Association rules mining analysis of app usage based on mobile traffic flow data

Liu, Yang; Yu, Ke; Wu, Xiaofei; Shi, Yuexi; Tan, Yaowen

doi:10.1109/icbda.2018.8367651

Cited by 5 publications

(7 citation statements)

References 15 publications

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“…This paper only focused on objective interestingness measure using support, confidence and lift to measure the meaningful and reliable association ruleset that were used to guide security analysts in making decisions. The thresholds for minimum support (minsup) and minimum confidence (minconf) were predefined manually by using trial and error method [7], [19], [22]. The summary of the association rules generated for all the datasets is depicted in Table XI using Apriori Algorithm.…”

Section: B Results Of Evaluation and Validation For Cti-armentioning

confidence: 99%

“…Agrawal and Srikant developed this algorithm to study customers' purchasing behavior in supermarkets where goods are often purchased together by customers [4]. Besides, the Apriori Algorithm has also been used successfully in many areas of daily life, including energy, recruitment, communication protocol, monitoring and network traffic behavior [7]. Hence, the implementation of the Apriori Algorithm in determining malicious network traffic behavior can help security analysts to study attacker behavior in conducting cyberattack.…”

Section: A Cyber Threat Intelligence (Cti) For Threat Attributionmentioning

confidence: 99%

“…The Apriori algorithm is proven to be suitable for this study. Meanwhile Liu et al have utilized Apriori and MS-Apriori algorithm to investigate the relationship of data for network footprint (NFP) which consists of DPI data from ISPs and Crawler data from Web for App usage analysis [7]. The result provides insights for mobile application developers to recommend other applications for their users based on their interest and usage pattern.…”

Section: A Cyber Threat Intelligence (Cti) For Threat Attributionmentioning

confidence: 99%

See 2 more Smart Citations

Formulation of Association Rule Mining (ARM) for an Effective Cyber Attack Attribution in Cyber Threat Intelligence (CTI)

Abu¹,

Selamat²,

Yusof³

et al. 2021

IJACSA

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In recent year, an adversary has improved their Tactic, Technique and Procedure (TTPs) in launching cyberattack that make it less predictable, more persistent, resourceful and better funded. So many organisation has opted to use Cyber Threat Intelligence (CTI) in their security posture in attributing cyberattack effectively. However, to fully leverage the massive amount of data in CTI for threat attribution, an organisation needs to spend their focus more on discovering the hidden knowledge behind the voluminous data to produce an effective cyberattack attribution. Hence this paper emphasized on the research of association analysis in CTI process for cyber attack attribution. The aim of this paper is to formulate association ruleset to perform the attribution process in the CTI. The Apriori algorithm is used to formulate association ruleset in association analysis process and is known as the CTI Association Ruleset (CTI-AR). Interestingness measure indicator specially support (s), confidence (c) and lift (l) are used to measure the practicality, validity and filtering the CTI-AR. The results showed that CTI-AR effectively identify the attributes, relationship between attributes and attribution level group of cyberattack in CTI. This research has a high potential of being expanded into cyber threat hunting process in providing a more proactive cybersecurity environment.

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Section: B Results Of Evaluation and Validation For Cti-armentioning

confidence: 99%

Section: A Cyber Threat Intelligence (Cti) For Threat Attributionmentioning

confidence: 99%

Section: A Cyber Threat Intelligence (Cti) For Threat Attributionmentioning

confidence: 99%

See 1 more Smart Citation

Formulation of Association Rule Mining (ARM) for an Effective Cyber Attack Attribution in Cyber Threat Intelligence (CTI)

Abu¹,

Selamat²,

Yusof³

et al. 2021

IJACSA

View full text Add to dashboard Cite

show abstract

“…Agrawal [9] developed this algorithm to study customers' purchasing behavior in supermarkets where goods are often purchased together by customers. Besides, Apriori Algorithm also has been used in many areas of daily life successfully, including energy, recruitment, communication protocol, monitoring and network traffic behavior [10]. Hence the implementation of Apriori Algorithm in determining network traffic behavior can help security analysts to study attacker behavior in conducting cyberattack.…”

Section: Wherementioning

confidence: 99%

“…As discussed in Section 2, this paper will only focus on objective interestingness measure specifically using support, confidence and lift. While, the thresholds for minimum support (minsup) and minimum confidence (minconf) are manually defined by user [20][21] [10]. Table III depicts the top five association rules with respect to lift measure.…”

Section: Association Rule Evaluation Usingmentioning

confidence: 99%

An Attribution of Cyberattack using Association Rule Mining (ARM)

Abu¹,

Selamat²,

Yusof³

et al. 2020

IJACSA

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With the rapid development of computer networks and information technology, an attacker has taken advantage to manipulate the situation to launch a complicated cyberattack. This complicated cyberattack causes a lot of problems among the organization because it requires an effective cyberattack attribution to mitigate and reduce the infection rate. Cyber Threat Intelligence (CTI) has gain wide coverage from the media due to its capability to provide CTI feeds from various data sources that can be used for cyberattack attribution. In this paper, we study the relationship of basic Indicator of Compromise (IOC) based on a network traffic dataset from a data mining approach. This dataset is obtained using a crawler that is deployed to pull security feed from Shadowserver. Then an association analysis method using Apriori Algorithm is implemented to extract rules that can discover interesting relationship between large sets of data items. Finally, the extracted rules are evaluated over the factor of interestingness measure of support, confidence and lift to quantify the value of association rules generated with Apriori Algorithm. By implementing the Apriori Algorithm in Shadowserver dataset, we discover some association rules among several IOC which can help attribute the cyberattack.

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A Combined Approach for Customer Profiling in Video on Demand Services Using Clustering and Association Rule Mining

2020

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The purpose of this paper is to propose a combined data mining approach for analyzing and profiling customers in video on demand (VoD) services. The proposed approach integrates clustering and association rule mining. For customer segmentation, the LRFMP model is employed alongside the k-means and Apriori algorithms to generate association rules between the identified customer groups and content genres. The applicability of the proposed approach is demonstrated on real-world data obtained from an Internet protocol television (IPTV) operator. In this way, four main customer groups are identified: ''high consuming-valuable subscribers'','' less consuming subscribers'','' less consuming-loyal subscribers'' and ''disloyal subscribers''. In detail, for each group of customers, a different marketing strategy or action is proposed, mainly campaigns, special-day promotions, discounted materials, offering favorite content, etc. Further, genres preferred by these customer segments are extracted using the Apriori algorithm. The results obtained from this case study also show that the proposed approach provides an efficient tool to form different customer segments with specific content rental characteristics, and to generate useful association rules for these distinct groups. The proposed combined approach in this research would be beneficial for IPTV service providers to implement effective CRM and customer-based marketing strategies.

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Association rules mining analysis of app usage based on mobile traffic flow data

Cited by 5 publications

References 15 publications

Formulation of Association Rule Mining (ARM) for an Effective Cyber Attack Attribution in Cyber Threat Intelligence (CTI)

Formulation of Association Rule Mining (ARM) for an Effective Cyber Attack Attribution in Cyber Threat Intelligence (CTI)

An Attribution of Cyberattack using Association Rule Mining (ARM)

A Combined Approach for Customer Profiling in Video on Demand Services Using Clustering and Association Rule Mining

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