1,721,045 research outputs found
Fine-Grained Traffic Prediction of Communication-and-Collaboration Apps Via Deep-Learning: A First Look at Explainability
No full textThe lifestyle change originated from the COVID-19 pandemic has caused a measurable impact on Internet traffic in terms of volume and application mix, with a sudden increase in usage of communication-and-collaboration apps. In this work, we focus on four of these apps (Skype, Teams, Webex, and Zoom), whose traffic we collect, reliably label at fine (i.e. per-activity) granularity, and analyze from the viewpoint of traffic prediction. The outcome of this analysis is informative for a number of network management tasks, including monitoring, planning, resource provisioning, and (security) policy enforcement. To this aim, we employ state-of-the-art multitask deep learning approaches to assess to which degree the traffic generated by these apps and their different use cases (i.e. activities: audio-call, video-call, and chat) can be forecast at packet level. The experimental analysis investigates the performance of the considered deep learning architectures, in terms of both traffic-prediction accuracy and complexity, and the related trade-off. Equally important, our work is a first attempt at interpreting the results obtained by these predictors via eXplainable Artificial Intelligence (XAI)
A Survey on Information and Communication Technologies for Industry 4.0: State-of-the-Art, Taxonomies, Perspectives, and Challenges
No full textA new industrial revolution is undergoing, based on a number of technological paradigms. The will to foster and guide this phenomenon has been summarized in the expression “Industry 4.0” (I4.0). Initiatives under this term share the vision that many key technologies underlying Cyber-Physical Systems and Big Data Analytics are converging to a new distributed, highly automated, and highly dynamic production network, and that this process needs regulatory and cultural advancements to effectively and timely develop. In this work, we focus on the technological aspect only, highlighting the unprecedented complexity of I4.0 emerging from the scientific literature. While previous works have focused on one or up to four related enablers, we consider ten technological enablers, including besides the most cited Big Data, Internet of Things, and Cloud Computing, also others more rarely considered as Fog and Mobile Computing, Artificial Intelligence, Human-Computer Interaction, Robotics, down to the often overlooked, very recent, or taken for granted Open-Source Software, Blockchain, and the Internet. For each we explore the main characteristics in relation to I4.0 and its interdependencies with other enablers. Finally we provide a detailed analysis of challenges in leveraging each of the enablers in I4.0, evidencing possible roadblocks to be overcome and pointing at possible future directions of research. Our goal is to provide a reference for the experts in some of the technological fields involved, for a reconnaissance of integration and hybridization possibilities with other fields in the endeavor of I4.0, as well as for the laymen, for a high-level grasp of the variety (and often deep history) of the scientific research backing I4.0
A Secure Adaptive Control for Cooperative Driving of Autonomous Connected Vehicles in the Presence of Heterogeneous Communication Delays and Cyberattacks
No full textThe development of autonomous connected vehicles, moving as a platoon formation, is a hot topic in the intelligent transportation system (ITS) research field. It is on the road and deployment requires the design of distributed control strategies, leveraging secure vehicular ad-hoc networks (VANETs). Indeed, wireless communication networks can be affected by various security vulnerabilities and cyberattacks leading to dangerous implications for cooperative driving safety. Control design can play an important role in providing both resilience and robustness to vehicular networks. To this aim, in this article, we tackle and solve the problem of cyber-secure tracking for a platoon that moves as a cohesive formation along a single lane undergoing different kinds of cyber threats, that is, application layer and network layer attacks, as well as network induced phenomena. The proposed cooperative approach leverages an adaptive synchronization-based control algorithm that embeds a distributed mitigation mechanism of malicious information. The closed-loop stability is analytically demonstrated by using the Lyapunov-Krasovskii theory, while its effectiveness in coping with the most relevant type of cyber threats is disclosed by using PLEXE, a high fidelity simulator which provides a realistic simulation of cooperative driving systems
A simulation environment for an experimental analysis of multiple traffic scheduling algorithms over GPRS networks
No full textOrlando, Florida (USA
Distributed Internet Traffic Generator (D-ITG): analysis and experimentation over heterogeneous networks
No full textPoster Proceeding
A simulation environment for an experimental analysis of multiple traffic scheduling algorithms over GPRS networks
No full textOrlando, Florida (USA
AI-powered Internet Traffic Classification: Past, Present, and Future
No full textTraffic classification (TC) is pivotal for network traffic management and security. Over time, TC solutions leveraging Artificial Intelligence (AI) have undergone significant advancements, primarily fueled by Machine Learning (ML). This paper analyzes the history and current state of AI-powered TC on the Internet, highlighting unresolved research questions. Indeed, despite extensive research, key desiderata goals to product-line implementations remain. AI presents untapped potential for addressing the complex and evolving challenges of TC, drawing from successful applications in other domains. We identify novel ML topics and solutions that address unmet TC requirements, shaping a comprehensive research landscape for the TC future. We also discuss the interdependence of TC desiderata and identify obstacles hindering AI-powered next-generation solutions. Overcoming these roadblocks will unlock two intertwined visions for future networks: self-managed and human-centered networks
A hierarchical hybrid intrusion detection approach in IoT scenarios
No full textInternet of Things (IoT) fosters unprecedented network heterogeneity and dynamicity, thus increasing the variety and the amount of related vulnerabilities. Hence, traditional security approaches fall short, also in terms of resulting scalability and privacy. In this paper we propose H2ID, a two-stage hierarchical Network Intrusion Detection approach. H2ID performs (i) anomaly detection via a novel lightweight solution based on a MultiModal Deep AutoEncoder (M2-DAE), and (ii) attack classification, using soft-output classifiers. We validate our proposal using the recently-released Bot-IoT dataset, inferring among four relevant categories of attack (DDoS, DoS, Scan, and Theft) and unknown attacks. Results show gains of the proposed M2-DAE in the case of simple anomaly detection (up to -40% false-positive rate when compared with several baselines at same true positive rate) and for H2ID as a whole when compared to the best-performing misuse detector approach (up to ≈ +5% F1 score). Besides the performance advantages, our system is suitable for distributed and privacy-preserving deployments while limiting re-training necessities, in line with the high efficiency as well as the flexibility required in IoT scenarios
Cross-Evaluation of Deep Learning-based Network Intrusion Detection Systems
No full textNetwork Intrusion Detection Systems are essential tools for protecting networks against attacks. Deep Learning approaches are increasingly employed in developing these systems due to their versatility and effectiveness. However, the common procedure for training and testing Deep Learning models typically leverages traffic data entirely collected from the operational network managed by a single organization, posing privacy and security concerns in sharing these data. As a result, the assessment of the performance of these models in real-world scenarios is significantly hindered. On the other hand, given the wide variety of existing attacks and the emergence of new attack types, it is crucial to evaluate the robustness of Intrusion Detection Systems when the network context varies. Indeed, it is highly desirable that the effectiveness of trained Deep Learning models is not severely impacted when ported into other networks.To this aim, in this work, we exploit various single-modal and multimodal Deep Learning approaches and leverage a cross-evaluation procedure to assess their capability to distinguish malicious from benign traffic in different network contexts. Furthermore, we investigate the impact of various informative fields extracted from traffic on the generalization capability of models. Our cross-evaluation leverages three recent public-available network attack datasets related to diverse scenarios. The results obtained suggest that the availability at training time of traffic generated by attacks conducted in the operational network is crucial for designing a robust Intrusion Detection System that keeps working with minimal Fl-score degradation, when the network context changes
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