Traffic analysis is a compound of strategies intended to find relationships, patterns, anomalies, and misconfigurations, among others things, in Internet traffic. In particular, traffic classification is a subgroup of strategies in this field that aims at identifying the application's name or type of Internet traffic. Nowadays, traffic classification has become a challenging task due to the rise of new technologies, such as traffic encryption and encapsulation, which decrease the performance of classical traffic classification strategies. Machine Learning gains interest as a new direction in this field, showing signs of future success, such as knowledge extraction from encrypted traffic, and more accurate Quality of Service management. Machine Learning is fast becoming a key tool to build traffic classification solutions in real network traffic scenarios; in this sense, the purpose of this investigation is to explore the elements that allow this technique to work in the traffic classification field. Therefore, a systematic review is introduced based on the steps to achieve traffic classification by using Machine Learning techniques. The main aim is to understand and to identify the procedures followed by the existing works to achieve their goals. As a result, this survey paper finds a set of trends derived from the analysis performed on this domain; in this manner, the authors expect to outline future directions for Machine Learning based traffic classification.
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Nowadays, the Internet network system serves as a platform for communication, transaction, and entertainment, among others. This communication system is characterized by terrestrial and Satellite components that interact between themselves to provide transmission paths of information between endpoints. Particularly, Satellite Communication providers' interest is to improve customer satisfaction by optimally exploiting on demand available resources and offering Quality of Service (QoS). Improving the QoS implies to reduce errors linked to information loss and delays of Internet packets in Satellite Communications. In this sense, according to Internet traffic (Streaming, VoIP, Browsing, etc.) and those error conditions, the Internet flows can be classified into different sensitive and non-sensitive classes. Following this idea, this work aims at finding new Internet traffic classification approaches to improving the QoS. Machine Learning (ML) and Deep Learning (DL) techniques will be studied and deployed to classify Internet traffic. All the necessary elements to couple an ML or DL solution over a well-known Satellite Communication and QoS management architecture will be evaluated. To develop this solution, a rich and complete set of Internet traffic is required. In this context, an emulated Satellite Communication platform will serve as a data generation environment in which different Internet communications will be launched and captured. The proposed classification system will deal with different Internet communications (encrypted, unencrypted, and tunneled). This system will process the incoming traffic hierarchically to achieve a high classification performance. Finally, some experiments on a cloud emulated platform validates our proposal and set guidelines for its deployment over a Satellite architecture.
Multi-replicA decoding using corRelation baSed LocALisAtion (MARSALA) is a recent random access technique designed for satellite return links. It follows the multiple transmission and interference cancellation scheme of Contention Resolution Diversity Slotted Aloha (CRDSA). In addition, at the receiver side, MARSALA uses autocorrelation to localise replicas of a same packet so as to coherently combine them. Previous work has shown good performance of MARSALA with an assumption of ideal channel state information and perfectly coherent combining of the different replicas of a given packet. However, in a real system, synchronisation errors such as timing offsets and phase shifts between the replicas on separate timeslots will result in less constructive combining of the received signals. This paper describes a method to estimate and compensate the timing and phase differences between the replicas, prior to their combination. Then, the impact of signal misalignment in terms of residual timing offsets and phase shifts, is modeled and evaluated analytically. Finally, the performance of MARSALA in realistic channel conditions is assessed through simulations, and compared to CRDSA in various scenarios.
Abstract-In this paper we propose the introduction of adaptive hybrid automatic repeat request (HARQ) in the context of mobile satellite communications. HARQ schemes which are commonly used in terrestrial links, can be adapted to improve the throughput for delay tolerant services. The proposed method uses the estimation of the mutual information between the received and the sent symbols, in order to estimate the number of bits necessary to decode the message at next transmission. We evaluate the performance of our method by simulating a land mobile satellite (LMS) channel. We compare our results with the static HARQ scheme, showing that our adaptive retransmission technique has better efficiency while keeping an acceptable delay for services.
In recent random access methods used for satellite communications, collisions between packets are not considered as destructive. In fact, to deal with the collision problem, successive interference cancellation is performed at the receiver. Generally, it is assumed that the receiver has perfect knowledge of the interference. In practice, the interference term is affected by the transmission channel parameters, i.e., channel attenuation, timing offsets, frequency offsets and phase shifts, and needs to be accurately estimated and canceled to avoid performance degradation. In this paper, we study the performance of an enhanced channel estimation technique combining estimation using an autocorrelation based method and the Expectation-Maximization algorithm integrated in a joint estimation and decoding scheme. We evaluate the effect of residual estimation errors after successive interference cancellation. To validate our experimental results, we compare them to the Cramer-Rao lower bounds for the estimation of channel parameters in case of superimposed signals.
In this paper we propose a new random access (RA) channel technique for the return link of satellite communications. It concerns slotted transmissions. This proposed method called Shared POsition Technique for Interfered random Transmissions (SPOTiT), is based on a shared knowledge between the receiver and each of the terminals. The shared information is about the time slot locations on which the terminal transmits its replicas as well as the preamble to use. The presented random version of SPOTiT aims to reduce the complexity of replicas localization process of the legacy technique Multireplica Decoding using Correlation based Localisation (MARSALA). It presents a less complex system without degrading performance and with no extra signaling information. Thus, SPOTiT is applied at the same level as MARSALA, i.e. when Contention Resolution Diversity Slotted Aloha (CRDSA) fails in retrieving more packets. This technique combined with CRDSA significantly reduces the number of data localization correlations, while maintaining the same performance as in CRDSA/MARSALA in terms of packet loss ratio and throughput.
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