Hidden Anomaly Detection in Telecommunication Networks

Nowadays one of the challenges of telecommunication systems management is to detect in real-time unexpected or hidden malfunctions in extremely complex environments. In this report, we present an on-line algorithm that performs a flow of messages analysis. More precisely, it is able to highlight hidden abnormal behaviors that existing network management methods would not detect. Our algorithm uses the notion of constraint curves, introduced in the Network Calculus theory, defining successive time windows that bound the flow

A feedback based solution to emulate hidden terminals in wireless networks

Mobile wireless emulation allows the test of real applications and transport protocols over a wired network mimicking the behavior of a mobile wireless network (nodes mobility, radio signal propagation and specific communication protocols). Two-stage IP-level network emulation consists in using a dedicated offline simulation stage to compute an IPlevel emulation scenario, which is played subsequently in the emulation stage. While this type of emulation allows the use of accurate computation models together with a large number of nodes, it currently does not allow to deal with dynamic changes of the real traffic. This lack of reactivity makes it impossible to emulate specific wireless behaviors such as hidden terminals in a realistic way. In this paper we address the need to take into account the real traffic during the emulation stage and we introduce a feedback mechanism. During the simulation several emulation scenarios are computed, each scenario corresponding to alternative traffic conditions related to e.g. occurrence or not of hidden terminals. During the emulation stage, the traffic is observed and the currently played emulation scenario can be changed according to specific network conditions. We propose a solution based on multiple scenarios generation, traffic observers and a feedback mechanism to add a trafficbased dynamic behavior to a two-stage emulation platform. The solution will be illustrated with a simple experiment based on hidden terminals

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

Hidden and Uncontrolled - On the Emergence of Network Steganographic Threats

Network steganography is the art of hiding secret information within innocent network transmissions. Recent findings indicate that novel malware is increasingly using network steganography. Similarly, other malicious activities can profit from network steganography, such as data leakage or the exchange of pedophile data. This paper provides an introduction to network steganography and highlights its potential application for harmful purposes. We discuss the issues related to countering network steganography in practice and provide an outlook on further research directions and problems.Comment: 11 page