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Journal articles on the topic 'Network intrusion detection system'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Priyavengatesh, A. "A Predictive Model Using Deep Learning Neural Network for Efficient Intrusion Detection." International Journal for Research in Applied Science and Engineering Technology 11, no. 10 (2023): 577–85. http://dx.doi.org/10.22214/ijraset.2023.56020.

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Abstract: Network intrusion detection system helps to detect exploitations and mitigate damages. A network intrusion detection system detects the network traffic that deviates from the normal behavioral pattern. Developing an efficient intrusion detection system has many challenges and the patterns associated with one type of intrusion differ from other intrusions. In such situations, understanding different patterns and differentiating intrusions becomes essential to detect anomalies and attacks in the network. Deep learning models offer more power and intelligence to the detection system and

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Sreenivasa Reddy, G., and G. Shyama Chandra Prasad. "INTRUSION DETECTION SYSTEM USING CLUSTERING ALGORITHMS OF NEURAL NETWORKS." International Journal of Advanced Research 11, no. 11 (2023): 607–14. http://dx.doi.org/10.21474/ijar01/17861.

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This research paper explores the application of clustering algorithms in neural networks for enhancing Intrusion Detection Systems (IDS). Intrusion Detection Systems are critical in safeguarding information systems from unauthorized access, misuse, or damage. The dynamic nature of cyber threats necessitates advanced approaches for detection and prevention. Neural networks, with their ability to learn and adapt, offer significant potential in identifying and classifying network intrusions. This paper reviews various neural network architectures and clustering algorithms, their integration in ID

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Solomon, Irin Anna, Aman Jatain, and Shalini Bhaskar Bajaj. "Intrusion Detection System Using Deep Learning." Asian Journal of Computer Science and Technology 8, no. 2 (2019): 105–10. http://dx.doi.org/10.51983/ajcst-2019.8.2.2132.

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Intrusion detection system (IDS) plays a very critical part in identifying threats and monitoring malicious activities in networking system. The system administrators can use IDS to detect unauthorized access by intruders in different organizations. It has become an inevitable element to the security administration of every organization. IDSs can be generally categorized into two categories. The first group focuses on patterns/signatures of network packets/traffic and they identify network intrusions using rule-based matching. The second group uses machine learning (ML) based approaches such a

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Ali, Rashid, and Supriya Kamthania. "A Comparative Study of Different Relevant Features Hybrid Neural Networks Based Intrusion Detection Systems." Advanced Materials Research 403-408 (November 2011): 4703–10. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.4703.

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Intrusion detection is the task of detecting, preventing and possibly reacting to the attacks and intrusions in a network based computer system. The neural network algorithms are popular for their ability to ’learn’ the so called patterns in a given environment. This feature can be used for intrusion detection, where the neural network can be trained to detect intrusions by recognizing patterns of an intrusion. In this work, we propose and compare the three different Relevant Features Hybrid Neural Networks based intrusion detection systems, where in, we first recognize the most relevant featu

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Abdulhameed, Abbas A., Sundos A. Hameed Alazawi, and Ghassan Muslim Hassan. "An optimized model for network intrusion detection in the network operating system environment." Mesopotamian Journal of CyberSecurity 4, no. 3 (2024): 75–85. http://dx.doi.org/10.58496/mjcs/2024/017.

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With the heavy reliance on computers and information technology to send and receive data across networks of various types, there has been concern about securing that data from intrusions and cyber-attacks. The expansion of network usage has led to an increase in hacker attacks, which has led to prioritizing cybersecurity precautions in detecting potential threats. Intrusion detection techniques are a critical security measure to protect networks in both personal and corporate environments that are managed by network operating systems. For this, the paper relies on designing a network intrusion

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Veselý, A., and D. Brechlerová. "Neural networks in intrusion detection systems." Agricultural Economics (Zemědělská ekonomika) 50, No. 1 (2012): 35–40. http://dx.doi.org/10.17221/5164-agricecon.

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Security of an information system is its very important property, especially today, when computers are interconnected via internet. Because no system can be absolutely secure, the timely and accurate detection of intrusions is necessary. For this purpose, Intrusion Detection Systems (IDS) were designed. There are two basic models of IDS: misuse IDS and anomaly IDS. Misuse systems detect intrusions by looking for activity that corresponds to the known signatures of intrusions or vulnerabilities. Anomaly systems detect intrusions by searching for an abnormal system activity. Most IDS commercial

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A P, Niharika. "Deep Learning Approach for Intrusion Detection System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem33646.

