A novel model for user clicks identification based on hidden semi-Markov

Xu, Chengyu; Du, Changjiang; Zhao, Gou-Feng; Yu, Shui

doi:10.1016/j.jnca.2012.12.005

Cited by 16 publications

(6 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When compared to other filter-based mechanisms, it is found that APFS has 44% higher defense effectiveness. Xu et al [25] proposed a method for detection of DDoS attacks based on a model which throws light into user clicks identification based on a Markov model known as hidden semi-Markov. Especially, it makes use of data pre-processing for effectively detecting application-layer DDoS attacks.…”

Section: Related Workmentioning

confidence: 99%

Detection of spoofed and non-spoofed DDoS attacks and discriminating them from flash crowds

Gera

Battula

2018

EURASIP J. on Info. Security

View full text Add to dashboard Cite

Distributed computing technology is widely used by Internet-based business applications. Supply chain management (SCM), customer relationship management (CRM), e-Commerce, and banking are some of the applications employing distributed computing. These applications are the main target to massive attacks known as distributed denial-of-service (DDoS) that cause a denial of service or degradation of services being rendered. The servers that provide reliable services to genuine users in a distributed environment are victims of such attacks that flood fake requests that appear genuine. Flash crowd, on the other hand, is the huge amount of traffic caused by certain flash events (FEs) that mimics DDoS attacks. Detection of DDoS attacks in the wake of flash crowds is a challenging problem to be addressed. The existing solutions are generally meant for either flash crowds or DDoS attacks and more research is needed to have a comprehensive approach for catering to the needs of detection of spoofed and non-spoofed variants of DDoS attacks. This paper proposes a methodology that can detect aforementioned DDoS attacks and differentiate them from flash crowds. NS-2 simulations are carried out on Ubuntu platform for validating the effectiveness of the proposed methodology.

show abstract

Section: Related Workmentioning

confidence: 99%

Detection of spoofed and non-spoofed DDoS attacks and discriminating them from flash crowds

Gera

Battula

2018

EURASIP J. on Info. Security

View full text Add to dashboard Cite

show abstract

“…The unremitting growth of web and its complexity has made the web more complex and hence increases the difficulty of identifying the users' access patterns over the web [28]. The author has proposed a novel method for identifying the users' access patterns.…”

Section: Related Workmentioning

confidence: 99%

Quine-McCluskey: A Novel Concept for Mining the Frequency Patterns from Web Data

Bhandari¹,

Goudar²,

Kumar³

2018

IJEME

View full text Add to dashboard Cite

With the advancement in the web technology it is considered as one of the vast repository of information. However this information is in the hidden form. Various data mining techniques need to be applied for extracting the meaningful information from the web. In this paper the various techniques are discussed that have been used by many researchers for extracting the information and also shown the disadvantages with the existing approaches. The paper put forward a novel concept of mining the association rule from the web data by using Quine-McCluskey algorithm. This algorithm is an optimization technique over the existing algorithm like Apriori, reverse Apriori, k-map. This paper exhibits the working of the Quine-McCluskey algorithm that can extract the frequently accessed web pages with minimum number of candidate sets generation. However the limitation of Quine-McCluskey algorithm is that it cannot find the infrequent patterns.

show abstract

“…In traditional methods, the analysis of user's behavior preferences is only in either of the dimensions of time, space, or content. Xu et al 11 proposed an implicit semi-Markov model to mine user online click patterns. Zhang et al 12 used the hidden Markov model and multi-state model to find the app usage patterns of users.…”

Section: Related Workmentioning

confidence: 99%

Analysis of behavioral differentiation in smart cities based on mobile users’ usage detail record data

Zhao

Nai

et al. 2018

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

The expansion of big data has played an important role in the feasibility of the smart city initiative. The massive amounts of data offer the potential for cities to obtain valuable insights from a large amount of data collected through various sources. Usage detail records not only include plentiful spatial-temporal information, but also describe users' activities in content space and time. They have three dimensions of information, which makes them favorable for the research of human behavior dynamics. To support smart cities, we collected usage detail records containing three dimensions of information from individuals and analyzed the relationship between them to get modes of users' behavior. In this article, we propose a method to discover the needed content for users and a way to provide these data to them. The result shows that two of these three dimensions have an invisible association. New behavioral patterns that we discovered from usage detail records can be derived for configuring resources reasonably and supporting creation of smart cities.

show abstract

A novel model for user clicks identification based on hidden semi-Markov

Cited by 16 publications

References 27 publications

Detection of spoofed and non-spoofed DDoS attacks and discriminating them from flash crowds

Detection of spoofed and non-spoofed DDoS attacks and discriminating them from flash crowds

Quine-McCluskey: A Novel Concept for Mining the Frequency Patterns from Web Data

Analysis of behavioral differentiation in smart cities based on mobile users’ usage detail record data

Contact Info

Product

Resources

About