International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 09 | Sep 2025
p-ISSN: 2395-0072
www.irjet.net
A Survey on Cyber Attack Detection Techniques in Cloud Environment Madhu Nagaraj1, Ananya Desai2, Bhoomika V3, Likhithashree S M4, Maheshwari H S5 1Assistant Professor, CSE(Data Science) ,ATME College of Engineering ,Mysuru 570028,India 2UG Student , CSE(Data Science) ,ATME College of Engineering ,Mysuru 570028,India 3UG Student , CSE(Data Science) ,ATME College of Engineering ,Mysuru 570028,India 4UG Student , CSE(Data Science) ,ATME College of Engineering ,Mysuru 570028,India 5UG Student , CSE(Data Science) ,ATME College of Engineering ,Mysuru 570028,India
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solutions that can learn, adapt, and respond to new threats in real-time. Deep learning, a subset of machine learning, has emerged as a powerful tool for intrusion detection and anomaly recognition due to its ability to process large datasets and uncover hidden patterns in network traffic. The AI-powered cyberattack detection project aims to utilize machine learning and artificial intelligence (AI), data analytics techniques to predict and optimize threat detection patterns. By analyzing historical attack data, network traffic patterns, and other relevant factors, the project will provide accurate, reliable, and real-time detection of cyber threats.
critical domains such as healthcare, education, and governance has heightened the need for robust cybersecurity frameworks. Despite the widespread implementation of conventional security mechanisms like firewalls, antivirus software, and intrusion detection systems (IDS), cloud infrastructures remain susceptible to ever-evolving cyber threats. This review paper focuses on the application of deep learning techniques to enhance the detection and prevention of cyber-attacks in cloud computing environments, exploring the rationale for integrating intelligent models that can adapt to complex and large-scale network data, offering improved accuracy and faster detection of anomalies and malware. This review addresses key research questions related to the effectiveness of deep learning models in intrusion detection, anomaly identification, and malware classification. The major studies examined include models that employ ensemble learning, hybrid optimization algorithms, and predictive analytics to handle intrusion detection tasks in cloud and IoT environments. These models, while promising in terms of performance, often face limitations such as interpretability challenges, computational overhead, and difficulty in handling zeroday attacks. The conclusions drawn highlight that deep learning models, when properly optimized and integrated with advanced feature selection and anomaly detection techniques, can significantly enhance the security posture of cloud systems.
2.OVERVIEW In response to escalating cyber threats and the limita tions of traditional security measures, this project focuses on deep learning-powered cyberattack detection to enhance cloud security and resilience. Cloud computing environments are vital to modern digital infrastructure, sup porting critical operations across multiple industries. By leveraging advanced machine learning techniques such as Convolutional Neural Networks (CNNs), Long Short Term Memory (LSTM) networks, and ensemble models, these systems analyze network traffic patterns, user behaviors, and system logs to provide accurate, real-time threat detection capabilities. The integration of deep learning in cybersecurity offers transformative potential for threat identification, enabling proactive defense mechanisms that can adapt to new attack vectors. The project explores various AI applications in cloud security, including anomaly detection, malware classification, and intrusion prevention, offering enhanced protection for dynamic cloud environments.
1.INTRODUCTION Cloud computing has revolutionized the way data and computing resources are accessed and managed, offering scalable, flexible, and cost-efficient solutions to businesses and individuals. As the reliance on cloud infrastructure grows, so does the exposure to potential cyber threats. Cloud environments, by nature, are accessible via the internet and thus susceptible to a wide array of malicious activities, including data breaches, denial of service attacks, and malware intrusions. These threats compromise the confidentiality, integrity, and availability of cloud-based systems and services, making cybersecurity a top priority. Traditional security mechanisms are increasingly inadequate in addressing sophisticated and rapidly evolving attack
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3. LITERATURE REVIEW According to [1], Ahmed, M. S., Al-Badi, A. H., and Gastli, A. present "Deep Learning-Based Intrusion Detection for Cloud Computing Environments" in IEEE Transactions on Cloud Computing. This comprehensive study analyzes the implementation of deep learning architectures specifically designed for cloud-based intrusion detection systems. The research emphasizes the unique challenges posed by cloud environments, including the dynamic nature of virtualized resources and the complexity of multi-tenant architectures.
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