Search Results - (( java implementation during algorithm ) OR ( framework implementation detection algorithm ))

An enhanced classification framework for intrusions detection system using intelligent exoplanet atmospheric retrieval algorithm by Slamet, ., Izzeldin Ibrahim, Mohamed Abdelaziz

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Enhanced Community Detection Based On Cross Time For Higher Visibility In Supply Chain: A Six-Steps Model Framework by Abal Abas, Zuraida, Mohd Zaki, Nurul Hafizah, Asmai, Siti Azirah, Abdul Rahman, Ahmad Fadzli Nizam, Zainal Abidin, Zaheera

“…The proposed framework utilizes the Cross-Time approach for enhancing temporal community detection algorithm. …”
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Real-time anomaly detection using clustering in big data technologies / Riyaz Ahamed Ariyaluran Habeeb by Riyaz Ahamed , Ariyaluran Habeeb

“…Based on the outcome of the analysis, this research proposed a novel framework namely real-time anomaly detection based on big data technologies (RTADBDT), along with supporting implementation algorithms. …”
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An enhanced classification framework for intrusions detection system using intelligent exoplanet atmospheric retrieval by Slamet, Slamet, Izzeldin, Ibrahim Mohamed Abdelaziz

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Pairwise testing tools based on hill climbing algorithm (PTCA) by Lim, Seng Kee

“…The actual implementation of the algorithm which is in Java programming language, the program is implemented on Net Bean 7.0.1. …”
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A conceptual multi-agent framework using ant colony optimization and fuzzy algorithms for learning style detection by Basheer G.S., Ahmad M.S., Tang A.Y.C.

“…The multi-agent system applies ant colony optimization and fuzzy logic search algorithms as tools to detecting learning styles. Ultimately, a working prototype will be developed to validate the framework using ant colony optimization and fuzzy logic. � 2013 Springer-Verlag.…”

Security alert framework using dynamic tweet-based features for phishing detection on twitter by Liew, Seow Wooi

“…This framework is divided into three phases which are classification model of phishing detection, detection algorithm of phishing tweet detection and security alert mechanism of phishing tweet detection. …”
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An implementation of open systems interconnection (OSI) transport layer P2P identification algorithm using Netflow and Netfilter as a P2P traffic firewall / Amir Herman Amiruddin by Amiruddin, Amir Herman

“…The experiment showed that, for P2P identification ability, Netflow based algorithm detected 28.7% more than DPI. Further investigation clearly showed it was because DPI failed to detect encrypted P2P hosts compared to DPI. …”
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Fraud Detection in Shipping Industry using K-NN Algorithm by Subramaniam G., Mahmoud M.A.

A Telemedicine Tool Framework For Lung Sounds Classification Using Ensemble Classifier Algorithms by Abd, Sura Khalil, Shakeel, P.Mohamed, M.A., Burhanuddin, Jaber, Mustafa Musa, Mohammed, Mohammed Abdulameer, Yussof, Salman

“…The overall classification accuracy for the Improved Random Forest algorithm has 99.04%. The telemedicine framework was implemented with the Improved Random Forest algorithm. …”
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An improved diabetes risk prediction framework : An Indonesian case study by Sutanto, Daniel Hartono

“…However,there is the issue of noisy dataset detected as incomplete data and the outlier class problem that affects sampling bias.Existing frameworks were deemed difficult in identifying the critical risk factors of diabetes;some of which were considerably inaccurate and consume substantial computation time.The purpose of this study is to develop a suitable framework for predicting diabetes risks.From a complete blood test,the framework can predict and classify the output of either having diabetes risk or no diabetes risk.A Diabetes Risk Prediction Framework (DRPF) was developed from the literature review and case studies were afterwards conducted in three private hospitals in Semarang.Analyses were conducted to find a suitable component of the framework—due to lack of comparison and analysis on the combination of feature selection and classification algorithm.DRPF comprises four main sections: pre-processing,outlier detection,risk weighting,and learning. …”
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A smart framework for mobile botnet detection using static analysis by Anwar, Shahid, Mohamad Fadli, Zolkipli, Mezhuyev, Vitaliy, Inayat, Zakira

“…This technique combines permissions, activities, broadcast receivers, background services, API and uses the machine-learning algorithm to detect mobile botnets applications. The prototype was implemented and used to validate the performance, accuracy, and scalability of the proposed framework by evaluating 3000 android applications. …”
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Overhead view based person counting using deep learning by Kaw, Chee Zhao