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The rapid growth of the Internet and communications has resulted in a huge increase in transmitted data. These data are coveted by attackers and they continuously create novel attacks to steal or corrupt these data. The growth of these attacks is an issue for the security of our systems and represents one of the biggest challenges for intrusion detection. An intrusion detection system (IDS) is tool that helps to detect intrusions by inspecting the network traffic. A system called an intrusion detection system (IDS) observes network traffic for malicious transactions and sends immediate alerts

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Qazi, Emad Ul Haq, Muhammad Hamza Faheem, and Tanveer Zia. "HDLNIDS: Hybrid Deep-Learning-Based Network Intrusion Detection System." Applied Sciences 13, no. 8 (2023): 4921. http://dx.doi.org/10.3390/app13084921.

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Attacks on networks are currently the most pressing issue confronting modern society. Network risks affect all networks, from small to large. An intrusion detection system must be present for detecting and mitigating hostile attacks inside networks. Machine Learning and Deep Learning are currently used in several sectors, particularly the security of information, to design efficient intrusion detection systems. These systems can quickly and accurately identify threats. However, because malicious threats emerge and evolve regularly, networks need an advanced security solution. Hence, building a

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Kaur, Harpreet. "NETWORK INTRUSION DETECTION AND PREVENTION ATTACKS." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 2, no. 3 (2012): 21–23. http://dx.doi.org/10.24297/ijct.v2i3a.2669.

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Intrusion detection is an important technology in business sector as well as an active area of research. It is an important tool for information security. A Network Intrusion Detection System is used to monitor networks for attacks or intrusions and report these intrusions to the administrator in order to take evasive action. Today computers are part of networked; distributed systems that may span multiple buildings sometimes located thousands of miles apart. The network of such a system is a pathway for communication between the computers in the distributed system. The network is also a pathw

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Ahmad, Iftikhar, Qazi Emad Ul Haq, Muhammad Imran, Madini O. Alassafi, and Rayed A. AlGhamdi. "An Efficient Network Intrusion Detection and Classification System." Mathematics 10, no. 3 (2022): 530. http://dx.doi.org/10.3390/math10030530.

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Intrusion detection in computer networks is of great importance because of its effects on the different communication and security domains. The detection of network intrusion is a challenge. Moreover, network intrusion detection remains a challenging task as a massive amount of data is required to train the state-of-the-art machine learning models to detect network intrusion threats. Many approaches have already been proposed recently on network intrusion detection. However, they face critical challenges owing to the continuous increase in new threats that current systems do not understand. Th

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Jyoti, Snehi, Bhandari Abhinav, Baggan Vidhu, and Snehi Ritu Manish. "Diverse Methods for Signature based Intrusion Detection Schemes Adopted." International Journal of Recent Technology and Engineering (IJRTE) 9, no. 2 (2020): 44–49. https://doi.org/10.35940/ijrte.A2791.079220.

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Intrusion Detection Systems (IDS) is used as a tool to detect intrusions on IT networks, providing support in network monitoring to identify and avoid possible attacks. Most such approaches adopt Signature-based methods for detecting attacks which include matching the input event to predefined database signatures. Signature based intrusion detection acts as an adaptable device security safeguard technology. This paper discusses various Signature-based Intrusion Detection Systems and their advantages; given a set of signatures and basic patterns that estimate the relative importance of each int

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Battini Sujatha, Et al. "An Efficient Fuzzy Based Multi Level Clustering Model Using Artificial Bee Colony For Intrusion Detection." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 11 (2023): 264–73. http://dx.doi.org/10.17762/ijritcc.v11i11.9390.

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Network security is becoming increasingly important as computer technology advances. One of the most important components in maintaining a secure network is an Intrusion Detection System (IDS). An IDS is a collection of tools used to detect and report network anomalies. Threats to computer networks are increasing at an alarming rate. As a result, it is critical to create and maintain a safe computing environment. For network security, researchers employ a range of technologies, including anomaly-based intrusion detection systems (AIDS). These anomaly-based detections face a major challenge in

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Li, Yimin, Dezhi Han, Mingming Cui, Fan Yuan, and Yachao Zhou. "RESNETCNN: An abnormal network traffic flows detection model." Computer Science and Information Systems, no. 00 (2023): 4. http://dx.doi.org/10.2298/csis221124004l.

