[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121997-en":3,"doc-seo-121997-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121997,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Intrusion Detection Using Machine Learning and Deep Learning Models on Cyber Security Attacks","Network intrusion detection is a core defensive function in cybersecurity, especially as rule-based techniques struggle to keep pace with evolving attack vectors and increasingly diverse threats. Machine learning and deep learning approaches can process large-scale network traffic, learning patterns and flagging anomalies. This paper reviews state-of-the-art methods, evaluating strengths and weaknesses across attack scenarios and network environments, and discusses future directions including transfer learning, adversarial robustness, and ensemble learning. Reported experimental results highlight high accuracies for CNN/LSTM and traditional classifiers under specific datasets, supporting their role in reducing risk and protecting critical assets.","VFAST Transactions on Software Engineering Volume 12, Issue 2, 2024  \nVFAST Transactions on Software Engineering  \n[https://vfast.org/journals/index.php/VTSE@ 2024](https://vfast.org/journals/index.php/VTSE@ 2024), ISSN(e): 2309-3978, ISSN(p): 2411-6246  \nVolume 12, Number 2, April-June 2024 pp:95-113  \nKeywords: Machine Learning Algorithms, Deep Learning Algorithms, Network Intrusion Detection, Cyber Security Attacks, Countermeasures Cyber Attacks.  \nJournal Info:  \nSubmitted: April 20, 2024  \nAccepted: May 25, 2024  \nPublished: June 24, 2024  \nIntrusion Detection Using Machine Learning and Deep Learning Models on Cyber Security Attacks  \nMuhammad Asad Iftikhar  \n2  \n,  \nKhalid Hameed  \n2  \n1 Department of Computer Science Iqra National University Peshawar,Pakistan; 2 CECOS University of IT and Emerging Sciences  \nAbstract  \nTo detect and stop harmful activity in computer networks, network intrusion detection is an essential part of cybersecurity defensive systems. It is becoming more difficult for traditional rule-based techniques to identify new attack vectors in the face of the increasing complexity and diversity of cyber threats. Machine learning (ML) and deep learning (DL) models can analyze vast amounts of network traffic data and automatically identify patterns and anomalies, there has been a surge in interest in using these models for network intrusion detection. This paper examines the approaches, algorithms, and realworld applications of machine learning and deep learning techniques for network intrusion detection in order to present a thorough review of the state-of-the-art in countering cyber threats. We assess ML and DL-based intrusion detection systems’ effectiveness, strengths, and weaknesses in a range of attack scenarios and network environments by synthesizing current literature and empirical research. Additionally, we talk about new developments, obstacles, and paths forward in the areas of transfer learning, adversarial robustness, and ensemble learning. The understanding gained from this investigation clarifies the potential of ML and DL models in strengthening defenses against changing cyber threats, reducing risks, and protecting vital assets. In deep learning autoencode accuracy 68% less than other models. The performance of the CNN and LSTM algorithm is impressive and outperformed with 100% accuracy on cyber security attacks datasets. Machine learning algorithm accuracy rate of SVM and KNN 100% while logistic regression accuracy is 99% GNB accuracy 80% with training data of the models. The overall models perforamance deep learning increadible accuracy with 100% on the training and testing data.  \n*Correspondence author email [address:](address: Waqas.ahmad@inu.edu.pk)[ Waqas.ahmad@inu.edu.pk](address: Waqas.ahmad@inu.edu.pk), onlinesoftteach@gmail. com  \nDOI: 10.21015/vtse.v12i2 .1817  \nThis work is licensed under a Creative Commons Attribution 3.0 License.   \nVFAST Transactions on Software Engineering Volume 12, Issue 2, 2024  \n1 Introduction  \nNetwork attacks pose cybersecurity risks, necessitating intrusion detection systems. Advanced methods like machine learning and deep learning are being explored to address the limitations of traditional rule-based approaches in handling dynamic cyber threats [1] .  \nMachine learning and deep learning models improve intrusion detection by learning patterns from network traffic data, detecting sophisticated attack vectors and constantly adapting to new threats, making them valuable cybersecurity tools [2] . This study reviews network intrusion detection using machine learning and deep learning models, discussing their merits, limitations, and practical consequences, discussing practical difficulties, and offering solutions [3] .  \nHowever, the machine and deep learning technologies critically examine the effectiveness of ML and DL-based NIDS in various attack scenarios and network topologies, discussing new trends for enhanced intrusion detection systems [4] . The signifi","cbCaitZoNpy9DkW1","https://ap.wps.com/l/cbCaitZoNpy9DkW1","pdf",551256,1,19,"English","en",105,"# Introduction\n## Background and motivation\n## Related work and key directions","[{\"question\":\"Why is network intrusion detection important in cybersecurity?\",\"answer\":\"Network intrusion detection helps identify and stop harmful activities, acting as a key second line of defense alongside controls such as access control, authentication, and encryption.\"},{\"question\":\"How do machine learning and deep learning improve intrusion detection compared with rule-based methods?\",\"answer\":\"They learn from network traffic to detect patterns, sophisticated attack vectors, and anomalies, enabling adaptation to new and dynamic cyber threats.\"},{\"question\":\"What topics are highlighted for future development in ML/DL-based intrusion detection?\",\"answer\":\"The paper discusses developments and challenges in transfer learning, adversarial robustness, and ensemble learning, along with obstacles and paths forward for stronger defenses.\"}]","Intrusion Detection Using Machine Learning and Deep Learning Models on Cyber Security Attacks | 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is network intrusion detection important in cybersecurity?","Question",{"text":75,"@type":76},"Network intrusion detection helps identify and stop harmful activities, acting as a key second line of defense alongside controls such as access control, authentication, and encryption.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do machine learning and deep learning improve intrusion detection compared with rule-based methods?",{"text":80,"@type":76},"They learn from network traffic to detect patterns, sophisticated attack vectors, and anomalies, enabling adaptation to new and dynamic cyber threats.",{"name":82,"@type":73,"acceptedAnswer":83},"What topics are highlighted for future development in ML/DL-based intrusion detection?",{"text":84,"@type":76},"The paper discusses developments and challenges in transfer learning, adversarial robustness, and ensemble learning, along with obstacles and paths forward for stronger 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