HoneySpam 2.0: Profiling Web Spambot Behaviour

2010 24th IEEE International Conference on Advanced Information Networking and Applications

Talevski

et al. 2010

Self Cite

Abstract-This paper aims to provide an analytical view in estimating the cost of Spam 2.0. For this purpose, the authors define the web spam lifecycle and its associated impact. We also enlisted 5 stakeholders and focused on defining 5 cost calculations using a large collection of references. The cost of web spam then can be calculated with the definition of 13 parameters. Detail explanations of the web spam cost impacts are given with regards to the main four stakeholders: spammer, application provider, content provider and content consumer. Ongoing research in developing honey spam is also presented in this paper.

Section: Sending Spam Contentmentioning

confidence: 99%

Key Parameters in Identifying Cost of Spam 2.0

Ridzuan

2010 24th IEEE International Conference on Advanced Information Networking and Applications

Talevski

et al. 2010

Self Cite

“…Hence, using behavior analysis along with content based analysis might provide a better solution [21].…”

Section: Create Spam Messagesmentioning

confidence: 99%

Factors Involved in Estimating Cost of Email Spam

Ridzuan

Computational Science and Its Applications – ICCSA 2010

Talevski

2010

Self Cite

Abstract. This paper analyses existing research work to identify all possible factors involved in estimating cost of spam. Main motivation of this paper is to provide unbiased spam costs estimation. For that, we first study the email spam lifecycle and identify all possible stakeholders. We then categorise cost and study the impact on each stakeholder. This initial study will form the backbone of the real time spam cost calculating engine that we are developing for Australia.

“…However, these logs can not specify usernames and sessions of each request. Hence we employ our own web usage tracking system developed in our previous work, HoneySpam 2.0 [4] in order to collect web usage data.…”

Section: Web Usage Trackingmentioning

confidence: 99%

“…The study performed in this paper continues from our previous work on spambot detection [4] and presents a new method to detect spambot based on web usage navigation behaviour. The main focuses and contributions of this paper are to:…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Behaviour-Based Web Spambot Detection by Utilising Action Time and Action Frequency

Hayati

Chai

Computational Science and Its Applications – ICCSA 2010

et al. 2010

Self Cite

Abstract. Web spam is an escalating problem that wastes valuable resources, misleads people and can manipulate search engines in achieving undeserved search rankings to promote spam content. Spammers have extensively used Web robots to distribute spam content within Web 2.0 platforms. We referred to these web robots as spambots that are capable of performing human tasks such as registering user accounts as well as browsing and posting content. Conventional content-based and link-based techniques are not effective in detecting and preventing web spambots as their focus is on spam content identification rather than spambot detection. We extend our previous research by proposing two action-based features sets known as action time and action frequency for spambot detection. We evaluate our new framework against a real dataset containing spambots and human users and achieve an average classification accuracy of 94.70%.