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Intrusion detection is an important means to protect system security by detecting intrusions or intrusion attempts on the system through operational behaviors, security logs, and data audit. However, existing intrusion detection systems suffer from incomplete data feature extraction and low classification accuracy, which affects the intrusion detection effect. To this end, this paper proposes an intrusion detection model that fuses residual network(RESNET) and parallel cross-convolutional neural network, called RESNETCCN. RESNETCNN can efficiently learn various data stream features through the

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Mahendra S Dalvi. "Machine Learning Based Intrusion Detection System." Journal of Information Systems Engineering and Management 10, no. 36s (2025): 550–55. https://doi.org/10.52783/jisem.v10i36s.6528.

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System administrators use a network intrusion detection system (NIDS) to identify network security breaches inside their own firm. Building a clever and robust NIDS for irregular and capricious attacks, however, raises various challenges. One of the key subjects in NIDS research in recent years has been the application of machine learning understanding of strategies. This approach provides a network intrusion detection tool that effectively identifies several kinds of network intrusions, including Dos, U2R, R2L, Probe, and Normal.It employs twin support vector machines and decision trees. The

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Meliboev, Azizjon. "IOT NETWORK INTRUSION DETECTION SYSTEM USING MACHINE LEARNING TECHNIQUES." QO‘QON UNIVERSITETI XABARNOMASI 11 (June 30, 2024): 112–15. http://dx.doi.org/10.54613/ku.v11i11.972.

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The proliferation of Internet of Things (IoT) devices has transformed various industries by providing smart and automated solutions. However, the extensive connectivity and diverse nature of IoT devices have also introduced significant security challenges, particularly in terms of network intrusion. This paper explores the development and implementation of an Intrusion Detection System (IDS) for IoT networks using Machine learning techniques. The proposed IDS aims to detect and mitigate various cyber threats by analyzing network traffic and identifying anomalous patterns indicative of intrusio

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Sudhanshu, Sekhar Tripathy, and Behera Bichitrananda. "EVALUATION OF FUTURE PERSPECTIVES ON SNORT AND WIRESHARK AS TOOLS AND TECHNIQUES FOR INTRUSION DETECTION SYSTEM." Industrial Engineering Journal 53, no. 10 (2024): 18–40. https://doi.org/10.5281/zenodo.14213834.

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The increasing reliance on inter-organizational information exchange has raised significant concerns about the security of data and network infrastructures. Network monitoring plays a crucial role in mitigating these concerns, with tools like Wireshark and Snort forming the backbone of Intrusion Detection Systems (IDS). Initially developed as a packet inspection application, Wireshark is widely regarded for its user-friendly interface and intuitive packet-enhancement features, making it effective for classifying various types of network traffic. This research explores the practical application

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Priya, Ms Siva. "Intrusion Detection System Using Probabilistic Adaptive Learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 1222–25. http://dx.doi.org/10.22214/ijraset.2024.61565.

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Abstract: Machine learning and deep learning techniques are widely used to evaluate intrusion detection systems (IDS) capable of rapidly and automatically recognizing and classifying cyber-attacks on networks and hosts. However, when destructive attacks are becoming more extensive, more challenges develop, needing a comprehensive response. Numerous intrusion detection datasets are publicly accessible for further analysis by the cybersecurity research community. The Intrusion Detection System (IDS) is an effective tool utilized in cybersecurity systems to detect and identify intrusion attacks.

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Erlansari, Aan, Funny Farady Coastera, and Afief Husamudin. "Early Intrusion Detection System (IDS) using Snort and Telegram approach." SISFORMA 7, no. 1 (2020): 21. http://dx.doi.org/10.24167/sisforma.v7i1.2629.

