Search Results - (( pattern detection service algorithm ) OR ( java code classification algorithm ))

Object-Oriented Programming semantics representation utilizing agents by Mohd Aris, Teh Noranis

“…A formal algorithm that can be applied to any two related Java-based source codes examples is invented to generate the semantics of these source codes. …”
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Development Of Machine Learning User Interface For Pump Diagnostics by Lee, Zhao Yang

“…Build up a user interface by using Visual Studio Code (VSC) to run the coding of Cascading Style Sheet (CSS), Hyper Text Markup Language (HTML) and JavaScript (JS) as a webpage and connect to Azure Machine Learning Model and this will allow the user from using the model from a webpage when they have active internet with any devices.…”
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BIOLOGICAL INSPIRED INTRUSION PREVENTION AND SELF-HEALING SYSTEM FOR CRITICAL SERVICES NETWORK by MOHAMED AHMED ELSHEIK, MUNA ELSADIG

“…This thesis presents new intrusion prevention and self-healing system (SH) for critical services network security. The design features of the proposed system are inspired by the human immune system, integrated with pattern recognition nonlinear classification algorithm and machine learning. …”
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Hybrid Classification Algorithm For Denial Of Service Attack Detection Using Rough Set Theory And Artificial Immune by Fatin Norsyafawati, Mohd Sabri

Anomaly detection of denial-of-service network traffic attacks using autoencoders and isolation forest by Ghulam Hussain, Muhammad Thaqif, Shafeeq Lone, Aman, Maspo, Nur-Adib, Attarbashi, Zainab

“…This paper presents an unsupervised network-based anomaly detection framework that integrates deep autoencoders with the Isolation Forest algorithm. …”
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DeepIoT.IDS: Hybrid deep learning for enhancing IoT network intrusion detection by A. Mostafa, Salama, Al-Azzawi, Ziadoon Kamil Maseer, Bahaman, Nazrulazhar, Yusof, Robiah, Musa, Omar, Al-rimy, Bander Ali Saleh

“…Recently, researchers have suggested deep learning (DL) algorithms to define intrusion features through training empirical data and learning anomaly patterns of attacks. …”
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Hybrid weight deep belief network algorithm for anomaly-based intrusion detection system by Maseer, Ziadoon Kamil

“…Recently, researchers suggested a deep belief network (DBN) algorithm to construct and build a network intrusion detection system (NIDS) for detecting attacks that have not been seen before. …”
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Face detection in image sequence / Noor Diana Abdullah by Abdullah, Noor Diana

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Adaptive feature selection for denial of services (DoS) attack by Yusof, Ahmad Riza'ain, Udzir, Nur Izura, Selamat, Ali, Hamdan, Hazlina, Abdullah @ Selimun, Mohd Taufik

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Anomaly detection in network traffic using machine learning by Amir Muhammad Hafiz, Othman, Mohd Faizal, Ab Razak, Mohd Izham, Mohd Jaya, Nurul Azma, Abdullah, Alanda, Alde

“…Five (5) performance metrics, which are accuracy, precision, recall, and f-measure, are utilized to assess the result of each algorithm. The results highlight that the KNN algorithm achieves the highest accuracy, at 97%, while the ID3 algorithm produces a balanced trade-off between performance and efficiency. …”
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An enhanced android botnet detection approach using feature refinement by Anwar, Shahid

“…The obtained results show that by using the additional features the detection accuracy improved. The experimental evaluation based on real-world benchmark datasets shows that the selected unique patterns can achieve high detection accuracy with low false positive rate. …”
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Hybrid of rough set theory and Artificial Immune Recognition System as a solution to decrease false alarm rate in intrusion detection system by Sabri F.N.M., Norwawi N.M., Seman K.

ios Mobile Malware Deep Analysis And Classification For Social Media And Online Banking Exploitation by Muhammad ‘Afif Bin Husainiamer

SYN Flood detection via machine learning / Muhammad Muhaimin Aiman Mazlan by Mazlan, Muhammad Muhaimin Aiman

“…Recently, one the most popular attack is denial of service (DoS) that attempt to be malicious pattern to compromise a server or a network resource. …”
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Bibliometric analysis of AI-driven FinTech revolution: mapping global trends, thematic evolution, and future directions by Magli, Amirah Shazana, Sabri, Mohamad Fazli, Hazudin, Siti Fahazarina, Law, Siong Hook, Janani, M., Najam, Usama, Shahabudin, Sharifah Muhairah

“…Using the PRISMA methodology, 978 articles from the Web of Science (WoS) database were analysed to identify research trends, collaboration patterns, and citation networks. Results show an immersive publication growth rate of 26.84, indicating rising academic interest in AI-driven FinTech, with global collaboration accounting for 38.4, as supported by an increase in international co-authorship in areas such as robo-advisory services and fraud detection. …”
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Energy Efficient High-Performance Computing Aware Proactive Dynamic Virtual Machine Consolidation Technique in Cloud Computing by Rukshanda, Kamran

“…The proposed approach, Energy-Aware Multi-Dimensional Online Bin Packing (EAMDOBP), was tested against Power-aware best fit decreasing algorithm (PABFD), Modified Worst Fit decreasing algorithm (MWFD) and Hybrid Local Regression Host Overload Detection (HLRHOD). …”
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Data Encryption-Enabled Cloud Cost Optimization and Energy Efficiency-Based Border Security Model by Alomari M.F., Mahmoud M.A., Yusoff Y.B., Gharaei N., Abdalla R.A., Gunasekaran S.S.

Q-Learning-based detection of IPv6 intrusions: a behavioral and performance study by Daru, April Firman, Hirzan, Alauddin Maulana, Mahmod Attar Bashi, Zainab Senan, Fanani, Fajriannoor

“…This problem can significantly affect local network performance or completely deny service to targeted servers. While numerous studies have proposed intrusion detection systems based on supervised learning models, a critical limitation persists. …”
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An early warning system for students at risk using supervised machine learning by Yam, Zheng Hong, Mohd Norshahriel, Abd Rani, Nabilah Filzah, Mohd Radzuan, Lim, Huay Yen, Sarasvathi, Nagalingam

“…Besides, this study also focuses on the Decision Tree, Random Forest, and XGBoost models in the system. The system will detect or forecast symptoms of dropout or potential dangers ahead of time, allowing educational institutions to anticipate problems and provide adequate educational services through appropriate intervention and response. …”
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