E-Business and Telecommunications

Retailers offer their products via e-Commerce applications in order to promote them better, globalize their clientèle, enhance and support sales. This paper explores and proposes techniques that will collect data from the physical shopping floor mainly via low cost mobile technologies and promote them for processing to a customized sophisticated e-Commerce Analyzer. This Analyzer connects the two worlds of physical and virtual shopping. The information is collected automatically during the physical store customer interaction with the support of iBeacons and Near Field Communications, used for data acquisition in the retail shop floor. The generated retail data is pushed to the Analyzer in near real time. This data enhances the Analyzer input with physical store data and can be used in order to enrich the customer experience, provide customer clustering and precise behavioral analysis, by enhancing the available operational data sets, collected by a Web Analytics application, providing better overview and supporting decision making for the entire corporation. The consumer has a choice to shop on-line from home via a browser or mobile app or go alternatively in person to a store and accomplish a shopping goal. The latter is usually without the need of any on-line device. The two approaches are not integrated into one system. The consumer always seeks value for money to satisfy his need, also to be inspired and to have a great uplifting shopping experience. The store requires increasing profits while trying to excel in satisfying any customer requirement. The future customer will always be connected. The customer will enjoy a shopping experience, receiving personalized services leading to building loyalty and trust with the shop. This vision of the future is based on the interplay of a set of technologies which will combine the interaction of mobile data with store items in real time. This merging not only must it work well, but also be well optimized. The talk will cover the marriage of the local store together with the store electronic presence (e-shop) into one whole, smoothly operating system, from both the administrator of the system point of view and the customer. We envisage developments in physical shopping where sensors intelligently will interact, using a number of devices and techniques to assist this process, such as mobile phones or other devices using audio directions. A real time customer servicing system will be coupled to the e-shop system of the company business. The use of web Analytics and User Profiling is crucial to e-shopping in identifying customer profiles and to offer vital statistics to the management for the state of the business as well as traffic, speed, throughput diagrams and faults of the e-shop in need of improvement. This paper will © Springer International Publishing Switzerland 2016 M.S. Obaidat and P. Lorenz (Eds.): ICETE 2015, CCIS 585, pp. 3–35, 2016. DOI: 10.1007/978-3-319-30222-5_1 describe an architecture we envisage for this holistic system of managing both e-shop as well as physical shop offering good prospects for profitable management for a 24/7 service. The system requirements for real time or near real time Analytics also for the physical shopping mode, sets new requirements for the sensory interpretation and data fetching/pushing into devices. A real time customer servicing application must immediately provide offers and discounted articles in conjunction with the location of the customer in the store. Since the lifetime of the customer needs is short, the system must identify the customer profile, guess the customer need, process the need and push for an inspiring solution to this need to the customer device, before the customer has walked passed this product range, ideally. The talk will describe the status of our architecture for integrated shopping, our research on Web Analytics, Intelligent sensing and other complementary technologies and applications required for shopping experiences of the future [1].

[1]  Hao Chen,et al.  AndroidLeaks: Automatically Detecting Potential Privacy Leaks in Android Applications on a Large Scale , 2012, TRUST.

[2]  Mike Hibler,et al.  The Flask Security Architecture: System Support for Diverse Security Policies , 1999, USENIX Security Symposium.

[3]  L. Cavallaro,et al.  A System Call-Centric Analysis and Stimulation Technique to Automatically Reconstruct Android Malware Behaviors , 2013 .

[4]  Mark Stamp,et al.  Profile hidden Markov models and metamorphic virus detection , 2009, Journal in Computer Virology.

[5]  Gail-Joon Ahn,et al.  Visualization-based policy analysis for SELinux: framework and user study , 2012, International Journal of Information Security.

[6]  Haibo Shen.  A Semantic-Aware Attribute-Based Access Control Model for Web Services , 2009, ICA3PP.

[7]  Byung-Gon Chun,et al.  TaintDroid: An Information-Flow Tracking System for Realtime Privacy Monitoring on Smartphones , 2010, OSDI.

[8]  Gerardo Canfora,et al.  A Classifier of Malicious Android Applications , 2013, 2013 International Conference on Availability, Reliability and Security.

[9]  Daniel Bilar,et al.  Opcodes as predictor for malware , 2007, Int. J. Electron. Secur. Digit. Forensics.

[10]  M. Masrom,et al.  Opcodes histogram for classifying metamorphic portable executables malware , 2012, 2012 International Conference on E-Learning and E-Technologies in Education (ICEEE).

[11]  Konrad Rieck,et al.  DREBIN: Effective and Explainable Detection of Android Malware in Your Pocket , 2014, NDSS.

[12]  Babak Bashari Rad,et al.  Metamorphic Virus Variants Classification Using Opcode Frequency Histogram , 2011, ArXiv.

[13]  Mark Stamp,et al.  Structural entropy and metamorphic malware , 2013, Journal of Computer Virology and Hacking Techniques.

[14]  C. R. Ramakrishnan,et al.  Symbolic reachability analysis for parameterized administrative role-based access control , 2011, Comput. Secur..

[15]  Jin Tong,et al.  Attributed based access control (ABAC) for Web services , 2005, IEEE International Conference on Web Services (ICWS'05).

[16]  Julian Schütte,et al.  On the Effectiveness of Malware Protection on Android An evaluation of Android antivirus , 2013 .

[17]  Ninghui Li,et al.  Using probabilistic generative models for ranking risks of Android apps , 2012, CCS.

[18]  Robert N. M. Watson,et al.  A decade of OS access-control extensibility , 2013, CACM.

[19]  Anthony Desnos,et al.  Android: Static Analysis Using Similarity Distance , 2012, 2012 45th Hawaii International Conference on System Sciences.

[20]  John C. S. Lui,et al.  Droid Analytics: A Signature Based Analytic System to Collect, Extract, Analyze and Associate Android Malware , 2013, 2013 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications.

[21]  Stephen Smalley,et al.  Configuring the SELinux Policy , 2008 .

[22]  Luigi V. Mancini,et al.  Towards a formal model for security policies specification and validation in the selinux system , 2004, SACMAT '04.

[23]  Srdjan Capkun,et al.  Application Collusion Attack on the Permission-Based Security Model and its Implications for Modern Smartphone Systems , 2010 .

[24]  Stephen Smalley,et al.  Security Enhanced (SE) Android: Bringing Flexible MAC to Android , 2013, NDSS.

[25]  Ninghui Li,et al.  The Foundational Work of Harrison-Ruzzo-Ullman Revisited , 2013, IEEE Transactions on Dependable and Secure Computing.

[26]  Patrick Traynor,et al.  MAST: triage for market-scale mobile malware analysis , 2013, WiSec '13.

[27]  Hahn-Ming Lee,et al.  DroidMat: Android Malware Detection through Manifest and API Calls Tracing , 2012, 2012 Seventh Asia Joint Conference on Information Security.

[28]  Latifur Khan,et al.  A Machine Learning Approach to Android Malware Detection , 2012, 2012 European Intelligence and Security Informatics Conference.

[29]  Arun Lakhotia,et al.  Using engine signature to detect metamorphic malware , 2006, WORM '06.

[30]  Michael Franz,et al.  Fine-Grained Information Flow Analysis and Enforcement in a Java Virtual Machine , 2007, Twenty-Third Annual Computer Security Applications Conference (ACSAC 2007).