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Computer network security is an important factor that must be considered. Guaranteed security can avoid losses caused by attacks on the network security system. The most common prevention against network attacks is to place an administrator, but problems will arise when the administrator is not supervising the network, so to overcome these problems a system called IDS (Intrusion Detection System) can detect suspicious activity on the network through automating the work functions of an administrator. Snort is one of the software that functions to find out the intrusion. Data packets that pass t

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Hasan, Mokhtar Mohammed, and Noor Adnan Ibraheem. "APPLYING ADAPTIVE FUZZY NEURAL ALGORITHM FOR INTRUSION DETECTION." Journal of Engineering 16, no. 01 (2010): 4488–509. http://dx.doi.org/10.31026/j.eng.2010.01.08.

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Many Network applications used as remote login have some ways for detecting the intruders which are classical ways applied by comparison of operations between login user interface and system stored information. The proposed system tried to detect the intrusions happened by the network intruders using new technique called Adaptive Fuzzy Neural Network which have the ability to detect the intrusions at the same time even if the number of users is large. The proposed system consists of two stages, the first stage is for monitoring all events that happen and analyzing them, and the second stage is

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Bhelkar,, Mr Sahil. "Network Intrusion Detection System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem31278.

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Network intrusion detection systems (NIDS) play a crucial role in safeguarding network infrastructures against unauthorized access and malicious activities. This abstract explores the fundamental concepts, methodologies, and challenges associated with NIDS. It delves into the various techniques employed by NIDS, ranging from signature-based detection to anomaly detection, and highlights the importance of real-time monitoring and analysis for timely threat detection and response. Additionally, the abstract discusses the evolving landscape of cyber threats and the need for continuous adaptation

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Afzal, Shehroz, and Jamil Asim. "Systematic Literature Review over IDPS, Classification and Application in its Different Areas." STATISTICS, COMPUTING AND INTERDISCIPLINARY RESEARCH 3, no. 2 (2021): 189–223. http://dx.doi.org/10.52700/scir.v3i2.58.

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Cyber-attacks are becoming more sophisticated and thereby presenting increasing challenges in accurately detecting intrusions. Failure to prevent the intrusions could degrade the credibility of security services, e.g. data confidentiality, integrity, and availability. Numerous intrusion detection methods have been proposed in the literature to tackle computer security threats, which can be broadly classified into Signature-based Intrusion Detection Systems (SIDS) and Anomaly-based Intrusion Detection Systems (AIDS). Network security is vital for any organization connected to the Internet. Rock

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Afzal, Shehroz, and Jamil Asim. "Systematic Literature Review over IDPS, Classification and Application in its Different Areas." STATISTICS, COMPUTING AND INTERDISCIPLINARY RESEARCH 3, no. 2 (2021): 189–223. http://dx.doi.org/10.52700/scir.v3i2.58.

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Cyber-attacks are becoming more sophisticated and thereby presenting increasing challenges in accurately detecting intrusions. Failure to prevent the intrusions could degrade the credibility of security services, e.g. data confidentiality, integrity, and availability. Numerous intrusion detection methods have been proposed in the literature to tackle computer security threats, which can be broadly classified into Signature-based Intrusion Detection Systems (SIDS) and Anomaly-based Intrusion Detection Systems (AIDS). Network security is vital for any organization connected to the Internet. Rock

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Lafta, Hameed. "Network Intrusion Detection Using Optimal Perception with Cuckoo Algorithm." Wasit Journal for Pure sciences 3, no. 1 (2024): 95–105. http://dx.doi.org/10.31185/wjps.326.

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ABSTRACT To safeguard computer networks from intruders, intrusion detection systems have been created. These systems operate in conjunction with firewalls and other security measures to guarantee the safety and efficiency of the computer system. An intrusion detection system is a tool designed to detect and pinpoint attacks and vulnerabilities within a network or computer system. It subsequently notifies the system administrator of them. The primary challenge with intrusion detection systems is enhancing their speed and precision in detecting intruders. This article explores a novel technique

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Lama, Amin, and Dr Preeti Savant. "A SURVEY ON NETWORK-BASED INTRUSION DETECTION SYSTEMS USING MACHINE LEARNING ALGORITHMS." International Journal of Engineering Applied Sciences and Technology 6, no. 9 (2022): 225–30. http://dx.doi.org/10.33564/ijeast.2022.v06i09.031.

