Advancements in Machine Learning for Intrusion Detection in Cloud Environments
Shashank Sharma1,a), Kewal Krishan Sharma2,b), Aditya Kumar Jha3,c), Divya Tiwari4,d), Animesh Kumar Jain5,e) , Vikas6,f)
1,3,4,5,6Assistant Professor,2Assosciate Professor
1,2,4,5,6School of Computer Science and Applications IIMT University, Meerut, India
, 3RD Engineering College Ghaziabad, India
Abstract
Cloud computing has transformed the storage, processing, and sharing of data for organizations, but it has also brought about new security concerns. Intrusion detection systems (IDS) are essential in identifying and mitigating potential threats in cloud environments. This research paper delves into the advancements in machine learning techniques for intrusion detection in cloud systems. It provides a comprehensive analysis of different machine learning algorithms, methodologies, and approaches employed to bolster the security of cloud environments. Machine learning algorithms have shown promise in enhancing intrusion detection by analyzing vast amounts of data and detecting patterns indicative of malicious activities. These algorithms can adapt and learn from new data, enabling them to detect previously unseen attacks. The paper examines the benefits and challenges associated with the application of machine learning in intrusion detection. It highlights real-world use cases that demonstrate the effectiveness of machine learning in detecting and preventing various types of cyber threats. Additionally, the paper explores the integration of machine learning with other security mechanisms to augment the overall effectiveness of intrusion detection systems in cloud environments. This integration can involve combining machine learning with traditional rule-based approaches or incorporating anomaly detection techniques. The research paper also discusses the evaluation metrics used to assess the performance of machine learning-based intrusion detection systems, such as detection accuracy, false positive rates, and computational efficiency. By providing an in-depth analysis of machine learning techniques for intrusion detection in cloud systems, this research paper contributes to the understanding of how these technologies can enhance the security of cloud environments. It serves as a valuable resource for organizations seeking to implement robust and efficient intrusion detection systems in their cloud infrastructures.
KEYWORDS
Machine learning, Intrusion detection, Cloud computing, Deep learning, Artificial intelligence, Data mining, Ensemble learning, Real-time detection, Threat intelligence, Network traffic analysis.
