Conclusion and Future Work, and References

Table of Links

Abstract and I. Introduction

II. Literature Review

III. Methodology

IV. Results and Discussion

V. Conclusion and Future Work, and References

V. CONCLUSION AND FUTURE WORK

In conclusion, our research addresses the rising threat of obfuscated malware in connected devices and the internet landscape. Through memory dump analysis and diverse machine learning algorithms, we’ve explored effective detection strategies and illuminated their strengths and limitations using the CIC-MalMem-2022 dataset. Emphasizing the synergy between machine learning and traditional security methods, our work underscores the need for a comprehensive defense strategy in the dynamic cybersecurity realm. While acknowledging the ever-evolving malware landscape, our research lays the groundwork for future endeavours, advocating continuous adaptation. Future efforts should focus on refining algorithms, exploring new data sources, and fostering interdisciplinary collaboration. We envision research on hybrid approaches, combining machine learning and signature-based methods, and studying the impact of adversarial attacks and explainable AI to enhance detection system robustness and transparency. In summary, our study provides valuable insights for resilient cybersecurity solutions, addressing the challenges of obfuscated malware and advancing detection capabilities to safeguard digital ecosystems against emerging threats.

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