“…Second, the OpenVINO Inference Engine is utilized to optimize the trained models in order to facilitate real-time implementation. Third, the accurate tracking of each detected person is performed using the deep learning based tracking framework, known as DeepSORT. …”
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Rogue access point detection and tracking system using trilateration algorithm by Lee, Chiew Min, Ang, Boon Keat, Kok, Ser Leen, Ghali, Abdulrahman Aminu, Sabri, Nor ‘Afifah

“…Besides, that the project introduces a robust method called trilateration algorithm. Therefore, the proposed trilateration algorithm if implemented will detect and reduce the risk of unauthorized network access in tertiary institutions and save the danger of financial loss. …”
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A COLLABORATIVE FRAMEWORK FOR ANDROID MALWARE IDENTIFICATION USING DYNAMIC ANALYSIS by Thayaaleni, Rajandran

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Multi-sensor fusion based on multiple classifier systems for human activity identification by Nweke, Henry Friday, Teh, Ying Wah, Mujtaba, Ghulam, Alo, Uzoma Rita, Al-garadi, Mohammed Ali

“…To this end, computationally efficient classification algorithms such as decision tree, logistic regression and k-Nearest Neighbors were used to implement diverse, flexible and dynamic human activity detection systems. …”
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A telemedicine tool framework for lung sounds classification using ensemble classifier algorithms by Jaber M.M., Abd S.K., Shakeel P.M., Burhanuddin M.A., Mohammed M.A., Yussof S.

An Optimized Semantic Segmentation Framework for Human Skin Detection by Huong, Audrey, Ngu, Xavier

“…Existing approaches used complex and various heuristic designs of image processing algorithms and deep models customized for skin detection problems. …”
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Embedded system for indoor guidance parking with Dijkstra’s algorithm and ant colony optimization by Mohammad Ata, Karimeh Ibrahim

“…The circuits have been designed by proteus, the microcontrollers have been programmed by micro C, and the Graphical User Interface (GUI) has been implemented in Java. Few by electronic components such as RFID, multiplexer, XBee, and servo motors have been used to realize the system. …”
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Passive client-centric rogue access point detection framework for WiFi hotspots by Ahmad, Nazrul Muhaimin

“…The proliferation of Wi-Fi hotspots in public places provides seamless Internet connectivity anywhere at any time to the wireless clients.Although many hotspots are often unprotected,unmanaged and unencrypted,this does not prevent the clients from actively connecting to the network.The underlying problem is that the network Access Point (AP) is always trusted.The adversary can impersonate a legitimate AP by setting up a rogue AP to commit espionage and to launch evil-twin attack,session hijacking,and eavesdropping.To aggravate the threats, existing detection solutions are ill-equipped to safeguard the client against rogue AP.Infrastructure- centric solutions are heavily relied on the deployment of sensors or centralized server for rogue AP detection, which are limited,expensive and rarely to be implemented in hotspots.Even though client-centric solutions offer threat-aware protection for the client,but the dependency of the existing solutions on the spoofable contextual network information and the necessity to be associated with the network makes those solutions are not viable for the hotspot’s client.Hence,this work proposes a framework of passive client-centric rogue AP detection for hotspots.Unlike existing solutions,the key idea is to piggyback AP-specific and network-specific information in IEEE 802.11 beacon frame that enables the client to perform the detection without authentication and association to any AP.Based on the spatial fingerprints included in the broadcasted information from the APs in the vicinity of the client,this work discloses a novel concept that enables the rogue AP detection via the client’s ability to self-colocalize and self-validate its own position in the hotspot.The legitimacy of the APs in the hotspot,in this view,lies in the fact that the correct matching between the Received Signal Strength Indicator (RSSI) measurements at the client and pre-recorded fingerprints is attainable when the beacons are transmitted only from the legitimate APs.Hence,any anomalousness in AP’s beacon frame or any attempt to replay the legitimate AP’s beacon frame from different location can be detected and classified as rogue AP threats.Through experiments in real environment,the results demonstrate that with proper algorithm selection and parameters tuning,the rogue AP detection framework can achieve over 90% detection accuracy in classifying the absence and presence of rogue AP threats in the hotspot.…”
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