Mining Network Events using Traceroute Empathy

In the never-ending quest for tools that enable an ISP to smooth troubleshooting and improve awareness of network behavior, very much effort has been devoted in the collection of data by active and passive measurement at the data plane and at the control plane level. Exploitation of collected data has been mostly focused on anomaly detection and on root-cause analysis. Our objective is somewhat in the middle. We consider traceroutes collected by a network of probes and aim at introducing a practically applicable methodology to quickly spot measurements that are related to high-impact events happened in the network. Such filtering process eases further in- depth human-based analysis, for example with visual tools which are effective only when handling a limited amount of data. We introduce the empathy relation between traceroutes as the cornerstone of our formal characterization of the traceroutes related to a network event. Based on this model, we describe an algorithm that finds traceroutes related to high-impact events in an arbitrary set of measurements. Evidence of the effectiveness of our approach is given by experimental results produced on real-world data.Comment: 8 pages, 7 figures, extended version of Discovering High-Impact Routing Events using Traceroutes, in Proc. 20th International Symposium on Computers and Communications (ISCC 2015

Role of sustainability attributes and price in determining consumers' fruit perceived value

This work analyses consumers' behaviour and attitudes toward products characterised by a reduced environmental impact in terms of carbon footprint (CF). Value perception was measured using a contingent valuation approach, asking consumers to state their willingness to accept (WTA) monetary compensation for a product exchange offer, particularly fruit characterised by a higher CF in place of fruit characterised by a lower CF. Field experiments were conducted to determine consumers WTA as well as factors affecting the choice. Consumers were hypothetically endowed with a punnet of fruit produced with innovative, low CF farming methods and were offered to exchange it with a punnet of regular fruit. Variables representing consumer fruit consumption habits, consumer attitude and concern towards the environment, and socio-demographics were chosen to represent factors that motivate consumers' value perception of environmentally-friendly fruit. The scale of green consumption values (GCVs) was used to model consumer concern towards the environment. Results showed that demographics affect the perceived value of fruit characterised by a lower CF and that consumers' preference for lower CF products is associated with fruit consumption habits and environmental concerns. At the same time, a positive relationship with CF levels needs further investigation

Consumer attitudes and value perception for fruit with a lower carbon footprint

This work analyses consumers' attitudes for products characterised by a reduced environmental impact on carbon footprint (CF). Value perception was measured using a contingent valuation approach, asking consumers to state their willingness to accept (WTA) monetary compensation for a product exchange offer. Consumers were hypothetically endowed with a punnet of fruit produced with innovative, low CF farming methods and was offered to exchange it with a punnet of regular fruit. Results showed that demographics affect the value associated with fruit with a lower CF and that consumers' preference for lower CF products is associated with fruit consumption habits and environmental concern. At the same time, a positive relationship with CF levels needs further investigation

Verifying Data Secure Flow in AUTOSAR Models by Static Analysis

This paper presents a method to check data secure flow in security annotated AUTOSAR models. The approach is based on information flow analysis and abstract interpretation. The analysis computes the lowest security level of data sent on a communication, according to the annotations in the model and the code of runnables. An abstract interpreter executes runnables on abstract domains that abstract from real values and consider only data dependency levels. Data secure flow is verified if data sent on a communication always satisfy the security annotation in the model. The work has been developed in the EU project Safure, where modeling extensions to AUTOSAR have been proposed to improve security in automotive communications

Computational Complexity of Traffic Hijacking under BGP and S-BGP

Harmful Internet hijacking incidents put in evidence how fragile the Border Gateway Protocol (BGP) is, which is used to exchange routing information between Autonomous Systems (ASes). As proved by recent research contributions, even S-BGP, the secure variant of BGP that is being deployed, is not fully able to blunt traffic attraction attacks. Given a traffic flow between two ASes, we study how difficult it is for a malicious AS to devise a strategy for hijacking or intercepting that flow. We show that this problem marks a sharp difference between BGP and S-BGP. Namely, while it is solvable, under reasonable assumptions, in polynomial time for the type of attacks that are usually performed in BGP, it is NP-hard for S-BGP. Our study has several by-products. E.g., we solve a problem left open in the literature, stating when performing a hijacking in S-BGP is equivalent to performing an interception.Comment: 17 pages with 6 figure

Verifying data secure flow in AUTOSAR models

This paper presents an approach for enhancing the design phase of AUTOSAR models when security annotations are required. The approach is based on information flow analysis and abstract interpretation. The analysis evaluates the correctness of the model by assessing if the flow of data is secure with respect to causal data dependencies within the model. To find these dependencies an exhaustive search through the model would be required. Abstract interpretation is used as a trade-off between the precision and complexity of the analysis. The approach also provides annotated models without oversizing the set of annotations

Schematic Representation of Large Biconnected Graphs

Suppose that a biconnected graph is given, consisting of a large component plus several other smaller components, each separated from the main component by a separation pair. We investigate the existence and the computation time of schematic representations of the structure of such a graph where the main component is drawn as a disk, the vertices that take part in separation pairs are points on the boundary of the disk, and the small components are placed outside the disk and are represented as non-intersecting lunes connecting their separation~pairs. We consider several drawing conventions for such schematic representations, according to different ways to account for the size of the small components. We map the problem of testing for the existence of such representations to the one of testing for the existence of suitably constrained $1$-page book-embeddings and propose several polynomial-time and pseudo-polynomial-time algorithms.Comment: Appears in the Proceedings of the 28th International Symposium on Graph Drawing and Network Visualization (GD 2020

Solar axions cannot explain the XENON1T excess

We argue that the interpretation in terms of solar axions of the recent XENON1T excess is not tenable when confronted with astrophysical observations of stellar evolution. We discuss the reasons why the emission of a flux of solar axions sufficiently intense to explain the anomalous data would radically alter the distribution of certain type of stars in the color-magnitude diagram in first place, and would also clash with a certain number of other astrophysical observables. Quantitatively, the significance of the discrepancy ranges from $3.3\sigma$ for the rate of period change of pulsating White Dwarfs, and exceedes $19\sigma$ for the $R$-parameter and for $M_{I,{\rm TRGB}}$.Comment: 6 pages, 2 figures, 1 table. Version accepted for publication on PR

GloNets: Globally Connected Neural Networks

Deep learning architectures suffer from depth-related performance degradation, limiting the effective depth of neural networks. Approaches like ResNet are able to mitigate this, but they do not completely eliminate the problem. We introduce Globally Connected Neural Networks (GloNet), a novel architecture overcoming depth-related issues, designed to be superimposed on any model, enhancing its depth without increasing complexity or reducing performance. With GloNet, the network's head uniformly receives information from all parts of the network, regardless of their level of abstraction. This enables GloNet to self-regulate information flow during training, reducing the influence of less effective deeper layers, and allowing for stable training irrespective of network depth. This paper details GloNet's design, its theoretical basis, and a comparison with existing similar architectures. Experiments show GloNet's self-regulation ability and resilience to depth-related learning challenges, like performance degradation. Our findings suggest GloNet as a strong alternative to traditional architectures like ResNets