Page 216 - Kaleidoscope Academic Conference Proceedings 2024

2024 ITU Kaleidoscope Academic Conference

such as model complexity and interpretability, and provide
solutions to overcome them. The study emphasizes XAI’s
potential for improving decision-making processes and
lowering risks in energy and power systems, providing useful
insights for researchers and practitioners in the field.

Nassar and Kamal [10] did a comprehensive evaluation
of machine learning and big data analytics strategies for
detecting cybersecurity threats. The authors shed light on
the strengths and limits of various methodologies, as well
as their practical consequences for cybersecurity operations.
In today’s interconnected and data-driven settings, the report
emphasizes the necessity of incorporating machine learning
and big data analytics into cybersecurity frameworks to
improve threat detection skills and effectively reduce cyber
threats. Kumar et al. [11] explore the interaction of
explainable AI (XAI) with blockchain technology inside
the metaverse, with an emphasis on security and privacy
concerns. Their research, which examines the potential
application of XAI approaches with blockchain systems to
enhance security and privacy in virtual environments. The
paper suggests a novel way to tackle the metaverse’s unique
security and privacy problems by offering transparency and
auditability using explainable AI models and leveraging the
irreversible nature of blockchain for data integrity and access
management. The study helps to further our understanding
of new technologies and their implications for cybersecurity
in virtual environments.

Alperin et al. [12] provide a study on enhancing
interpretability for cyber risk assessment with focus
and context visualizations. Their paper propose novel Figure 3 – Proposed system architecture for security incident
visualization approaches to improve the interpretability of response using Mistral-7B language model
cyber vulnerability assessment results. The authors hope that
by combining focus and context visualization methodologies, 3. MISTRAL-7B: A STATE-OF-THE-ART LARGE
analysts can have a better grasp of complicated vulnerability LANGUAGE MODEL
data and be able to make more educated decisions about Large language models (LLM’s) are advanced artificial
cybersecurity operations. The study emphasizes the intelligence system trained on vast amounts of text data
relevance of visualization approaches in bridging the gap to understand and generate human-like language. Mistral
between raw data and actionable insights, and it provides AI [14] [15] [16] a leading artificial intelligence research
practical strategies for increasing the efficacy of vulnerability company has developed Mistral-7B a cutting-edge large
assessment procedures. Dash [13] presents a Zero-Trust language model. It is a part of Mistral AI’s Mistral series of
Architecture (ZTA) architecture to solve the cloud security language models, which are renowned for their exceptional
concerns caused by Large Language Models’ (LLMs) performance and innovative architecture. Figure 3 shows the
black box issues. The paper, available on SSRN, offers proposed system architecture for security incident response
an AI-powered security architecture based on zero-trust and Figure 4 shows the comparison between Mistral-7B,
principles, with the goal of mitigating the risks associated Llama 2 7B, Llama 2 13B and Llama 2 34B Figure 5 shows
with LLM opacity and possible vulnerabilities. By using the performance of pre-trained Mistral-7B model.
AI approaches for anomaly detection and behavior analysis,
the ZTA framework aims to improve cloud security posture • Sparse Mixture-of-Experts Architecture: Mistral-7B
and resilience. The study adds to the continuing discussion leverages a Sparse Mixture-of-Experts (SMoE)
about using AI technology to boost cybersecurity defenses, architecture, which is a complex deep learning
emphasizing the significance of implementing proactive and technique that combines the strengths of multiple
adaptive security measures in the face of increasing threats. expert models. This approach allows the model to
Table 1 shows the summary of literature survey. efficiently distribute computation across specialized

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