Genetic Algorithm Modeling with GPU Parallel Computing Technology

We present a multi-purpose genetic algorithm, designed and implemented with GPGPU / CUDA parallel computing technology. The model was derived from a multi-core CPU serial implementation, named GAME, already scientifically successfully tested and validated on astrophysical massive data classification problems, through a web application resource (DAMEWARE), specialized in data mining based on Machine Learning paradigms. Since genetic algorithms are inherently parallel, the GPGPU computing paradigm has provided an exploit of the internal training features of the model, permitting a strong optimization in terms of processing performances and scalability.Comment: 11 pages, 2 figures, refereed proceedings; Neural Nets and Surroundings, Proceedings of 22nd Italian Workshop on Neural Nets, WIRN 2012; Smart Innovation, Systems and Technologies, Vol. 19, Springe

Topology Discovery at the Router Level: a New Hybrid Tool Targeting ISP Networks

For a long time, traceroute measurements combined with alias resolution methods have been the sole way to collect Internet router level maps. Recently, a new approach has been introduced with the use of a multicast management tool, mrinfo, and a recursive probing scheme. In this paper, after analyzing advantages and drawbacks of probing approaches based on traceroute and mrinfo, we propose a hybrid discovery tool, MERLIN (MEasure the Router Level of the INternet), mixing mrinfo and traceroute probes. Using a central server controlling a set of distributed vantage points in order to increase the exploration coverage while limiting the probing redundancy, the purpose of MERLIN is to provide an accurate router level map inside a targeted Autonomous System (AS). MERLIN also takes advantage of alias resolution methods to reconnect scattered mul- ticast components. To evaluate the performance of MERLIN, we report experimental results describing its efficiency in topology exploration and reconstruction of several ASes

Quantifying and Mitigating IGMP Filtering in Topology Discovery

peer reviewedRecent developments in router level topology discovery have suggested the introduction of IGMP probing in addition to standard techniques such as traceroute and alias resolution. With a single IGMP probe, one can obtain all multicast interfaces and links of a multicast router. If such a probing is a promising approach, we noticed that IGMP probes are subject to filtering, leading so to the fragmentation of the collected multicast graph into several disjoint connected components. In this paper, we cope with the fragmentation issue. Our contributions are threefold: (i) we experimentally quantify the damages caused by IGMP filtering on collected topologies of large tier-1 ISPs; (ii) using traceroute data, we construct a hybrid graph and estimate how far each IGMP fragment is from each other; (iii) we provide and experimentally evaluate a recursive approach for reconnecting disjoint multicast components. The key idea of the third contribution is to recursively apply alias resolution to reassemble disjoint fragments and, thus, progressively extend the mapping of the targeted ISP. Data presented in the paper, as well as reconstructed topologies, are freely available at http://svnet.u-strasbg.fr/merlin

Toward Effective Mobile Encrypted Traffic Classification through Deep Learning

Traffic Classification (TC), consisting in how to infer applications generating network traffic, is currently the enabler for valuable profiling information, other than being the workhorse for service differentiation/blocking. Further, TC is fostered by the blooming of mobile (mostly encrypted) traffic volumes, fueled by the huge adoption of hand-held devices. While researchers and network operators still rely on machine learning to pursue accurate inference, we envision Deep Learning (DL) paradigm as the stepping stone toward the design of practical (and effective) mobile traffic classifiers based on automatically-extracted features, able to operate with encrypted traffic, and reflecting complex traffic patterns. In this context, the paper contribution is fourfold. First, it provides a taxonomy of the key network traffic analysis subjects where DL is foreseen as attractive. Secondly, it delves into the non-trivial adoption of DL to mobile TC, surfacing potential gains. Thirdly, to capitalize such gains, it proposes and validates a general framework for DL-based encrypted TC. Two concrete instances originating from our framework are then experimentally evaluated on three mobile datasets of human users’ activity. Lastly, our framework is leveraged to point to future research perspectives

A collaborative approach for improving the security of vehicular scenarios: The case of platooning

Autonomous vehicles platooning has received considerable attention in recent years, due to its potential to significantly benefit road transportation, improving traffic efficiency, enhancing road safety and reducing fuel consumption. The Vehicular ad hoc Networks and the de facto vehicular networking standard IEEE 802.11p communication protocol are key tools for the deployment of platooning applications, since the cooperation among vehicles is based on a reliable communication structure. However, vehicular networks can suffer different security threats. Indeed, in collaborative driving applications, the sudden appearance of a malicious attack can mainly compromise: (i) the correctness of data traffic flow on the vehicular network by sending malicious messages that alter the platoon formation and its coordinated motion; (ii) the safety of platooning application by altering vehicular network communication capability. In view of the fact that cyber attacks can lead to dangerous implications for the security of autonomous driving systems, it is fundamental to consider their effects on the behavior of the interconnected vehicles, and to try to limit them from the control design stage. To this aim, in this work we focus on some relevant types of malicious threats that affect the platoon safety, i.e. application layer attacks (Spoofing and Message Falsification) and network layer attacks (Denial of Service and Burst Transmission), and we propose a novel collaborative control strategy for enhancing the protection level of autonomous platoons. The control protocol is designed and validated in both analytically and experimental way, for the appraised malicious attack scenarios and for different communication topology structures. The effectiveness of the proposed strategy is shown by using PLEXE, a state of the art inter-vehicular communications and mobility simulator that includes basic building blocks for platooning. A detailed experimental analysis discloses the robustness of the proposed approach and its capabilities in reacting to the malicious attack effects

Industry 4.0 and Health: Internet of Things, Big Data, and Cloud Computing for Healthcare 4.0

Industry 4.0 and its main enabling information and communication technologies are completely changing both services and production worlds. This is especially true for the health domain, where the Internet of Things, Cloud and Fog Computing, and Big Data technologies are revolutionizing eHealth and its whole ecosystem, moving it towards Healthcare 4.0. By selectively analyzing the literature, we systematically survey how the adoption of the above-mentioned Industry 4.0 technologies (and their integration) applied to the health domain is changing the way to provide traditional services and products. In this paper, we provide (i) a description of the main technologies and paradigms in relation to Healthcare 4.0 and discuss (ii) their main application scenarios; we then provide an analysis of (iii) carried benefits and (iv) novel cross-disciplinary challenges; finally, we extract (v) the lessons learned