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Network security is of central significance in the current information world. Due to the rapid increase of network-enabled devices, there is a significant risk of network intrusion more than ever. Hackers and intruders can successfully attack to cause the crash of the networks and web services by the unauthorized intrusion, which may cause a significant loss to an organization in terms of data and money. So, it is high time to create an intrusion detection system that can detect all types of intrusion. Due to the rapid growth and significant results of machine learning (ML) algorithms in sever

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Bhavini Ahir, Bhavini Ahir, Prachi Tambakhe, and Dr Kalpesh Lad Dr. Kalpesh Lad. "Open Source Intelligent Network Intrusion Detection System Analyzer." Indian Journal of Applied Research 2, no. 3 (2011): 84–87. http://dx.doi.org/10.15373/2249555x/dec2012/27.

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Hussein, Salam Allawi, Alyaa Abduljawad Mahmood, and Emaan Oudah Oraby. "Network Intrusion Detection System Using Ensemble Learning Approaches." Webology 18, SI05 (2021): 962–74. http://dx.doi.org/10.14704/web/v18si05/web18274.

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To mitigate modern network intruders in a rapidly growing and fast pattern changing network traffic data, single classifier is not sufficient. In this study Chi-Square feature selection technique is used to select the most important features of network traffic data, then AdaBoost, Random Forest (RF), and XGBoost ensemble classifiers were used to classify data based on binary-classes and multi-classes. The aim of this study is to improve detection rate accuracy for every individual attack types and all types of attacks, which will help us to identify attacks and particular category of attacks.

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Majeed, Dr Saad K., Dr Soukaena H. Hashem, and Ikhlas K. Gbashi. "Proposal to WNIDS Wireless Network Intrusion Detection System." International Journal of Scientific Research 2, no. 10 (2012): 4–8. http://dx.doi.org/10.15373/22778179/oct2013/29.

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Boskany, Najmadin Wahid. "Design of Alarm Based Network Intrusion Detection System." Journal of Zankoy Sulaimani - Part A 16, no. 2 (2014): 65–69. http://dx.doi.org/10.17656/jzs.10294.

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Liu, Gui Guo. "Intrusion Detection Systems." Applied Mechanics and Materials 596 (July 2014): 852–55. http://dx.doi.org/10.4028/www.scientific.net/amm.596.852.

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In the ear of information society, network security have become a very important issues. Intrusion is a behavior that tries to destroy confidentiality, data integrality, and data availability of network information. Intrusion detection systems are constructed as a software that automates the automatically detects possible intrusions. In this paper, we present the existing intrusion detection techniques in details including intrusion detection types, firewalls, etc.

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Fegade, Saurabh, Amey Bhadkamka, Kamlesh Karekar, Jaikishan Jeshnani, and Vinayak Kachare. "Network Intrusion Detection System Using C4.5 Algorithm." Journal of Communications Technology, Electronics and Computer Science 10 (March 1, 2017): 15. http://dx.doi.org/10.22385/jctecs.v10i0.139.

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There is a great concern about the security of computer these days. The number of attacks has increased in a great number in the last few years, intrusion detection is the main source of information assurance. While firewalls can provide some protection, they fail to provide protection fully and they even need to be complemented with an intrusion detection system (IDS). A newer approach for Intrusion detection is data mining techniques.IDS system can be developed using individual algorithms like neural networks, clustering, classification, etc. The result of these systems is good detection rat

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Vaishnavi, Vali Sai Jitha. "Advance Network Intrusion Detection System Using Deep Learning Techniques." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47507.

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Abstract :With the rapid increase in cyber threats, traditional intrusion detection systems (IDS) struggle to keep up with sophisticated attacks. This project aims to develop an Advanced Network Intrusion Detection System (NIDS) using Deep Learning techniques to detect and classify network intrusions effectively. The system processes real-time network traffic and classifies it as normal or malicious using deep learning models such as ML models. The dataset is preprocessed using feature engineering techniques like One-Hot Encoding and Min-Max Scaling to improve accuracy. The trained model is de

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Kumar, Abhijeet. "Enhancing Network Security with Tree-Based Machine Learning: A Study on Ensemble Intrusion Detection System Models." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 1417–22. https://doi.org/10.22214/ijraset.2025.67586.

