Cognitive Analysis of Intrusion Detection System

Abstract

Usability evaluation methods have gained a substantial attention in networks particularly in Intrusion Detection System (IDS) as these evaluation methods are envisioned to achieve usability and define usability defects for a large number of practical software’s. Despite a good number of available survey and methods on usability evaluation, we feel that there is a gap in existing literature in terms of usability evaluation methods, IDS interfaces and following usability guidelines in IDS development. This paper reviews the state of the art for improving usability of networks that illustrates the issues and challenges in the context of design matters. Further, we propose the taxonomy of key issues in evaluation methods and usability problems. We also define design heuristics for IDS users and interfaces that improves detection of usability defects and interface usability compared to conventional evaluation heuristics. The similarities and differences of usability evaluation methods and usability problems are summarized on the basis of usability factors, current evaluation methods and interfaces loopholes

Cyberpsychology: A Longitudinal Analysis of Cyber Adversarial Tactics and Techniques

“…The results of the current study align with the existing body of research on network anomaly detection while also providing unique insights. In line with previous research, the study reaffirms the role of machine learning in detecting network anomalies [18,21,25,[30][31][32]35]. It further extends this by focusing on anomalies in network behavior characterized by unusual patterns potentially indicative of cyberthreats.…”

“…The study's methodology involved the following steps: data preprocessing, exploratory data analysis, anomaly detection, temporal analysis, cross-referencing with established threat intelligence using OSINT, and visualization and reporting. Additionally, by applying advanced ML techniques and data analysis, strategies led to extracting many insights from the extensive dataset [8,14,15,18,25,30,32,35].…”

“…The study followed Hypothesis (H1), proposing a rising sophistication and specificity in cyberattacks, with certain attack types exhibiting a discernible increase. The investigative methodology was built on an analytical process featuring stages such as data preprocessing, exploratory data analysis, clustering and anomaly detection, temporal analysis, cross-referencing with known threat intelligence, visualization and reporting, and interpretation and implications [22,32]. This method is anticipated to generate crucial insights to strengthen cybersecurity strategies in a world marked by growing digital interconnectivity [16,30,31].…”

A Longitudinal Analysis of Cyber Adversarial Tactics and Techniques

“…The results of the current study align with the existing body of research on network anomaly detection while also providing unique insights. In line with previous research, the study reaffirms the role of machine learning in detecting network anomalies [18,21,25,[30][31][32]35]. It further extends this by focusing on anomalies in network behavior characterized by unusual patterns potentially indicative of cyberthreats.…”

“…The study's methodology involved the following steps: data preprocessing, exploratory data analysis, anomaly detection, temporal analysis, cross-referencing with established threat intelligence using OSINT, and visualization and reporting. Additionally, by applying advanced ML techniques and data analysis, strategies led to extracting many insights from the extensive dataset [8,14,15,18,25,30,32,35].…”