مدل ترکیبی برای کشف نفوذ با استفاده از تحلیل داده‌های بزرگ و تکنیک‌های یادگیری عمیق در شبکه‌های توزیع‌شده
محورهای موضوعی : پردازش چند رسانه ای، سیستمهای ارتباطی، سیستمهای هوشمند
1 - دانشجوی دکتری، گروه مدیریت، واحد نجف آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران
کلید واژه: کشف نفوذ, امنیت سایبری, یادگیری عمیق, داده‌های بزرگ, محیط‌های توزیع‌شده,
چکیده مقاله :
این پژوهش مدل ترکیبی جدیدی برای کشف نفوذ در محیط‌های توزیع‌شده ارائه می‌دهد که بر تحلیل داده‌های بزرگ و تکنیک‌های یادگیری عمیق متکی است. هدف اصلی مطالعه، طراحی سیستمی با دقت بالا و توانایی شناسایی و پاسخ سریع به حملات سایبری است. مدل پیشنهادی با استفاده از شبکه‌های عصبی پیچشی و بازگشتی الگوهای حملات پیچیده را در داده‌های ترافیک شبکه تشخیص می‌دهد. این مدل بر روی داده‌های مجموعه KDD Cup 99 آزمایش شده و با دقت 98%، نرخ شناسایی 95% و نرخ مثبت کاذب کمتر از 2% عملکرد برجسته‌ای داشته است. توانایی مدل در یادگیری مستمر و به‌روزرسانی خودکار آن را قادر می‌سازد تا به تهدیدات جدید و ناشناخته به‌سرعت واکنش نشان دهد و امنیت داده‌های حساس را با استفاده از روش‌های پیشرفته رمزنگاری و یادگیری فدراسیون تضمین می‌کند. این مدل توانایی ارائه پاسخ‌های سریع و بهینه به حملات را داراست که آن را برای استفاده در شبکه‌های کوچک و بزرگ مناسب می‌کند. مقیاس‌پذیری این سیستم، که از تحلیل داده‌های بزرگ و یادگیری عمیق بهره می‌برد، آن را قادر می‌سازد تا در محیط‌های پیچیده و با حجم زیاد داده‌های شبکه نیز کارآمد باشد. این سیستم نه‌تنها دقت و عملکرد کشف حملات سایبری را بهبود می‌بخشد، بلکه باعث کاهش تعداد هشدارهای اشتباه نیز می‌شود که این امر به بهبود اعتماد به زیرساخت‌های امنیتی کمک می‌کند. افزون بر این، توجه ویژه‌ای به چالش‌های امنیتی در شبکه‌های توزیع‌شده شده و این سیستم توانایی بهبود عملکرد امنیتی در مقیاس‌های مختلف را دارد. این پژوهش با ایجاد پایه‌های محکمی برای توسعه سیستمهای هوشمند کشف نفوذ، گامی مؤثر در جهت پیشرفت چارچوب‌های امنیت سایبری در مقیاس جهانی به شمار می‌رود و راهکارهای نوینی برای مقابله با تهدیدات جدید ارائه می‌دهد.
This research presents a new hybrid model for intrusion detection in distributed environments, based on big data analytics and deep learning. The primary goal of the study is to develop a system with high accuracy and efficiency for rapid identification and response to cyberattacks. The methodology includes preprocessing network traffic data and utilizing convolutional and recurrent neural networks to detect complex attack patterns. The proposed model, evaluated on the KDD Cup 99 dataset, demonstrated remarkable performance with 98% accuracy, a 95% detection rate, and a false positive rate of less than 2%. The model's ability for continuous learning and automatic updates enables it to adapt to emerging threats. Additionally, by incorporating advanced encryption methods and federated learning techniques, sensitive data security is ensured. This study can serve as a foundation for future research in cybersecurity and the development of intelligent intrusion detection systems.