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In the era of rapid technological advancement, cybersecurity has become a paramount concern, with the ever-growing threat of unauthorized access, data breaches, and malicious attacks on networks. In this paper, we proposed an advanced intrusion detection system (IDS) leveraging various machine learning ensemble learning techniques. Intrusion detection systems are critical for cybersecurity, identifying and mitigating unauthorized access and attacks on networks. By utilizing models such as Decision Tree, Random Forest, Gradient Boosting, and XGBoost, we aimed to enhance the accuracy and efficie

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Et. al., T. Sushma,. "A Review of the cluster based Mobile Adhoc Network Intrusion Detection System." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (2021): 2070–76. http://dx.doi.org/10.17762/turcomat.v12i2.1811.

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The Mobile Ad-hoc Network is decentralized and consisting of numerous different communication devices. Its distributed design and lack of infrastructure are the means of numerous network assaults. For personal computer users, companies, and the military, network security has become more important. Safety becomes a significant issue with the rise of the internet, and the past of security enables a better understanding of the evolution of security technology. Via the audit and monitoring phase, the implementation of Intrusion Detection Systems (IDS) in ad-hoc node securities was improved. This f

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Researcher. "AN APPLICATION OF LINEAR DISCRIMINANT ANALYSIS IN THE DESIGN OF INTRUSION DETECTION SYSTEM (LINEAR DISCRIMINANT ANALYSIS & INTRUSION DETECTION SYSTEM)." International Journal of Information Security (IJIS) 3, no. 2 (2024): 13–24. https://doi.org/10.5281/zenodo.14504686.

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<strong>Background and objective:</strong> Intrusion Detection Systems essentially involve processing of high dimensional network data using various multivariate statistical methods. In this work the Intrusion detection systems designed for network features and non-network features are compared for their efficiency. <strong>Materials and Methods: </strong> This current work has used Linear Discriminant Method to analyze the network data in the design of Intrusion Detection System by applying it to the network attacks from UNSW-NB15 intrusion detection data along with MITRE ATT&CK framework

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Sk, Mr Shafiulilah. "AI-Driven Network Intrusion Detection System." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 1481–86. https://doi.org/10.22214/ijraset.2025.67539.

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In the evolving landscape of network security, conventional Intrusion Detection Systems (IDS) often fall short in addressing sophisticated and novel cyber threats. It provides an advanced approach to Network Intrusion Detection by leveraging Generative Adversarial Networks (GANs) to enhance detection accuracy and adaptability. The proposed system integrates GANs to generate synthetic attack patterns and improve anomaly detection capabilities. By training a GAN with diverse network traffic data, our method not only detects known threats but also identifies previously unseen attack vectors with

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Verma, Anil, Enish Paneru, and Bishal Baaniya. "Anomaly-Based Network Intrusion Detection System." Journal of Lumbini Engineering College 4, no. 1 (2022): 38–42. http://dx.doi.org/10.3126/lecj.v4i1.49364.

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Network security has been a really hot topic since the inception of the internet in the early ’80s. With millions of people entrusting their life savings in the hands of an organization, it is really necessary to keep the network intruders out of the system. The most alarming thing is that - even today, many organizations are detecting these intrusions through manual labour. Many researchers have proven that these intrusions have a certain pattern i.e. they can be detected with an Artificial Intelligence (AI) based system with enough training which can prove to be a really an effective substit

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Azarudeen, K., Dasthageer Ghulam, G. Rakesh, Balaji Sathaiah, and Raj Vishal. "Intrusion Detection System Using Machine Learning by RNN Method." E3S Web of Conferences 491 (2024): 04012. http://dx.doi.org/10.1051/e3sconf/202449104012.

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As computer networks continue to grow, network intrusions become more frequent, advanced, and volatile, making it challenging to detect them. This has led to an increase in illegal intrusions that current security tools cannot handle. NIDS is currently available and most reliable ways to monitor network traffic, identify unauthorized usage, and detect malicious attacks. NIDS can provide better visibility of network activity and detect any evidence of attacks and malicious traffic. Recent research has shown that machine learning-based NIDS, particularly with deep learning, is more effective in

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A. M., Riyad, M. S. Irfan Ahmed, and R. L. Raheemaa Khan. "An adaptive distributed Intrusion detection system architecture using multi agents." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 6 (2019): 4951. http://dx.doi.org/10.11591/ijece.v9i6.pp4951-4960.

