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Improving Accuracy in Network Intrusion Detection via Machine Learning Algorithms: An In-Depth Examination

Improving Accuracy in Network Intrusion Detection via Machin...

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2024 international conference on distributed systems, computer networks and Cybersecurity, ICDSCNC 2024

作者： Suriya Prakash, J. Trishlaa, S. Soniya, R. Lakshmanan, V. Kiran, S. Department of Cse Ramnagara India Rajarajeshwari College of Engineering Department of Artificial Intelligence and Machine Learning Bengaluru India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India Nitte Meenakshi Institute of Technology Department of Mathematics Bengaluru India

ISBN: (纸本)9798350375442

Intrusion detection is essential for securing computer networks by monitoring and analyzing activity to detect and respond to unauthorized access and malicious behavior. By examining network traffic and system logs, intrusion detection systems (IDS) help protect data integrity and availability, enabling swift responses to potential security incidents. In an ever-evolving cyber threat landscape, IDS plays a critical role in maintaining network security. This research investigates the performance of various machine learning algorithms for classification tasks using a dataset from Kyoto collected on December 31st, 2015. The algorithms explored include Naive Bayes, K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), AdaBoost, and CatBoost. Training and testing the model with different data splits (80/20, 70/30, 60/40) revealed that AdaBoost, LGBM, XGBoost, Decision Tree, Gradient Boosting, and Random Forest consistently delivered the highest accuracy. The study concludes that selecting the appropriate algorithms and data splits is crucial for enhancing IDS effectiveness, providing valuable insights for future research. Explore the code behind this study at https://***/Trishlaa07/Kyoto_2015_Dec_day31 © 2024 IEEE.

关键词： Network intrusion

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Enhanced Hybrid Deep Learning Model for distributed Denial of Service Attack Detection in IoT Environments

Enhanced Hybrid Deep Learning Model for Distributed Denial o...

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2024 international conference on distributed systems, computer networks and Cybersecurity, ICDSCNC 2024

作者： Sharmila Kumari, N. Vimala, H.S. Pruthvi, C.N. Shreyas, J. Uvce Dept. of Cse Bengaluru India Dayananda Sagar College of Engineering Dept. of Cse Bengaluru India Manipal Academy of Higher Education Dept. of Information Technology Manipal Bengaluru India

ISBN: (纸本)9798350375442

security concerns have grown significantly with the proliferation of Internet of Things (IoT) devices, particularly in light of distributed Denial of Service (DDoS) attacks. Network resources may be overloaded by these assaults, leading to significant disruptions, data leaks, and outages. Studies have demonstrated that a combined deep learning strategy utilizing Convolutional Neural networks (CNN) and Long Short-Term Memory (LSTM) networks may successfully identify these DDoS assaults in Internet of Things environments. This study aims to make the hybrid model better by adding new techniques like feature selection, ensemble learning, and an improved attention mechanism. We thoroughly test the upgraded model against the original hybrid model and other traditional machine learning methods using different datasets. The results show that the improved model boosts detection accuracy, precision, recall, and F1-score, making it a stronger tool for spotting DDoS attacks in IoT networks. By using CIC-DDoS2019 dataset, our model yields results with an accuracy of 99%, precision, recall and F1 score of 98%. © 2024 IEEE.

关键词： Convolutional neural networks

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Detecting Cyberattacks to Federated Learning on Software-Defined networks 15th

Detecting Cyberattacks to Federated Learning on Software-Def...

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15th international conference on Management of Digital Ecosystems (MEDES)

作者： Babbar, Himanshi Rani, Shalli Singh, Aman Gianini, Gabriele Chitkara Univ Chitkara Univ Inst Engn & Technol Rajpura 140401 Punjab India Univ Europea Atlantico Santander 39011 Spain Univ Int Iberoamer Arecibo PR 00613 USA Univ Int Iberoamer Campeche 24560 Mexico Univ Milan Via Celoria 18 I-20133 Milan Italy

