Biblio

Todays' era of internet-of-things, cloud computing and big data centers calls for more fresh graduates with expertise in digital data processing techniques such as compression, encryption and error correcting codes. This paper describes a project-based elective that covers these three main digital data processing techniques and can be offered to three different undergraduate majors electrical and computer engineering and computer science. The course has been offered successfully for three years. Registration statistics show equal interest from the three different majors. Assessment data show that students have successfully completed the different course outcomes. Students' feedback show that students appreciate the knowledge they attain from this elective and suggest that the workload for this course in relation to other courses of equal credit is as expected.

Feedback loss can severely degrade the overall system performance, in addition, it can affect the control and computation of the Cyber-physical Systems (CPS). CPS hold enormous potential for a wide range of emerging applications including stochastic and time-critical traffic patterns. Stochastic data has a randomness in its nature which make a great challenge to maintain the real-time control whenever the data is lost. In this paper, we propose a data recovery scheme, called the Efficient Temporal and Spatial Data Recovery (ETSDR) scheme for stochastic incomplete feedback of CPS. In this scheme, we identify the temporal model based on the traffic patterns and consider the spatial effect of the nearest neighbor. Numerical results reveal that the proposed ETSDR outperforms both the weighted prediction (WP) and the exponentially weighted moving average (EWMA) algorithm regardless of the increment percentage of missing data in terms of the root mean square error, the mean absolute error, and the integral of absolute error.
 

Recently, threat of previously unknown cyber-attacks are increasing because existing security systems are not able to detect them. Past cyber-attacks had simple purposes of leaking personal information by attacking the PC or destroying the system. However, the goal of recent hacking attacks has changed from leaking information and destruction of services to attacking large-scale systems such as critical infrastructures and state agencies. In the other words, existing defence technologies to counter these attacks are based on pattern matching methods which are very limited. Because of this fact, in the event of new and previously unknown attacks, detection rate becomes very low and false negative increases. To defend against these unknown attacks, which cannot be detected with existing technology, we propose a new model based on big data analysis techniques that can extract information from a variety of sources to detect future attacks. We expect our model to be the basis of the future Advanced Persistent Threat(APT) detection and prevention system implementations.

The success of the IoT world requires service provision attributed with ubiquity, reliability, high-performance, efficiency, and scalability. In order to accomplish this attribution, future business and research vision is to merge the Cloud Computing and IoT concepts, i.e., enable an “Everything as a Service” model: specifically, a Cloud ecosystem, encompassing novel functionality and cognitive-IoT capabilities, will be provided. Hence the paper will describe an innovative IoT centric Cloud smart infrastructure addressing individual IoT and Cloud Computing challenges.
 

Multi-touch attribution, which allows distributing the credit to all related advertisements based on their corresponding contributions, has recently become an important research topic in digital advertising. Traditionally, rule-based attribution models have been used in practice. The drawback of such rule-based models lies in the fact that the rules are not derived form the data but only based on simple intuition. With the ever enhanced capability to tracking advertisement and users' interaction with the advertisement, data-driven multi-touch attribution models, which attempt to infer the contribution from user interaction data, become an important research direction. We here propose a new data-driven attribution model based on survival theory. By adopting a probabilistic framework, one key advantage of the proposed model is that it is able to remove the presentation biases inherit to most of the other attribution models. In addition to model the attribution, the proposed model is also able to predict user's 'conversion' probability. We validate the proposed method with a real-world data set obtained from a operational commercial advertising monitoring company. Experiment results have shown that the proposed method is quite promising in both conversion prediction and attribution.

Threats to modern ICT systems are rapidly changing these days. Organizations are not mainly concerned about virus infestation, but increasingly need to deal with targeted attacks. This kind of attacks are specifically designed to stay below the radar of standard ICT security systems. As a consequence, vendors have begun to ship self-learning intrusion detection systems with sophisticated heuristic detection engines. While these approaches are promising to relax the serious security situation, one of the main challenges is the proper evaluation of such systems under realistic conditions during development and before roll-out. Especially the wide variety of configuration settings makes it hard to find the optimal setup for a specific infrastructure. However, extensive testing in a live environment is not only cumbersome but usually also impacts daily business. In this paper, we therefore introduce an approach of an evaluation setup that consists of virtual components, which imitate real systems and human user interactions as close as possible to produce system events, network flows and logging data of complex ICT service environments. This data is a key prerequisite for the evaluation of modern intrusion detection and prevention systems. With these generated data sets, a system's detection performance can be accurately rated and tuned for very specific settings.