An Improved Apriori Algorithm

Gu, Jiabin; Wang, Bao-jin; Zhang, Fengyu; Wang, Weiming; Gao, Ming

doi:10.1007/978-3-642-23214-5_18

Cited by 4 publications

(7 citation statements)

References 3 publications

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“…Due to the challenges of apriori algorithm in generating large quatities of itemsets and time consuming in testing and verifying candidate frequent k-items [3], which have resulted to its inefficiency, different versions of Apriori algorithm have been developed that manifested an improvement in the original algorithm like an improved Apriori algorithm that addressed the inefficiency in Apriori algorithm [3]. This research, in a bid to improve Apriori algorithm for the detection of malicious programs adopts particle swarm optimization in the candidate generation of detector so as to increase the detection process and reduce false alarm rate.…”

Section: Related Workmentioning

confidence: 99%

“…The original Apriori algorithm was proposed by Agrawal R. et al [2] in order to address the problem of mining association rules. The need to improve the efficiency of mining of frequent itemsets (highest occurring items), by reducing the times of scanning the database and reducing the number of candidate item sets, prompted [3] to propose an improved Apriori algorithm based on the classic Apriori algorithm. The basic idea of Apriori algorithm is to generate the frequent itemsets using iterative method in order to generated rules that Normaziah AbdulAziz Computer Science Department, International Islamic University Malaysia naa@iium.edu.my meet the minimum confidence to form rule sets and outputs [3].…”

Section: Introductionmentioning

confidence: 99%

“…The need to improve the efficiency of mining of frequent itemsets (highest occurring items), by reducing the times of scanning the database and reducing the number of candidate item sets, prompted [3] to propose an improved Apriori algorithm based on the classic Apriori algorithm. The basic idea of Apriori algorithm is to generate the frequent itemsets using iterative method in order to generated rules that Normaziah AbdulAziz Computer Science Department, International Islamic University Malaysia naa@iium.edu.my meet the minimum confidence to form rule sets and outputs [3]. Android malware has been very critical in compromising the security of information on the smartphone, since most facilities available on the conventional operating systems on computer are also present on the android operating system.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Android malware classification using static code analysis and Apriori algorithm improved with particle swarm optimization

Adebayo

AbdulAziz

2014

2014 4th World Congress on Information and Communication Technologies (WICT 2014)

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Several machine learning techniques based on supervised learning have been adopted in the classification of malware. However, only supervised learning techniques have proofed insufficient for malware classification task. This paper presents a classification of android malware using candidate detectors generated from an unsupervised association rule of Apriori algorithm improved with particle swarm optimization to train three different supervised classifiers. In this method, features were extracted from Android applications byte-code through static code analysis, selected and were used to train supervised classifiers. Using a number of candidate detectors, the true positive rate of detecting malicious code is maximized, while the false positive rate of wrongful detection is minimized. The results of the experiments show that the proposed combined technique has remarkable benefits over the detection using only supervised or unsupervised learners.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Android malware classification using static code analysis and Apriori algorithm improved with particle swarm optimization

Adebayo

AbdulAziz

2014

2014 4th World Congress on Information and Communication Technologies (WICT 2014)

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show abstract

“…Original a priori algorithm was proposed by Agrawal R. et al [2] in order to address the problem of mining association rules. The need to improve the efficiency of mining of frequent itemsets, by reducing the times of scanning the database and reducing the number of candidate itemsets, prompted [3] to propose an improved a priori algorithm based on the classic apriori algorithm. The basic idea of apriori algorithm is to generate the frequent itemsets using iterative method in order to generate rules that meet the minimum confidence to form rule sets and outputs [3].…”

Section: Introductionmentioning

confidence: 99%

“…The need to improve the efficiency of mining of frequent itemsets, by reducing the times of scanning the database and reducing the number of candidate itemsets, prompted [3] to propose an improved a priori algorithm based on the classic apriori algorithm. The basic idea of apriori algorithm is to generate the frequent itemsets using iterative method in order to generate rules that meet the minimum confidence to form rule sets and outputs [3]. This research purposely adopts particle swarm optimization in order to improve the generation of candidate detectors (flagbearers) which will otherwise improve the detection process by maximizing the true positive detection and minimizing the false positive detection.…”

Section: Introductionmentioning

confidence: 99%

Improved Malware Detection Model with Apriori Association Rule and Particle Swarm Optimization

Adebayo

Aziz

2019

Security and Communication Networks

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The incessant destruction and harmful tendency of malware on mobile devices has made malware detection an indispensable continuous field of research. Different matching/mismatching approaches have been adopted in the detection of malware which includes anomaly detection technique, misuse detection, or hybrid detection technique. In order to improve the detection rate of malicious application on the Android platform, a novel knowledge-based database discovery model that improves apriori association rule mining of a priori algorithm with Particle Swarm Optimization (PSO) is proposed. Particle swarm optimization (PSO) is used to optimize the random generation of candidate detectors and parameters associated with apriori algorithm (AA) for features selection. In this method, the candidate detectors generated by particle swarm optimization form rules using apriori association rule. These rule models are used together with extraction algorithm to classify and detect malicious android application. Using a number of rule detectors, the true positive rate of detecting malicious code is maximized, while the false positive rate of wrongful detection is minimized. The results of the experiments show that the proposed a priori association rule with Particle Swarm Optimization model has remarkable improvement over the existing contemporary detection models.

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Data Mining Using Apriori Algorithm and Linear Regression in Product Recommendations

Laurentinus¹,

Rizan²,

Sarwindah³

et al. 2021

2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

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An Improved Apriori Algorithm

Cited by 4 publications

References 3 publications

Android malware classification using static code analysis and Apriori algorithm improved with particle swarm optimization

Android malware classification using static code analysis and Apriori algorithm improved with particle swarm optimization

Improved Malware Detection Model with Apriori Association Rule and Particle Swarm Optimization

Data Mining Using Apriori Algorithm and Linear Regression in Product Recommendations

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