ISBN: (纸本)9783031516429;9783031516436

Federated learning is a distributed machine-learning technique that enables multiple devices to learn a shared model while keeping their local data private. The approach poses security challenges, such as model integrity, that must be addressed to ensure the reliability of the learned models. In this context, software-defined networking (SDN) can play a crucial role in improving the security of federated learning systems;indeed, it can provide centralized control and management of network resources, enforcement of security policies, and detection and mitigation of network-level threats. The integration of SDN with federated learning can help achieve a secure and efficient distributed learning environment. In this paper, an architecture is proposed to detect attacks on Federated Learning using SDN;furthermore, the machine learning model is deployed on a number of devices for training. The simulation results are carried out using the N-BaIoT dataset and training models such as Random Forest achieves 99.6%, Decision Tree achieves 99.8%, and K-Nearest Neighbor achieves 99.3% with 20 features.

关键词： Software Defined networks Federated Learning RSA Cyber security NBaIoT

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Federated Learning-Based Solution for DDoS Detection in SDN

Federated Learning-Based Solution for DDoS Detection in SDN

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international conference on Computing, Networking and Communications (ICNC)

作者： Mateus, Jovita Zodi, Guy-Alain Lusilao Bagula, Antoine UWC Dept Comp Sci Cape Town South Africa NUST Dept Comp Sci Windhoek Namibia

ISBN: (纸本)9798350371000;9798350370997

One major threat to Software Defined Network (SDN) environments and other computing systems is distributed Denial of Service (DDoS) attacks. For the longest time, conventional Machine Learning (ML) techniques have been used for DDoS detection, but they have limitations because of their centralised training requirements, which can lead to excessive bandwidth utilization, increased latency, and server overhead. Moreover, end-user data privacy and confidentiality are jeopardised by transferring the data to a central server. We present Federated Learning (FL) as a way to counteract DDoS attacks in SDN. FL reduces the need for centralised servers and lessens the limitations of conventional ML techniques by enabling decentralised training of ML models on distributed devices. FL lowers the possibility of data breaches and protects the privacy of sensitive data by training models locally on devices. We used FL to train three classifiers: Deep Neural networks (DNN), Convolutional Neural networks (CNN), and Long Short-Term Memory (LSTM) to classify three classes of DDoS attacks, namely: UDP Flood, TCP SYN, and DNS Flood. The results demonstrate how well our FL models perform and how they can replace centralized and conventional methods for identifying DDoS attacks in SDN environments while protecting the privacy of users.

关键词： DDoS Federated Learning Machine Learning Deep Learning Software Defined Networking security

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Safeguarding E Commerce and Preventing Financial Fraud with AdaBoost CNN Cybersecurity Approach

Safeguarding E Commerce and Preventing Financial Fraud with ...

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2024 international conference on distributed systems, computer networks and Cybersecurity, ICDSCNC 2024

作者： Malathi, V. Shammi, L. Unni, Manu Vasudevan Babulal Nadaf, Akabarsaheb Devi, S. Rukmani Thangam, E. Dr. N.G.P Arts and Science College Department of Computer Science with Cyber Security Coimbatore India East Point College of Engineering and Technology Deptartment of Computer Science Bengaluru India St. Claret College Autonomous Department of Management Bangalore India Abhijit Kadam Institute of Management Ame Social Sciences Solapur India Saveetha College of Liberal Arts and Sciences Simats Deemed to Be University Department of Computer Science Chennai India Ramco Institute of Technology Department of Eee Rajapalayam India

ISBN: (纸本)9798350375442

Examining the cyber security risks faced by companies and e-commerce platforms is the focus of this research. People in the corporate world and those in academia are curious about the technology applications of e-commerce. There are now possibilities that were before unimaginable for both businesses and consumers. On the other hand, there were certain issues with its arrival, chief among them being cyber security. Among the many forms of cybercrime, this study focuses on social engineering, denial-of-service attacks, malware, and data breaches. This proposed approach consists of three phases, which are feature extraction, model training, and data preprocessing. Addressing missing values, normalizing features, and eliminating noise are all part of preprocessing, which aims to clean and organize raw data. When dealing with multivariate data, principal component analysis (PCA) is a common tool for feature extraction. Throughout the training process, we employed an AdaBoost-CNN. With an average accuracy of 90.65%, this innovative technique surpasses CNN and AdaBoost © 2024 IEEE.

