Android Malware Detection Using Kullback-Leibler Divergence

Cooper, Vanessa N.; Haddad, Hisham M.; Shahriar, Hossain

doi:10.14201/adcaij2014321725

Cited by 5 publications

(2 citation statements)

References 5 publications

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“…Cooper [21,35] applies KLD to differentiate the behavior of malware and good android applications. In particular, SMS applications are analyzed to develop a set of elements (one or more related API calls) representing SMS sending or receiving operations within good applications.…”

Section: Kld-based Approachesmentioning

confidence: 99%

Content Provider Leakage Vulnerability Detection in Android Applications

Shahriar

Haddad

2014

Proceedings of the 7th International Conference on Security of Information and Networks

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Although much research effort has focused on Android malware detection, very little attention has been given to implementationlevel vulnerabilities. This paper focuses on Content Provider Leakage vulnerability that can be exploited by viewing or editing sensitive data through malware. We present a new technique for detecting content provider leakage vulnerability. We propose Kullback-Leibler Divergence (KLD) as a measure to detect the content provider leakage vulnerability. In particular, our contribution includes the development of a set of elements and mapping the elements to programming principles for secure implementation of content provider classes. These elements are captured from the implementation to form the initial population set. The population set is used to measure the divergence of a newly implemented application with content provider to identify potential vulnerabilities. We also apply a back-off smoothing technique to compute the KLD value. We implement a java prototype tool to evaluate a set of content provider implementations to show the effectiveness of the proposed approach. The initial results show that by choosing an appropriate threshold level, KLD is an effective method for detecting content provider leakage vulnerability.

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Section: Kld-based Approachesmentioning

confidence: 99%

Content Provider Leakage Vulnerability Detection in Android Applications

Shahriar

Haddad

2014

Proceedings of the 7th International Conference on Security of Information and Networks

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“…Also, they have focused on minimizing long-term service delays and computation costs under the resource along with deadline constraints. To intend for this problem, they have applied the reinforcement learning approach and have presented a Double Deep Q-Learning (DDQL)-based scheduling algorithm using the target network and experience replay techniques [56][57][58][59][60][61][62]. Finally, they have validated that their proposed algorithm outperforms other states of the arts.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED CHAPTER: An Energy-Aware Dynamic Resource Management Technique Using Deep Q-Learning Algorithm and Joint VM and Container Consolidation Approach for Green Computing in Cloud Data Centers

Gholipour

Shoeibi

Arianyan

2021

Advances in Intelligent Systems and Computing

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These days by a high increase in the amount of computation and big data gathering and analysis, everybody needs more resources. Buying more computational and storage resources are so expensive. However, cloud computing solved this problem by providing a "pay as you go" plans therefor, users will only pay for resources that they used. However, using this technology has its challenges. One of them is resource management, which is focusing on the methodologies of dedicating resources to the users with the minimum of waste. In this paper, we propose a novel energy-aware resource management technique, using the concepts of both joint VM and container consolidation approach and deep Q-Learning algorithm for green computing in cloud data centers in order to minimize the waste of resources, migration rate, and energy.

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Some Universal Insights on Divergences for Statistics, Machine Learning and Artificial Intelligence

Broniatowski

Stummer

2018

Geometric Structures of Information

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Android Malware Detection Using Kullback-Leibler Divergence

Cited by 5 publications

References 5 publications

Content Provider Leakage Vulnerability Detection in Android Applications

Content Provider Leakage Vulnerability Detection in Android Applications

RETRACTED CHAPTER: An Energy-Aware Dynamic Resource Management Technique Using Deep Q-Learning Algorithm and Joint VM and Container Consolidation Approach for Green Computing in Cloud Data Centers

Some Universal Insights on Divergences for Statistics, Machine Learning and Artificial Intelligence

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