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Intrusion detection systems are used for monitoring the network data, analyze them and find the intrusions if any. The major issues with these systems are the time taken for analysis, transfer of bulk data from one part of the network to another, high false positives and adaptability to the future threats. These issues are addressed here by devising a framework for intrusion detection. Here, various types of co-operating agents are distributed in the network for monitoring, analyzing, detecting and reporting. Analysis and detection agents are the mobile agents which are the primary detection m

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Riyad, A. M., S. Irfan Ahmed M., and L. Raheemaa Khan R. "An adaptive distributed intrusion detection system architecture using multi agents." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 6 (2019): 4951–60. https://doi.org/10.11591/ijece.v9i6.pp4951-4960.

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Intrusion detection systems are used for monitoring the network data, analyze them and find the intrusions if any. The major issues with these systems are the time taken for analysis, transfer of bulk data from one part of the network to another, high false positives and adaptability to the future threats. These issues are addressed here by devising a framework for intrusion detection. Here, various types of co-operating agents are distributed in the network for monitoring, analyzing, detecting and reporting. Analysis and detection agents are the mobile agents which are the primary detection m

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Ghawade, Miss Manoshri A. "Study of Intrusion Detection System." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 788–92. http://dx.doi.org/10.22214/ijraset.2021.34935.

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An intrusion detection system (IDS) could be a device or software application that observes a network for malicious activity or policy violations. Any malicious activity or violation is often reported or collected centrally employing a security information and event management system. Some IDS’s are proficient of responding to detected intrusion upon discovery. These are classified as intrusion prevention systems (IPS). A system that analyzes incoming network traffic is thought as Network intrusion detection system (NIDS). A system that monitors important software files is understood as Host i

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Jeevaraj, Deepa, B. Karthik, T. Vijayan, and M. Sriram. "Feature Selection Model using Naive Bayes ML Algorithm for WSN Intrusion Detection System." International journal of electrical and computer engineering systems 14, no. 2 (2023): 179–85. http://dx.doi.org/10.32985/ijeces.14.2.7.

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Intrusion detection models using machine-learning algorithms are used for Intrusion prediction and prevention purposes. Wireless sensor network has a possibility of being attacked by various kinds of threats that will de-promote the performance of any network. These WSN are also affected by the sensor networks that send wrong information because of some environmental causes in- built disturbances misaligned management of the sensors in creating intrusion to the wireless sensor networks. Even though signified routing protocols cannot assure the required security in wireless sensor networks. The

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Zhang, Ruohao, Jean-Philippe Condomines, and Emmanuel Lochin. "A Multifractal Analysis and Machine Learning Based Intrusion Detection System with an Application in a UAS/RADAR System." Drones 6, no. 1 (2022): 21. http://dx.doi.org/10.3390/drones6010021.

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The rapid development of Internet of Things (IoT) technology, together with mobile network technology, has created a never-before-seen world of interconnection, evoking research on how to make it vaster, faster, and safer. To support the ongoing fight against the malicious misuse of networks, in this paper we propose a novel algorithm called AMDES (unmanned aerial system multifractal analysis intrusion detection system) for spoofing attack detection. This novel algorithm is based on both wavelet leader multifractal analysis (WLM) and machine learning (ML) principles. In earlier research on unm

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Deshpande, Apoorva. "A Review on Intrusion Detection System using Artificial Intelligence Approach." SMART MOVES JOURNAL IJOSCIENCE 4, no. 8 (2018): 6. http://dx.doi.org/10.24113/ijoscience.v4i8.153.

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Today, intrusion detection system using the neural network is an interested and considerable area for the research community. The computational intelligence systems are defined on the basis of the following parameters: fault tolerance and adaptation; adaptable the requirements of make a better intrusion detection model. In this paper, provide an overview of the research progress using computational intelligence to the problem of intrusion detection. The goal of this paper summarized and compared research contributions of Intrusion detection system using computational intelligence and neural ne

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Shrikant, Vanve* Prof. Sarita Patil. "OGEDIDS: OPPOSITIONAL GENETIC PROGRAMMING ENSEMBLE FOR DISTRIBUTED INTRUSION DETECTION SYSTEMS." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 7 (2016): 756–62. https://doi.org/10.5281/zenodo.57737.