关键词： Cyber attacks

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Fortifying Network security via Intelligent Traffic Analysis with Hybrid IDS

Fortifying Network Security via Intelligent Traffic Analysis...

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2024 international conference on distributed systems, computer networks and Cybersecurity, ICDSCNC 2024

作者： Afreen, Syed Kranthi, S. Raasheed, Abdul Velagapudi Ramakrishna Siddhartha Engineering College Dept. of Information Technology Vijayawada India

ISBN: (纸本)9798350375442

With growth in technology, hacking and cyberattacks have increased hence the importance of network security. They also do not cease to invent new ways of penetrating the network, hence the need to install measures such as the IDS. This paper deals with improvement of IDS by combining signature based and anomaly based detection on packet data in Zeek (previously known as Bro). Autoencoder method focuses on the finding of intrusion in the network traffic while the signatures based method on the detection of known threat by matching the traffic with the existing signatures. This two-pronged approach is expected to enhance the chances of accurate detection while at the same time mitigating on the possibilities of having false alarms. The experiments showed that the average percent of accuracy to be nearly 88%, which proved that the system is capable of detecting both established and novel threats. The results showed that the anomaly-based method could successfully identify new threats with minimum number of false alarms and the signature based approach could locate the known threats perfectly. Altogether, these methods add more layers of protection to the network against today's ever-changing threats. © 2024 IEEE.

关键词： Network intrusion

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Next-Gen SDN security: Quantum Keying Meets AI Analytics

Next-Gen SDN Security: Quantum Keying Meets AI Analytics

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2024 international conference on distributed systems, computer networks and Cybersecurity, ICDSCNC 2024

作者： Virushabadoss, S. Anithaashri, T.P. Nivedha, M. Krishnan, S. Hari Karuppannan, Anand Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Dept. of Computer Science and Engineering Chennai India Arasu Engineering College Department of Electronics and Communication and Engineering Kumbakonam India Sanskrithi School of Engineering Department of Electronics and Communication and Engineering Puttaparthi India Gnanamani College of Technology Department of Electronics and Communication and Engineering Namakkal India

ISBN: (纸本)9798350375442

This paper suggests a complete security architecture that blends quantum key distribution (QKD) with cutting-edge machine learning methods to improve the Software-Defined Networking (SDN) control plane. By creating secure channels of communication through QKD, the framework ensures integrity and secrecy of control messages between the SDN controller and network devices. Furthermore, network traffic is continuously monitored and analyzed using machine learning techniques, enabling the detection of unusual behaviors that might be warning signs of upcoming security problems. Tests utilizing Mininet as the network interface and Open Daylight as the SDN controller proved the efficacy of the suggested method. The outcomes revealed a minimal computational overhead, exceptional detection accuracy up to 98.1%, and low false positive rates, substantiating the system's ability to effectively manage varied network conditions and traffic volumes. These results underscore the potential of integrating QKD with robust machine learning strategies to secure the SDN control plane against sophisticated threats. © 2024 IEEE.

关键词： Quantum cryptography

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AI based security in computer vision and machine learning algorithms for hand sign recognition 3

AI based Security in computer vision and machine learning al...

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3rd IEEE international conference on Blockchain and distributed systems security, ICBDS 2024

作者： Thorat, Nilesh N. Kulal, Nilesh M. Baburao, Khedekar Vilas Hirve, Sumit Arun Kolekar, Sachin Sangore, Rohidas Mit Art Design and Technology University Department of Computer Science and Engineering Pune India

ISBN: (纸本)9798350354348

In order to facilitate intuitive control and communication across a variety of areas, hand gesture recognition is an essential component of human-computer interaction systems. In this paper, a real-time hand gesture recognition system developed with the MediaPipe library, and convolutional neural networks (CNNs) is presented. The system utilizes a CNN model provided by MediaPipe for accurate detection of hand landmarks in camera feed images. Through extensive preprocessing and feature extraction, hand gestures, including finger gestures and palm orientations, are classified in real-time. The system offers dynamic selection between two modes: logging key points and logging point history, facilitating data collection for training or analysis *** features of the system include bounding rectangle calculation, landmark visualization, and gesture classification, providing users with immediate feedback on detected hand gestures. The modular design and parameter configurability make the system versatile for a wide range of applications, from interactive interfaces to sign language interpretation. The outcomes of the experiments showcase the practicality and efficacy of the suggested method, indicating that it has the potential to improve the experiences of people interacting with computers. Incorporating AI security measures, the system can significantly enhance security protocols in various applications. For instance, it can be used for gesture-based authentication, where specific hand gestures act as passwords to grant access to secure systems. This method can be further secured by combining it with traditional authentication techniques, creating a robust multi-factor authentication framework. Additionally, the real-time detection capability allows for immediate response to unauthorized access attempts, triggering alerts or lockdown procedures when unfamiliar gestures are detected. © 2024 IEEE.