Introduction: In the current digital age, Cybersecurity has become one of the main concerns of various organizations and institutions. With the increasing complexity and number of cyber threats, the necessity of employing advanced systems to identify and counter these threats becomes more apparent. Intrusion Detection refers to the process of detecting unauthorized attempts to access computer systems and networks. These systems attempt to identify unusual patterns in network traffic and prevent attacks. Utilizing Deep Learning techniques and Big Data analysis enables the design of more advanced intrusion detection systems capable of detecting more complex attacks[1,2]. Distributed and Cloud networks are among the emerging and widely used technologies that are rapidly expanding due to their superior capabilities in data processing and storage. These networks allow companies and institutions to offer their services with greater efficiency and flexibility. However, this rapid and extensive development of distributed and cloud networks also brings new security challenges. Among these challenges is the need for efficient and advanced intrusion detection systems to identify and respond to cyberattacks [3]. One effective approach to enhance the accuracy and efficiency of intrusion detection systems in distributed environments is the use of Hybrid Models of deep learning and Big Data analysis. These models can identify cyberattacks with greater accuracy by analyzing complex and sequential patterns in network data. Recent studies have shown that using deep learning models can significantly improve the performance of intrusion detection systems [4]. The present research focuses on the design and implementation of a hybrid intrusion detection system in distributed environments. The proposed system, using big data analysis and deep learning, has the capability to identify and respond to cyberattacks in real-time. The main objective of this paper is to present a model with high accuracy and efficiency to meet the security needs of distributed and cloud networks, and in addition to examining the theoretical foundations and literature review, it evaluates the proposed model with real data. The results of this research can help improve intrusion detection systems. In this study, special attention has been paid to the issue of system scalability. Given the vast volume of data generated in modern networks, the capability for parallel and distributed processing has been incorporated into the system design. This feature enables the use of the system in large and complex environments. Additionally, one of the innovations of this research is the integration of reinforcement learning techniques with deep learning models. This approach allows the system to continuously learn from its experiences and improve its performance. This capability is particularly useful in dealing with emerging and unknown threats. Furthermore, the present research examines the ethical and legal aspects of using artificial intelligence in security systems. This topic is of particular importance given the increasing concerns about privacy and ethical use of advanced technologies.
Method: This research generally consists of two parts: review and exploratory. In the review section, a comprehensive examination of research literature in the field of intrusion detection systems and deep learning models was conducted, and scientific articles from the last five years from reputable databases such as IEEE, Springer, and Elsevier were reviewed. The aim of this section was to identify research related to the optimization of intrusion detection models and analyze gaps in past studies. Additionally, Google Scholar and PubMed search tools were used to find new articles related to Cybersecurity and deep learning. In the exploratory section, data were collected from two main sources including scientific articles and real datasets related to cyberattacks such as KDD Cup 99 and NSL-KDD. These sets are well-suited for evaluating and analyzing the proposed models and contain information on
various cyberattacks. The data preprocessing process included cleaning and normalizing them to remove duplicate and invalid data and extract key features related to cyberattacks. In the implementation of deep learning models, convolutional neural networks and recurrent neural networks were used. The parameters of these models were adjusted based on the number of layers, learning rate, and number of neurons, and optimization techniques such as Adam and Dropout were employed to improve performance and prevent overfitting. Model evaluation was performed using metrics such as Accuracy, detection rate, and false positive rate. To improve the accuracy and efficiency of the models, methods such as Cross-Validation and transfer learning were employed to enable knowledge transfer between different models and achieve optimal results. Python was used as the main language for model implementation and data analysis, and tools such as TensorFlow and Keras were used as deep learning frameworks. Additionally, R was used for statistical analysis and initial analyses. This multidimensional approach and the use of various tools have provided the possibility of presenting an efficient and accurate model for detecting and countering cyberattacks in distributed environments.
Results: The main objective of this research was to develop and evaluate a hybrid intrusion detection model using deep learning techniques and big data analysis in distributed environments. The proposed model combined convolutional neural networks (CNN) for spatial feature extraction and recurrent neural networks (RNN) for temporal pattern analysis from network traffic data. The evaluation was conducted using the KDD Cup 99 dataset, which includes diverse categories of attacks, such as DoS, R2L, U2R, and Probe attacks. The CNN-RNN hybrid model demonstrated excellent performance, achieving a detection accuracy of 98% and a false positive rate of less than 2%. The model successfully identified both known and complex unknown attack patterns, leveraging big data analysis to process large volumes of network traffic efficiently. Key Results:
- Detection accuracy of 98% in identifying cyber-attacks.
- False positive rate reduced to less than 2%, crucial for minimizing false alarms.
- Efficient detection of complex and sequential attack patterns like DoS and R2L attacks.
- These findings confirm the effectiveness of the proposed model in improving the accuracy and efficiency of intrusion detection systems.
Discussion: The results of this study indicate that combining deep learning models with big data analysis significantly improves the performance of intrusion detection systems, particularly in distributed environments. The CNN-RNN hybrid model efficiently captured both spatial and temporal patterns in network traffic, enabling it to detect complex cyber-attacks with high accuracy. The ability to reduce the false positive rate to less than 2% is a critical achievement, as it helps minimize unnecessary alerts and enhances the reliability of security systems. Furthermore, the use of the KDD Cup 99 dataset, which covers a wide range of cyber-attack scenarios, validated the robustness of the model. The high detection rate of over 95% for different attack types, including DoS and R2L, demonstrates the model’s capability to identify both traditional and evolving threats in real-time environments. The hybrid model's combination of deep learning and big data analysis allows for scalable solutions in advanced cybersecurity frameworks, making it suitable for real-world applications where rapid and accurate threat detection is essential. In conclusion, the hybrid CNN-RNN model shows great promise in enhancing the efficiency of intrusion detection systems, providing a reliable tool for defending against emerging and complex cyber threats.
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