the various detection techniques, signature-based detection stands out as a well-established and direct method. Malicious network activity, often referred to as malware, encompasses a wide range of harmful programs or code, including trojans, viruses, and worms ( Corelight, n.d.) . Signature-based detection serves as a foundational approach to swiftly identify such activity within network traffic. A signature identifies a specific pattern. In the context of malware detection, signatures are extracted from indicators of compromise (IOCs) identified by security researchers or network defenders ( RiskXchange, 2023) . These IOCs serve as the building blocks for creating threat signatures or IDS rules. While signature-based detection remains a cornerstone of IDS, it should be complemented with other methods, such as anomaly detection, to address emerging threats. Anomaly-based detection is a powerful technique within IDS that focuses on identifying data points or patterns significantly deviating from expected norms. As cyber threats continue to evolve, detecting anomalous activity becomes critical. Anomaly-based detection provides a proactive approach by flagging deviations from established patterns ( Daszczyszak, 2019) . An anomaly refers to any data point or behavior that significantly diverges from the norm. These deviations could indicate unusual events, errors, or even potential fraud. Anomaly detection aims to identify these outliers within network traffic or
Your preview ends here
Eager to read complete document? Join bartleby learn and gain access to the full version
  • Access to all documents
  • Unlimited textbook solutions
  • 24/7 expert homework help