关键词： Convolutional neural networks

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Adaptive Defense Mechanisms Against Zero-Day Attacks in Wireless Sensor networks

Adaptive Defense Mechanisms Against Zero-Day Attacks in Wire...

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2024 international conference on distributed systems, computer networks and Cybersecurity, ICDSCNC 2024

作者： Chaturvedi, Abhay Kukreti, Sanjeev Durga Bhavani, A. Nizampatnam, Venkata Naga Ravi Kiran Kaur, Davinder Natrayan, L. Gla University Department of Electronics & Communication Engineering Chaumuhan India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Bms Institute of Technology and Management Department of Computer Science and Engineering Bengaluru India Expert Network Security Engineer Acxiom Llc Department of Computer Networks AustinTX United States Symbiosis Law School Department of Law Pune India Saveetha School of Engineering Simats Department of Mechanical Engineering Kuthambakkam India

ISBN: (纸本)9798350375442

Important fields including healthcare, environmental monitoring, and military operations are seeing a rise in the use of wireless sensor networks (WSNs). But they are susceptible to complex cyber dangers, especially zero-day assaults, due to their dispersed nature and lack of resources. In order to protect WSNs against zero-day attacks, this research introduces an Adaptive Defense Mechanism (ADM). A dynamic response system that can adjust to changing attack patterns in real-time is integrated with machine learning-based anomaly detection in the proposed ADM. A hybrid model integrating Support Vector Machines (SVM) and Long Short-Term Memory (LSTM) networks is used by the anomaly detection module. It achieves a detection accuracy of 94.7% on simulated zero-day assault scenarios. In comparison to static defensive systems, the ADM's adaptive response mechanism, which is based on reinforcement learning, dynamically modifies the network's security regulations, resulting in a 23.5% reduction in false positives. Results from testing the proposed ADM in a simulated WSN setting show that it can greatly improve the network's ability to withstand zero-day assaults. With assaults present, the total network throughput increased by 15% and the average detection time dropped to 0.35 seconds. Based on these findings, the ADM provides a strong and effective defensive technique, guaranteeing the safety and dependability of WSNs in mission-critical applications. © 2024 IEEE.

关键词： Zero-day attack

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Navigating IoT security Assessment: Current Methods, Challenges and Future Directions 14

Navigating IoT Security Assessment: Current Methods, Challen...

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14th international conference on Advanced computer Information Technologies, ACIT 2024

作者： Abdulhamid, Alhassan Kabir, Sohag Ghafir, Ibrahim Lei, Ci University of Bradford Faculty of Engineering and Digital Technologies Bradford United Kingdom

ISBN: (纸本)9798350350036

Rapid growth of Internet of Things (IoT) applications h as significantly transformed h uman interaction with technology, providing unprecedented convenience and connectivity in our daily lives. Securing a complex network of interconnected IoT devices poses significant c hallenges. T his r equires a continuous proactive security assessments of the systems, considering their interconnected nature, distributed networks, and their crucial role in our health, safety, and the environment. It is essential to comprehensively evaluate IoT systems' security features to comprehend and successfully address security risks. This arti-cle offers an in-depth exploration of quantifying IoT security, examining some of the existing assessment models, associated challenges in security quantification, a nd p otential a venues for advancement. The principal contribution of this article lies in providing an overview of the current state of IoT security quantification a nalysis, d elineating p rominent c hallenges, and proposing prospects for addressing them. © 2024 IEEE.

关键词： cybersecurity quantification dependability Internet of Things security assessment

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