Search Results - (( security classification _ algorithm ) OR ( java application sensor algorithm ))

Symmetric Key Size for Different Level of Information Classification by Ibrahim, Subariah, Maarof, Mohd. Aizaini

“…Therefore confidential information is normally protected by using cryptographic algorithms. In these algorithms, key is an important element since it is one of the parameters that determine the level of security that the algorithms can provide. …”
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An Analysis Of Various Algorithms For Text Spam Classification And Clustering Using Rapidminer And Weka by Zainal K., Sulaiman N.F., Jali M.Z.

ChoCD : Usable and secure graphical password authentication scheme by Radhi Rafiee Bin Afandi

Study and Implementation of Data Mining in Urban Gardening by Mohana, Muniandy, Lee, Eu Vern

“…The process begins through the monitoring of plants using sensors connected to the Arduino device. Attached sensors generate data and send these data to the Java Servlet application through a WIFI module. …”
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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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A new mobile botnet classification based on permission and API calls by Yusof M., Madihah Mohd Saudi, Farida Hazwani Mohd Ridzuan

An analysis of intrusion detection classification using supervised machine learning algorithms on NSL-KDD dataset / Sarthak Rastogi ... [et al.] by Rastogi, Sarthak, Shrotriya, Archit, Singh, Mitul Kumar, Potukuchi, Raghu Vamsi

“…The IDS with machine learning method improves the detection accuracy of the security attacks. To this end, this paper studies the classification analysis of intrusion detection using various supervised learning algorithms such as SVM, Naive Bayes, KNN, Random Forest, Logistic Regression and Decision tree on the NSL-KDD dataset. …”
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Crytojacking Classification based on Machine Learning Algorithm by Mansor W.N.A.B.W., Ahmad A., Zainudin W.S., Kama M.N., Madihah Mohd Saudi

A New Model For Network-Based Intrusion Prevention System Inspired By Apoptosis by Mohammed Nadir bin Ali

Power system security assessment using artificial neural network: article / Mohd Fathi Zakaria by Zakaria, Mohd Fathi

“…This paper presented an application of Artificial Neural Network (ANN) in steady state stability classifications. A multi layer feed forward ANN with Back Propagation Network algorithm is proposed in determining the steady state stability classifications. …”
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K-NN classifier for data confidentiality in cloud computing by Zardari, M.A., Jung, L.T., Zakaria, N.

“…After data classification we got that what data need security and what data does not need security. …”
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Machine Learning Algorithm for Malware Detection: Taxonomy, Current Challenges, and Future Directions by Gorment N.Z., Selamat A., Cheng L.K., Krejcar O.

Subjects:

A Framework For Classification Software Security Using Common Vulnerabilities And Exposures by Hassan, Nor Hafeizah

“…This inclusive of the investigation of vulnerability classification objectives,processes,classifiers and focus domains among prominent framework.Final step is to construct the framework by establishing the formal presentation of the vulnerability classification algo-rithm.The validation process was conducted empirically using statistical method to assess the accuracy and consistency by using the precision and recall rate of the algorithm on five data sets each with 500 samples.The findings show a significant result with precision's error rate or p value is between 0.01 and 0.02 with error rate for recall's error rate is between 0.02 and 0.04.Another validation was conducted to verify the correctness of the classification by using expert opinions,and the results showed that the ambiguity of several cases were subdue. …”
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VEHICLE CLASSIFICATION USING NEURAL NETWORKS AND IMAGE PROCESSING by ONG KANG WEI, ONG KANG WEI, LOH SER LEE, LOH SER LEE

“…The aim of this study is to propose a vehicle classification scheme where YOLO v5 algorithm and Faster R-CNN algorithm are being implemented separately into vehicle classification, followed by comparison of result between these two algorithms. …”
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Maldroid- attribute selection analysis for malware classification by Rahiwan Nazar, Romli, Mohamad Fadli, Zolkipli, Mohd Zamri, Osman

“…Hence, the objective of this paper is to find the most effective and efficient attribute selection and classification algorithm in malware detection. Moreover, in order to get the best combination between attribute selection and classification algorithm, eight attributes selection and seven categories machine learning algorithm are applied in this study. …”
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Improved method of classification algorithms for crime prediction by Babakura, Abba, Sulaiman, Md. Nasir, Yusuf, Mahmud Ahmad

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A New Mobile Botnet Classification based on Permission and API Calls by Yusof, M, Saudi, MM, Ridzuan, F

Data classification based on confidentiality in virtual cloud environment by Zardari, M.A., Jung, L.T., Zakaria, M.N.B.

“…It is very difficult to decide (in cloud) which data need what security and which data do not need security. However it will be easy to decide the security level for data after data classification according to their security level based on the characteristics of the data. …”
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PV fault classification: Impact on accuracy performance using feature extraction in random-forest cross validation algorithm by Muhamad Zahim, Sujod, Siti Nor Azlina, Mohd Ghazali, Mohd Fadzil, Abdul Kadir, Al-Shetwi, Ali Qasem

“…This paper introduces a Solar PV Smart Fault Diagnosis and Classification (SFDC) model that harnesses the Random Forest (RF) algorithm in conjunction with Cross-Validation (CV) and an optimized feature extraction (FE) set. …”
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Realization Of The 1D Local Binary Pattern (LBP) Algorithm In Raspberry Pi For Iris Classification Using K-NN Classifier by Siow, Shien Loong

“…Iris is one of the biometric that widely used in the field of security due to its uniqueness. There are a lot of feature extraction methods and classification methods for iris classification. …”
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