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Due to the wide range application of internet and computer networks, the securing of information is indispensable one. In order to secure the information system more effectively, various distributed intrusion detection has been developed in the literature. In this paper, we utilize the oppositional genetic algorithm for Distributed Network Intrusion Detection utilizing the oppositional set based population selection mechanism. This system is mostly useful for detecting unauthorized & malicious attack in distributed network. Here, Oppositional genetic algorithm (OGA) is utilized in OGA ense

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Phadatare, Yash. "Network Intrusion Monitoring System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem43029.

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This web application helps to identify an attack or sense abnormal behaviour in the network and send an alert to the user and protect the user. When the user login into the portal he gets the information about the network’s accuracy, f1-score, precision. This helps the user to detect how safe his network is for the system. Network intrusion detection systems (NIMS) play a critical role in safeguarding computer networks against various cyber threats. Traditional rule-based NIMS often struggle to keep pace with the evolving nature of attacks and the increasing complexity of network environments.

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Khazane, Hassan, Mohammed Ridouani, Fatima Salahdine, and Naima Kaabouch. "IoT Network Security based on Intrusion Detection System using Stacked Ensemble." WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS 22 (June 25, 2025): 466–73. https://doi.org/10.37394/23209.2025.22.38.

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The rapid evolution of IoT networks has led to an increasing number of devices connecting to the internet, exposing them to various cyber threats. Detecting intrusions in IoT environments is essential but challenging. Network Intrusion Detection Systems are vital in analyzing network traffic to differentiate normal and malicious activities without compromising security. However, the abundance of benign traffic complicates accurate detection. To overcome this challenge, we propose an Ensemble-based Network Intrusion Detection Systems framework, where five Machine Learning classifiers are combin

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Sharma, Himanshu, Prabhat Kumar, and Kavita Sharma. "Recurrent Neural Network based Incremental model for Intrusion Detection System in IoT." Scalable Computing: Practice and Experience 25, no. 5 (2024): 3778–95. http://dx.doi.org/10.12694/scpe.v25i5.3004.

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The security of Internet of Things (IoT) networks has become a integral problem in view of the exponential growth of IoT devices. Intrusion detection and prevention is an approach ,used to identify, analyze, and block cyber threats to protect IoT from unauthorized access or attacks. This paper introduces an adaptive and incremental intrusion detection and prevention system based on RNNs, to the ever changing field of IoT security. IoT networks require advanced intrusion detection systems that can identify emerging threats because of their various and dynamic data sources. The complexity of IoT

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Kumar, K. Praveen. "Network Based Intrusion Detection System Using Machine Learning." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48304.

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As cyber threats continue to evolve in complexity and frequency, conventional intrusion detection systems (IDS) often fail to detect advanced and novel attacks. This project introduces an intelligent Network Intrusion Detection System (NIDS) leveraging Deep Learning to efficiently identify and categorize network intrusions. It analyzes live network traffic and determines whether it is legitimate or malicious using advanced machine learning models. Prior to training, the dataset undergoes preprocessing with techniques like One-Hot Encoding and Min-Max Scaling to enhance model performance. The f

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Ritu Rani. "Layered Intrusion Detection System for Wireless Network." Journal of Information Systems Engineering and Management 10, no. 53s (2025): 764–73. https://doi.org/10.52783/jisem.v10i53s.10973.

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Security in networks has become a more sophisticated problem since that such networks are open and lack infrastructure. In this paper, we describe the primary safety issues for network and data link layer protection. These levels' security needs are identified and requirements for design are established to create networks that are safe from malicious attacks. Maintaining privacy, authenticity, integrity, and non-repudiation in networked environments becomes difficult when a network architecture's security is not well planned from the beginning. This paper discusses the various attack types and

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Shinde, Prof Ms S. P. "AI-Based Network Intrusion Detection System." International Journal for Research in Applied Science and Engineering Technology 12, no. 2 (2024): 1586–93. http://dx.doi.org/10.22214/ijraset.2024.58620.

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Abstract: This report discusses the research done on the chosen topic, which is Developing an AI-based Network Intrusion Detection System using ML and DL algorithms. Recently we have seen so much progress in Internet and communication technologies it is not just connecting computer networks and people but it is also connecting devices involving Big Data. It has so many benefits in each field which are crucial in today's world like education, health, digital transactions, traveling, and anything we can think of. With so many benefits it comes with its negative effects like cyber-attacks which c

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