Page 214 - Kaleidoscope Academic Conference Proceedings 2024
P. 214

2024 ITU Kaleidoscope Academic Conference




























                Figure 2 – Sample of security incident response generated using Mistral-7B language model on security events


           security events generated by different security systems. This  Intelligence (XAI), which envisioned security measures for
           research seeks to automate the generation of interpretable  cyber threat hunting. Recognizing the rising complexity of
           security responses via integrating Mistral-7B into existing  cyber threats and the need for transparent decision-making in
           systems that makes it easier to understand and respond  security operations, the authors investigated the incorporation
           to potential threats promptly.  It automatically correlate  of XAI approaches into current security frameworks.
           events, gather context, identify root causes and generate  By improving the explainability of security mechanisms
           clear security response messages explaining threats and  such as threat detection algorithms and decision support
           recommended actions.  This AI-driven automation with  systems, the study aims to provide security analysts with
           transparent analysis allows analysts to quickly focus on  actionable insights into discovered threats and suspicious
           critical issues first through prioritized and contextual alerting  behavior.  The authors highlighted the potential of XAI
           that continuously improves with feedback. The scope of this  in boosting cybersecurity methods by combining machine
           research includes exploration of effectiveness of Mistral-7B in  learning interpretability approaches, visualization tools, and
           generating interpretable security responses using real-world  domain-specific knowledge representation. This will allow
           datasets from security solutions. Figure 2 shows the sample  for more effective threat-hunting and response tactics. The
           of security incident response generated using Mistral-7B  study also stressed the importance of user-centered design
           model. The details of implementation is shared in section  ideas are when creating XAI-powered security systems. This
           3 and 4.                                           ensures that human operators can understand and utilize the
                                                              generated insights. Khan et al. [4] enriched the literature
                                                              on corporate security by investigating the use of User and
                      2. LITERATURE SURVEY
                                                              Entity Behavior Analytics (UEBA) for anomaly identification
                                                              and threat mitigation. The study focuses on using UEBA
           Neupane et al.  [2] performed an in-depth review of  techniques to examine the behavior of individuals and entities
           Explainable Intrusion Detection Systems (X-IDS) with the  within corporate networks, with the goal of identifying
           goal of understanding the existing environment, difficulties,  deviations from regular patterns that might indicate possible
           and prospects in the sector.  The study highlighted the  security breaches.  The authors proved the usefulness of
           importance of openness and interpretability in intrusion  UEBA in proactively detecting and responding to new
           detection, highlighting the necessity for security systems  cyber threats using sophisticated analytics and machine
           to develop warnings that are not only accurate but also  learning techniques. The study also showed importance for
           understandable to human analysts.  The authors gained  corporate security to have context-aware anomaly detection
           valuable insights into current practices and identified  and adaptive response mechanisms.  Through empirical
           crucial research paths to propel the field forward by  assessments and case studies, the authors gave practical
           examining various methods to enhance the understandability  insights into the implementation and integration of UEBA
           of intrusion detection systems, such as interpretable machine  systems in real-world security operations, delivering useful
           learning models and rule-based systems. The survey also  lessons for enterprises looking to improve their cybersecurity
           identified practical and implementation problems with using  posture through behavioral analytics. Alahmadi et al. [5]
           explainable intrusion detection systems in the real world. This  performed qualitative research to investigate the viewpoints
           shows how important it is to bridge the gap between complex  of Security Operations Center (SOC) analysts on security
           detection algorithms and useful information for security  alarms, with a special emphasis on the frequency of false
           professionals. Kumar et al. [3] suggested a unique approach  positives. Their findings, presented at the 31st USENIX
           to cybersecurity with their study on Explainable Artificial




                                                          – 170 –
   209   210   211   212   213   214   215   216   217   218   219