Data Mining in Electronic Commerce

Modern business is rushing toward e-commerce. If the transition is done properly, it enables better management, new services, lower transaction costs and better customer relations. Success depends on skilled information technologists, among whom are statisticians. This paper focuses on some of the contributions that statisticians are making to help change the business world, especially through the development and application of data mining methods. This is a very large area, and the topics we cover are chosen to avoid overlap with other papers in this special issue, as well as to respect the limitations of our expertise. Inevitably, electronic commerce has raised and is raising fresh research problems in a very wide range of statistical areas, and we try to emphasize those challenges.Comment: Published at http://dx.doi.org/10.1214/088342306000000204 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Security challenges and solutions for e-business

The advantages of economic growth and increasing ease of operation afforded by e-business and e-commerce developments are unfortunately matched by growth in cyber attacks. This paper outlines the common attacks faced by e-business and describes the defenses that can be used against them. It also reviews the development of newer security defense methods. These are: (1) biometrics for authentication; parallel processing to increase power and speed of defenses; (2) data mining and machine learning to identify attacks; (3) peer-to-peer security using blockchains; 4) enterprise security modelling and security as a service; and (5) user education and engagement. The review finds overall that one of the most prevalent dangers is social engineering in the form of phishing attacks. Recommended counteractions include education and training, and the development of new machine learning and data sharing approaches so that attacks can be quickly discovered and mitigated

Cyber Security

This open access book constitutes the refereed proceedings of the 18th China Annual Conference on Cyber Security, CNCERT 2022, held in Beijing, China, in August 2022. The 17 papers presented were carefully reviewed and selected from 64 submissions. The papers are organized according to the following topical sections: ​​data security; anomaly detection; cryptocurrency; information security; vulnerabilities; mobile internet; threat intelligence; text recognition

A Critical Analysis Of The State-Of-The-Art On Automated Detection Of Deceptive Behavior In Social Media

Recently, a large body of research has been devoted to examine the user behavioral patterns and the business implications of social media. However, relatively little research has been conducted regarding users’ deceptive activities in social media; these deceptive activities may hinder the effective application of the data collected from social media to perform e-marketing and initiate business transformation in general. One of the main contributions of this paper is the critical analysis of the possible forms of deceptive behavior in social media and the state-of-the-art technologies for automated deception detection in social media. Based on the proposed taxonomy of major deception types, the assumptions, advantages, and disadvantages of the popular deception detection methods are analyzed. Our critical analysis shows that deceptive behavior may evolve over time, and so making it difficult for the existing methods to effectively detect social media spam. Accordingly, another main contribution of this paper is the design and development of a generic framework to combat dynamic deceptive activities in social media. The managerial implication of our research is that business managers or marketers will develop better insights about the possible deceptive behavior in social media before they tap into social media to collect and generate market intelligence. Moreover, they can apply the proposed adaptive deception detection framework to more effectively combat the ever increasing and evolving deceptive activities in social medi

Cyber Security

This open access book constitutes the refereed proceedings of the 18th China Annual Conference on Cyber Security, CNCERT 2022, held in Beijing, China, in August 2022. The 17 papers presented were carefully reviewed and selected from 64 submissions. The papers are organized according to the following topical sections: ​​data security; anomaly detection; cryptocurrency; information security; vulnerabilities; mobile internet; threat intelligence; text recognition

Machine Learning based Anomaly Detection for Cybersecurity Monitoring of Critical Infrastructures

openManaging critical infrastructures requires to increasingly rely on Information and Communi- cation Technologies. The last past years showed an incredible increase in the sophistication of attacks. For this reason, it is necessary to develop new algorithms for monitoring these infrastructures. In this scenario, Machine Learning can represent a very useful ally. After a brief introduction on the issue of cybersecurity in Industrial Control Systems and an overview of the state of the art regarding Machine Learning based cybersecurity monitoring, the present work proposes three approaches that target different layers of the control network architecture. The first one focuses on covert channels based on the DNS protocol, which can be used to establish a command and control channel, allowing attackers to send malicious commands. The second one focuses on the field layer of electrical power systems, proposing a physics-based anomaly detection algorithm for Distributed Energy Resources. The third one proposed a first attempt to integrate physical and cyber security systems, in order to face complex threats. All these three approaches are supported by promising results, which gives hope to practical applications in the next future.openXXXIV CICLO - SCIENZE E TECNOLOGIE PER L'INGEGNERIA ELETTRONICA E DELLE TELECOMUNICAZIONI - Elettromagnetismo, elettronica, telecomunicazioniGaggero, GIOVANNI BATTIST

Detection of network anomalies and novel attacks in the internet via statistical network traffic separation and normality prediction

With the advent and the explosive growth of the global Internet and the electronic commerce environment, adaptive/automatic network and service anomaly detection is fast gaining critical research and practical importance. If the next generation of network technology is to operate beyond the levels of current networks, it will require a set of well-designed tools for its management that will provide the capability of dynamically and reliably identifying network anomalies. Early detection of network anomalies and performance degradations is a key to rapid fault recovery and robust networking, and has been receiving increasing attention lately. In this dissertation we present a network anomaly detection methodology, which relies on the analysis of network traffic and the characterization of the dynamic statistical properties of traffic normality, in order to accurately and timely detect network anomalies. Anomaly detection is based on the concept that perturbations of normal behavior suggest the presence of anomalies, faults, attacks etc. This methodology can be uniformly applied in order to detect network attacks, especially in cases where novel attacks are present and the nature of the intrusion is unknown. Specifically, in order to provide an accurate identification of the normal network traffic behavior, we first develop an anomaly-tolerant non-stationary traffic prediction technique, which is capable of removing both pulse and continuous anomalies. Furthermore we introduce and design dynamic thresholds, and based on them we define adaptive anomaly violation conditions, as a combined function of both the magnitude and duration of the traffic deviations. Numerical results are presented that demonstrate the operational effectiveness and efficiency of the proposed approach, under different anomaly traffic scenarios and attacks, such as mail-bombing and UDP flooding attacks. In order to improve the prediction accuracy of the statistical network traffic normality, especially in cases where high burstiness is present, we propose, study and analyze a new network traffic prediction methodology, based on the frequency domain traffic analysis and filtering, with the objective_of enhancing the network anomaly detection capabilities. Our approach is based on the observation that the various network traffic components, are better identified, represented and isolated in the frequency domain. As a result, the traffic can be effectively separated into a baseline component, that includes most of the low frequency traffic and presents low burstiness, and the short-term traffic that includes the most dynamic part. The baseline traffic is a mean non-stationary periodic time series, and the Extended Resource-Allocating Network (BRAN) methodology is used for its accurate prediction. The short-term traffic is shown to be a time-dependent series, and the Autoregressive Moving Average (ARMA) model is proposed to be used for the accurate prediction of this component. Furthermore, it is demonstrated that the proposed enhanced traffic prediction strategy can be combined with the use of dynamic thresholds and adaptive anomaly violation conditions, in order to improve the network anomaly detection effectiveness. The performance evaluation of the proposed overall strategy, in terms of the achievable network traffic prediction accuracy and anomaly detection capability, and the corresponding numerical results demonstrate and quantify the significant improvements that can be achieved