Self-structured confabulation network for fast anomaly detection and reasoning

The enduring diagnosis of patient's medical records might be useful to determine the causes that are responsible for a particular disease. So that, one can take early preventive measures to curtail the risk of diseases that may occur with the growing age. Consequently, this can enhance the life expectancy probability. Here, a new algorithm CMARM is proposed for analysis of symptoms in order to find out the disease that may occur frequently and rarely with growing age. It uses map reduce paradigm inspired by cognitive learning. It is concerned with acquisition of problem-solving skills, intelligence and conscious thought and uses prevailing knowledge to generate new rules. It has been evaluated over synthetic data sets collected from the health data repository. Since, CMARM requires one-time file access therefore, it is consistently faster and also consuming less memory space than the FP tree based algorithm.

show abstract

“…The proposed algorithm is designed as an iterative map reduce procedure based on confabulation method 20,22,23 . It is processed in two steps: 1.…”

Section: Methodsmentioning

confidence: 99%

“…The CMARM approach is divided into three modules 20,22,23 ; (i) Creating distributed cluster: Since, the medical data deals with voluminous and heterogeneous data structure. Therefore, to handle such big amount of data HDFS is used 3,10,14 .…”

Section: Design Modelmentioning

confidence: 99%

Prognosis of Disease that may Occur with Growing Age using Confabulation Based Algorithm

Srivastava

Gautam

2017

Def. Life. Sc. Jl.

View full text Add to dashboard Cite

show abstract

“…The "Kernel State Modeling" approach was proposed by Murtaza et al to transfer system call traces to kernel module traces for the reduction of processing time [34] [35]. The "anomaly recognition and detection (AnRAD)" approach proposed by Chen et al achieves high-speed incremental learning of data streams by implementing "a real-time self-structuring learning framework" [29] [30], etc.…”

Section: Host-based Intrusion Detection System With System Callsmentioning

confidence: 99%

SCADS: A Scalable Approach Using Spark in Cloud for Host-based Intrusion Detection System with System Calls

Liu¹,

Zhi²,

He³

et al. 2021

Preprint

View full text Add to dashboard Cite

Following the current big data trend, the scale of real-time system call traces generated by Linux applications in a contemporary data center may increase excessively. Due to the deficiency of scalability, it is challenging for traditional host-based intrusion detection systems deployed on every single host to collect, maintain, and manipulate those large-scale accumulated system call traces. It is inflexible to build data mining models on one physical host that has static computing capability and limited storage capacity. To address this issue, we propose SCADS, a corresponding solution using Apache Spark in the Google cloud environment. A set of Spark algorithms are developed to achieve the computational scalability. The experiment results demonstrate that the efficiency of intrusion detection can be enhanced, which indicates that the proposed method can apply to the design of next-generation host-based intrusion detection systems with system calls.

show abstract

“…In June 2015, Qiuwen Chen, Qing Wu, Morgan Bishop, Richard Linderman and QiuQiu proposed an algorithm for self-structured confabulation network for fast anomaly detection and reasoning [14]. In this paper, the author proposed an automatic procedure that learns the structure of a confabulation network from the incoming dataset.…”

Section: Related Workmentioning

confidence: 99%

“…Studies [11], [14] suggest that Confabulation association rule mining (CARM) approach, based on cognitive learning for generating association rules lead to generation of more interesting rules. CARM mine association rules by only one pass through the file as it is based on pairwise item conditional probability.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Medical Domain Using CMARM: Confabulation Mapreduce Association Rule Mining Algorithm for Frequent and Rare Itemsets

Gautam¹,

Srivastava²

2015

ijacsa

View full text Add to dashboard Cite

Abstract-In Human Life span, disease is a major cause of illness and death in the modern society. There are various factors that are responsible for diseases like work environment, living and working conditions, agriculture and food production, housing, unemployment, individual life style etc. The early diagnosis of any disease that frequently and rarely occurs with the growing age can be helpful in curing the disease completely or to some extent. The long-term prognosis of patient records might be useful to find out the causes that are responsible for particular diseases. Therefore, human being can take early preventive measures to minimize the risk of diseases that may supervene with the growing age and hence increase the life expectancy chances. In this paper, a new CMARM: Confabulation-MapReduce based association rule mining algorithm is proposed for the analysis of medical data repository for both rare and frequent itemsets using an iterative MapReduce based framework inspired by cogency. Cogency is the probability of the assumed facts being true if the conclusion is true, means it is based on pairwise item conditional probability, so the proposed algorithm mine association rules by only one pass through the file. The proposed algorithm is also valuable for dealing with infrequent items due to its cogency inspired approach.

show abstract

Self-structured confabulation network for fast anomaly detection and reasoning

Cited by 6 publications

References 21 publications

Prognosis of Disease that may Occur with Growing Age using Confabulation Based Algorithm

Prognosis of Disease that may Occur with Growing Age using Confabulation Based Algorithm

SCADS: A Scalable Approach Using Spark in Cloud for Host-based Intrusion Detection System with System Calls

Analysis of Medical Domain Using CMARM: Confabulation Mapreduce Association Rule Mining Algorithm for Frequent and Rare Itemsets

Contact Info

Product

Resources

About