A machine learning approach for feature selection traffic classification using security analysis

© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Class imbalance has become a big problem that leads to inaccurate traffic classification. Accurate traffic classification of traffic flows helps us in security monitoring, IP management, intrusion detection, etc. To address the traffic classification problem, in literature, machine learning (ML) approaches are widely used. Therefore, in this paper, we also proposed an ML-based hybrid feature selection algorithm named WMI_AUC that make use of two metrics: weighted mutual information (WMI) metric and area under ROC curve (AUC). These metrics select effective features from a traffic flow. However, in order to select robust features from the selected features, we proposed robust features selection algorithm. The proposed approach increases the accuracy of ML classifiers and helps in detecting malicious traffic. We evaluate our work using 11 well-known ML classifiers on the different network environment traces datasets. Experimental results showed that our algorithms achieve more than 95% flow accuracy results

Adopting incentive mechanisms for large-scale participation in mobile crowdsensing: from literature review to a conceptual framework

Mobile crowdsensing is a burgeoning concept that allows smart cities to leverage the sensing power and ubiquitous nature of mobile devices in order to capture and map phenomena of common interest. At the core of any successful mobile crowdsensing application is active user participation, without which the system is of no value in sensing the phenomenon of interest. A major challenge militating against widespread use and adoption of mobile crowdsensing applications is the issue of how to identify the most appropriate incentive mechanism for adequately and efficiently motivating participants. This paper reviews literature on incentive mechanisms for mobile crowdsensing and proposes the concept of SPECTRUM as a guide for inferring the most appropriate type of incentive suited to any given crowdsensing task. Furthermore, the paper highlights research challenges and areas where additional studies related to the different factors outlined in the concept of SPECTRUM are needed to improve citizen participation in mobile crowdsensing. It is envisaged that the broad range of factors covered in SPECTRUM will enable smart cities to efficiently engage citizens in large-scale crowdsensing initiatives. More importantly, the paper is expected to trigger empirical investigations into how various factors as outlined in SPECTRUM can influence the type of incentive mechanism that is considered most appropriate for any given mobile crowdsensing initiative

Online consumer misbehaviour: an application of neutralization theory

Studies have argued that misbehaviour by customers is becoming increasingly prevalent in certain sectors. However, online consumer misbehaviour is comparatively under-researched. The focus of the current study is peer-to-peer activities, including copying music, movies, software or video games: a phenomenon which affects the entertainment sector as a whole and costs the industry billions of pounds each year. Neutralization theory provides a potentially fruitful perspective from which to explore consumer justifications and rationalizations for their online misbehaviour. The aim of this paper is to explore the extent to which peer-to-peer users employ techniques of neutralization to justify prior-to behaviour or rationalize their activities post behaviour. First, a review of online customer misbehaviour is provided, followed by an overview of existing research into the techniques of neutralization. Following a discussion of the research methods employed, findings regarding the peer-to-peer online misbehaviours and neutralization techniques are presented. Data analysis reveals that peer-to-peer file-sharers employ (often multiple) techniques of neutralization in order to pre-justify or post-event rationalize their activities, including: denial of victim; denial of injury; denial of responsibility; claim of normality; claim of relative acceptability; justification by comparison; and appeal to higher loyalties. The paper concludes with a series of implications for both theory and practice