Enhancing Intrusion Detection Performance through Deep Learning Method

Enhancing Intrusion Detection Performance through Deep Learning Method

In the realm of cyber and data security, the field of intrusion detection systems (IDS) stands out as an area of active research. Traditional methods, such as data mining, statistical evaluation, and artificial neural networks, face significant challenges in achieving accurate intrusion detection. However, the emergence of machine learning algorithms offers promising solutions to this challenge. This paper presents a novel approach to intrusion detection, focusing on leveraging deep learning methodologies. Deep learning, as an extension of machine learning, holds the potential to enhance the accuracy of IDS. The proposed method employs a cascaded three-level convolutional neural network (CNN) architecture. Efficiency and scalability in intrusion detection hinge upon effective feature reduction. By streamlining features, the capacity for intrusion classification and attack detection is significantly enhanced. Notably, when applied to datasets like KDDCUP99 with diverse attributes, the proposed algorithm achieves a detection ratio nearing 100%, albeit with a slightly lower classification ratio due to attribute diversity. Comparative analysis demonstrates the superiority of the cascaded CNN algorithm over traditional CNN methods in both feature reduction and classification tasks. Particularly, the proposed algorithm showcases remarkable efficiency in handling dynamic attributes, thereby improving classification accuracy. the proposed approach utilizing cascaded CNN architecture presents a substantial advancement in intrusion detection and classification compared to conventional methods. Through the integration of deep learning techniques, this methodology offers a robust solution to the challenges encountered in traditional intrusion detection systems.

The global expansion of digitalization has interconnected the world into a single platform known as the cyber world. This encompassing digital realm includes all tools and platforms utilized for data sharing and transmission over the internet. However, the exchange of data within this vast information highway necessitates robust security systems to ensure the integrity and authentication of data. In response to these challenges, Anderson developed an intrusion detection system (IDS) in 1980, marking a significant milestone in cybersecurity research. Today, IDS remains one of the most widely employed methods for detecting various forms of malicious attacks within network environments. Deep learning techniques, such as Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM), have demonstrated remarkable effectiveness across diverse domains, including natural language processing, computer vision, and speech recognition. Our study introduces a unified model called Multiscale Convolutional Neural Network with Long Short-Term Memory (MSCNN-LSTM), which integrates spatial-temporal information for intrusion detection purposes. CNN and LSTM, being prominent deep learning algorithms, excel in extracting spatial and temporal properties from datasets, respectively. While CNN primarily focuses on spatial feature extraction and has shown significant advancements in computer vision tasks, LSTM incorporates self-connected memory units to capture temporal dependencies within sequences. The synergy between these techniques enables the MSCNN-LSTM model to effectively handle intrusion detection challenges by leveraging both spatial and temporal characteristics of intrusion data. The complexity of intrusion data poses challenges for detection performance, emphasizing the need for feature reduction techniques. In this dissertation, we explore the utilization of CNN algorithms for feature reduction in intrusion detection systems. By prioritizing essential features and eliminating redundant ones, we aim to enhance the efficiency of IDS without compromising detection accuracy. this paper presents a comprehensive investigation into the application of deep learning techniques, particularly CNN and LSTM, for intrusion detection. The proposed MSCNN-LSTM model demonstrates promising capabilities in extracting spatial-temporal features crucial for effective intrusion detection. Additionally, our exploration of CNN-based feature reduction techniques offers insights into optimizing IDS performance. The subsequent sections of the paper delve into related work, proposed methodology, and experimental analysis, and conclude with future research directions.

The subsequent discussion delves into the methodology of machine learning, examining the strengths and weaknesses of various approaches concerning intrusion detection capability and model complexity. Recent research indicates a prevalent use of Convolutional Neural Network (CNN) methodologies to bolster the performance and efficacy of Network Intrusion Detection Systems (NIDS) in terms of detection accuracy and reducing False Alarm Rates (FAR). Notably, CNN techniques feature prominently in approximately 80% of proposed solutions, with a decision tree (dt) and Multilayer Perceptron (MLP) emerging as the most favored algorithms. Furthermore, analysis reveals that about 70% of the recommended approaches were evaluated using the KDD Cup'99 and NSL-KDD datasets due to the wealth of data available in these repositories. Nevertheless, as these datasets fail to adequately represent recent network threats, the applicability of proposed solutions in real-time scenarios is somewhat limited. To address these limitations and enhance intrusion detection accuracy, this study underscores the importance of bridging research gaps. Specifically, there's a need for improving model performance in detecting low-frequency attacks in real-world environments. Additionally, the exploration of cost-effective strategies to streamline complexity in proposed models is deemed essential for achieving optimal performance in intrusion detection applications.

Author 

Dr.P.K.Sharma

Principal, NIRT