International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 04 | Apr 2024
p-ISSN: 2395-0072
www.irjet.net
IntruShield: Smart Intrusion Detection System Shraddha Trivedi1, Hrishika Singh2, Aryan Pal3, Saumya Thacker4, Smita Bansod5, Pranali Vhora6 ---------------------------------------------------------------------***--------------------------------------------------------------------sophistication of cyber threats. To address this challenge, Abstract - Strong Network Intrusion Detection Systems
this research endeavors to present an innovative and comprehensive approach to network intrusion detection, harnessing the power of various machine learning algorithms. The key to our suggested NIDS is the application of a variety of machine learning methods, each of which brings a special set of advantages to the job of differentiating between abnormal and typical network activity. The techniques used are Decision Tree Classifier, Linear Discriminant Analysis, Quadratic Discriminant Analysis, K-Nearest Neighbours, Ridge Classifier, and Logistic Regression. Our goal in merging these algorithms is to build an adaptable and synergistic intrusion detection system that can effectively respond to the ever-changing landscape of cyber threats. The significance of this research extends beyond the mere application of machine learning in intrusion detection; it addresses the pressing need for a holistic and versatile solution that can detect a wide range of cyber threats. The study explores the intricate relationship between different machine learning algorithms and their efficacy in capturing and interpreting patterns within network traffic data. Through rigorous experimentation and comparative analyses, we aim to provide insights into the strengths and weaknesses of each algorithm, facilitating an informed selection based on specific intrusion detection requirements. Using a variety of machine learning methods, the main goal of this research is to create a reliable and flexible Network Intrusion Detection System (NIDS). In light of changing cyberthreats, the study attempts to improve intrusion detection effectiveness and solve the shortcomings of conventional rule-based systems.
(NIDS) are now essential for protecting sensitive data and guaranteeing the integrity of network infrastructures due to the exponential growth of digital data and the rising sophistication of cyber attacks. Using a variety of machine learning methods, such as Logistic Regression, Ridge Classifier, K-Nearest Neighbours, Linear Discriminant Analysis, Quadratic Discriminant Analysis, and Decision Tree Classifier, this study suggests a novel method for network intrusion detection. The study employs a diverse set of machine learning techniques to enhance the accuracy and reliability of intrusion detection, addressing the limitations of traditional rule-based systems. Each algorithm contributes unique capabilities in capturing and understanding patterns within network traffic data, thereby improving the overall detection performance. The dataset used for training and evaluation is a representative collection of network traffic, encompassing both normal and anomalous activities. Features extracted from the network traffic data include packet headers, payload characteristics, and temporal information. The proposed system incorporates pre-processing techniques to handle imbalanced datasets and optimize feature selection, ensuring the models are robust and efficient. The findings of the study enable network managers and cybersecurity specialists to safeguard against a wide range of online threats and contribute to the advancement of intrusion detection methods. Enhancing threat detection accuracy and offering scalability and adaptability to changing network conditions make the recommended NIDS an effective weapon in the ongoing battle against cyber adversaries.
1.
Algorithmic Integration: To take advantage of each machine learning algorithm's special abilities in identifying a variety of patterns in network traffic data, combine Logistic Regression, Ridge Classifier, K-Nearest Neighbours, Linear Discriminant Analysis, Quadratic Discriminant Analysis, and Decision Tree Classifier into a single NIDS framework.
2.
Comprehensive Evaluation: Conduct a rigorous evaluation of the proposed NIDS using representative datasets encompassing normal and anomalous network activities. Assess the system's performance across various metrics such as precision, recall, F1-score, and the area under the receiver operating characteristic curve to ensure
Key Words: Network Traffic Patterns, Anomalous Activities, Network Traffic Data, Packet Headers, Online Threats
1.INTRODUCTION In the contemporary landscape of interconnected digital ecosystems, the proliferation of cyber threats has grown exponentially, posing significant challenges to the security and integrity of network infrastructures. As organizations increasingly rely on digital communication and data exchange, the need for robust Network Intrusion Detection Systems (NIDS) becomes paramount. Traditional rule-based systems, while effective to some extent, struggle to keep pace with the evolving
© 2024, IRJET
|
Impact Factor value: 8.226
|
ISO 9001:2008 Certified Journal
|
Page 1961