PAREEKSHA: a machine learning approach for intrusion and anomaly detection
A Nagaraja, S Aljawarneh - … of the First International Conference on Data …, 2018 - dl.acm.org
Membership functions help us to identify and know the similarity between two elements such
as vectors or sequences. The objective of this paper is to suggest a membership function …
as vectors or sequences. The objective of this paper is to suggest a membership function …
A machine learning approach for imputation and anomaly detection in IoT environment
R Vangipuram, RK Gunupudi, VK Puligadda… - Expert …, 2020 - Wiley Online Library
The problem of anomaly and attack detection in IoT environment is one of the prime
challenges in the domain of internet of things that requires an immediate concern. For …
challenges in the domain of internet of things that requires an immediate concern. For …
An efficient approach for imputation and classification of medical data values using class-based clustering of medical records
UR Yelipe, S Porika, M Golla - Computers & Electrical Engineering, 2018 - Elsevier
Medical data is usually not free from missing values and this is also true when data is
collected and sampled through various clinical trials. Existing Imputation techniques do not …
collected and sampled through various clinical trials. Existing Imputation techniques do not …
Mantra: a novel imputation measure for disease classification and prediction
S Aljawarneh, V Radhakrishna, GS Reddy - Proceedings of the first …, 2018 - dl.acm.org
Medical record instances can have missing values which makes them unsuitable for
learning process. Data Imputation is normally done to fill one or more missing data attribute …
learning process. Data Imputation is normally done to fill one or more missing data attribute …
UTTAMA: an intrusion detection system based on feature clustering and feature transformation
Detecting Intrusions and anomalies is becoming much more challenging with new attacks
popping out over a period of time. Achieving better accuracies by applying benchmark …
popping out over a period of time. Achieving better accuracies by applying benchmark …
NMVI: A data-splitting based imputation technique for distinct types of missing data
In the IoT world, where minute digital devices are acclimated to sense the data, a failure in
such devices results in immense information loss and insufficient information regarding …
such devices results in immense information loss and insufficient information regarding …
Sequential approach for mining of temporal itemsets
V Radhakrishna, S Aljawarneh, A Cheruvu - Proceedings of the Fourth …, 2018 - dl.acm.org
Sequential approach for mining temporal itemsets initially proposed by Yoo and Sekhar
uses the Euclidean distance measure to discover similarity profiled temporal associations …
uses the Euclidean distance measure to discover similarity profiled temporal associations …
A membership function for intrusion and anomaly detection of low frequency attacks
The ultimate objective of intrusion detection problem is to identify surprising intrusions that
compromise networks. Determining intrusions through the application of classifiers or …
compromise networks. Determining intrusions through the application of classifiers or …
An imputation measure for data imputation and disease classification of medical datasets
S Aljawarneh, V Radhakrishna… - AIP Conference …, 2019 - pubs.aip.org
Imputation of missing data values is an important pre-processing task for mining of medical
data records. Application of data mining principles, techniques requires the dataset to be …
data records. Application of data mining principles, techniques requires the dataset to be …
A similarity function for feature pattern clustering and high dimensional text document classification
Text document classification and clustering is an important learning task which fits to both
data mining and machine learning areas. The learning task throws several challenges when …
data mining and machine learning areas. The learning task throws several challenges when …