Unsupervised Anomaly Detection with Unlabeled Data Using Clustering by Chimphlee, Witcha, Abdullah, Abdul Hanan, Md. Sap, Mohd. Noor

“…We present a clustering-based intrusion detection algorithm, unsupervised anomaly detection, which trains on unlabeled data in order to detect new intrusions. …”
Get full text

Anomaly detection for controlling data accruracy in service industry by Samsuddin, Nurul Asyikin

“…The purpose of this project is to investigate the application of anomaly detection, particularly control charts for individual sample, to control data quality of a risk management system in a financial industry. …”

Anomaly detection for controlling data accuracy in service industry by Samsuddin, Nurul Asyikin

“…The purpose of this project is to investigate the application of anomaly detection, particularly control charts for individual sample, to control data quality of a risk management system in a financial industry. …”
Get full text

Adaptive and online data anomaly detection for wireless sensor systems by Rassam, Murad Abdo, Maarof, Mohd. Aizaini, Zainal, Anazida

“…In this paper, two efficient and effective anomaly detection models PCCAD and APCCAD are proposed for static and dynamic environments, respectively. …”

Integrating genetic algorithms and fuzzy c-means for anomaly detection by Chimphlee, Witcha, Abdullah, Abdul Hanan, Sap, Noor Md., Chimphlee, Siriporn, Srinoy, Surat

“…The amount of available network audit data instances is usually large; human labeling is tedious, time-consuming, and expensive. Traditional anomaly detection algorithms require a set of purely normal data from which they train their model. …”
Get full text

To identify suspicious activity in anomaly detection based on soft computing by Chimphlee, Witcha, Sap, M., Abdullah, Abdul Hanan, Chimphlee, Siriporn, Srinoy, Surat

“…Empirical studies using the network security data set from the DARPA 1998 offline intrusion detection project (KDD 1999 Cup) show the feasibility of misuse and anomaly detection results.…”

Unsupervised anomaly detection for unlabelled wireless sensor networks data by Mohd. Zamry, Nurfazrina, Zainal, Anazida, A. Rassam, Murad

“…Therefore, this paper will use the unsupervised one-class SVM (OCSVM) to build the anomaly detection schemes for better decision making. Unsupervised OCSVM is preferable to be used in WSNs domain due to the one class of data training is used to build normal reference model. …”
Get full text

Distributed CESVM-DR anomaly detection for wireless sensor network by Mohd. Zamry, Nurfazrina, Zainal, Anazida, A. Rassam, Murad

“…Meanwhile, the dimension reduction has been providing the lightweight of the anomaly detection schemes. In this paper Distributed Centered Hyperellipsoidal Support Vector Machine (DCESVM-DR) anomaly detection schemes is proposed to provide the efficiency and effectiveness of the anomaly detection schemes.…”
Get full text

A study on advanced statistical analysis for network anomaly detection by Ngadi, Md. Asri, Idris, Mohd. Yazid, Abdullah, Abd. Hanan

“…Algorithms for building detection models are usually classified into two categories: misuse detection and anomaly detection. Misuse detection algorithms model know attack behavior. …”

Anomaly detection in the temperature of an AC motor using embedded machine learning by Ismail, Ezzeldin Ayman Ibrahim, Ahmad, Mohd. Ridzuan

Get full text

An anti virus scheme using digital signature and anomaly detection techniques by Subramaniam, Suresh Babu

“…The scheme comprises two layers of protection, where the first layer implements digital signature technique while the second layer implements anomaly detection technique. In the scheme, newly downloaded files that have been digitally signed using SHA-l and RSA algorithms are verified at the first layer. …”
Get full text

CICIDS-2017 dataset feature analysis with information gain for anomaly detection by Kurniabudi, Kurniabudi, Stiawan, Deris, Darmawijoyo, Darmawijoyo, Idris, Mohd. Yazid, Bamhdi, Alwi M., Budiarto, Rahmat

“…The objective of this study is to analyze relevant and significant features of huge network traffic to be used to improve the accuracy of traffic anomaly detection and to decrease its execution time. Information Gain is the most feature selection technique used in Intrusion Detection System (IDS) research. …”
Get full text

Real valued negative selection for anomaly detection in wireless ad hoc networks by Abdul Majid, Azri, Maarof, Mohd. Aizaini

“…To achieve our goal, we studied how the real-valued negative selection algorithm can be applied in wireless ad hoc network network and finally we proposed the enhancements to real-valued negative selection algorithm for anomaly detection in wireless ad hoc network.…”
Get full text

Anomaly detection of intrusion based on integration of rough sets and fuzzy c-means by Chimphlee, Witcha, Md. Sap, Mohd. Noor, Abdullah, Abdul Hanan, Chimphlee, Siriporn

Get full text

Improving the anomaly detection by combining PSO search methods and J48 algorithm by Kurniabudi, Kurniabudi, Abdul Harris, Abdul Harris, Mintaria, Albertus Edward, Darmawijoyo, Darmawijoyo, Stiawan, Deris, Idris, Mohd. Yazid, Budiarto, Rahmat

“…Compared with the previous study the proposed technique has better accuracy, TPR, and FPR.Anomaly Detection, CICIDS2017…”

Long short-term memory autoencoder-based anomaly detection system for electric motors by Sharrar, Labib

“…Based on the experimental results, as the simulated defect worsened, the rate of anomalies detected by the system increased, with the maximum anomaly rate reaching 7 anomalies per second. …”
Get full text

Distributed anomaly detection scheme based on lightweight data aggregation in wireless sensor network by Mohd. Zamry, Nurfazrina

“…Lastly, enhancing the efficiency and effectiveness of the anomaly detection scheme by designing the distributed anomaly detection scheme (DCESVM-DR). …”
Get full text

Iterative window size estimation on self-similarity measurement for network traffic anomaly detection by Idris, Mohd. Yazid, Abdullah, Abdul Hanan, Maarof, Mohd. Aizaini

“…Thus, the purpose of this method is to minimize the curve-fitting error on self-similarity measurement and detection loss probability in anomaly detection. This iterative method was applied to network traffic data provided by Lincoln Lab, Massachuset Institute of Technology (MIT). …”

A review of anomaly detection techniques and Distributed Denial of Service (DDoS) on Software Defined Network (SDN) by Ahmad Khairi, Muntaz Hana, Syed Ariffin, Sharifah Hafizah, Abdul Latiff, Nurul Mu’azzah, Abdullah, Ahmad Sharmi, Hassan, Mohd. Kamal

“…This research explains DDoS attacks and the anomaly detection as one of the famous detection techniques for intelligent networks.…”

An anomaly detection model using candid covariance-free incremental principal component analysis for wireless sensor networks by Qasem, Murad Abdo Rassam