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Publications

Publications by Ricardo Morla

2017

Early identification of spammers through identity linking, social network and call features

Authors
Azad, MA; Morla, R;

Publication
Journal of Computational Science

Abstract

2016

Long-range trajectories from global and local motion representations

Authors
Pereira, EM; Cardoso, JS; Morla, R;

Publication
JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION

Abstract
Motion is a fundamental cue for scene analysis and human activity understanding in videos. It can be encoded in trajectories for tracking objects and for action recognition, or in form of flow to address behavior analysis in crowded scenes. Each approach can only be applied on limited scenarios. We propose a motion-based system that represents the spatial and temporal features of the flow in terms of I ong-range trajectories. The novelty resides on the system formulation, its generic approach to handle scene variability and motion variations, motion integration from local and global representations, and the resulting long-range trajectories that overcome trajectory-based approach problems. We report the results and conclusions that state its pertinence on different scenarios, comparing and correlating the extracted trajectories of individual pedestrians, manually annotated. We also propose an evaluation framework and stress the diverse system characteristics that can be used for human activity tasks, namely on motion segmentation.

2016

Power interference modeling for CSMA/CA based networks using directional antenna

Authors
Kandasamy, S; Morla, R; Ricardo, M;

Publication
Computer Communications

Abstract

2013

Predicting short 802.11 sessions from RADIUS usage data

Authors
Allandadi, A; Morla, R; Aguiart, A; Cardoso, JS;

Publication
38th Annual IEEE Conference on Local Computer Networks, Sydney, Australia, October 21-24, 2013 - Workshop Proceedings

Abstract
The duration of 802.11 user sessions has been widely studied in the context of analyzing user behavior and mobility. Short (smaller-than-5-minutes) sessions are never used or characterized in these analyses as they are unrelated to user behavior and considered as artifacts introduced by the wireless network. In this paper we characterize short 802.11 sessions as recorded through RADIUS authentication. We show that 50% of access points have 70% of smaller than 5 minutes sessions in a 5 months trace from the Eduroam academic wireless network in the University of Porto. Exactly because they are artifacts introduced by the network, short sessions are an important indicator for network management and the quality of the wireless access. Network managers typically do not collect and process session information but rely on SNMP to provide summaries of 802.11 usage data. We develop a modeling framework to provide predictions for the number of short sessions from SNMP data. We model the data stream of each access point using two methods of regression and one classification technique. We evaluate these models based on short session prediction accuracy. The models are trained on the 5 months data and the best results show prediction accuracy of 95.27% in polynomial regression at degree of 3. © 2013 IEEE.

2013

Caller-REP: Detecting unwanted calls with caller social strength

Authors
Azad, MA; Morla, R;

Publication
COMPUTERS & SECURITY

Abstract
Voice over IP (VoIP) is a cost effective mechanism for telemarketers and criminals to generate bulk spam calls. A challenge in managing a VoIP network is to detect spam calls without user involvement or content analysis. In this paper we present a novel content independent, non-intrusive approach based on caller trust and reputation to block spam callers in a VoIP network. Our approach uses call duration, interaction rate, and caller out-degree distribution to establish a trust network between VoIP users and computes the global reputation of a caller across the network. Our approach uses historical information for automatically determining a global reputation threshold below which a caller is declared as socially non-connected and as a spammer. No VoIP data-set is available for testing the detection mechanism. We verify the accuracy of our approach with synthetic data that we generate by randomly varying the call duration, call rate, and out-degree distributions of spammers and legitimate users. This evaluation shows that our approach can automatically detect spam callers in a network. Our approach achieves a false positive rate of less than 10% and true positive rate of almost 80% in the first two days even in the presence of a significant number of spammers. This increases to a true positive rate of 99% and drops a false positive rate to less than 2% on the third day. In a network with no spammers, our approach achieves a false positive rate of less than 10%. In a network heavily saturated with more than 60% of spam callers, our approach achieves a true positive rate of 98% and no false positives. We compare the performance of our approach with a closely related spam detection approach named Call-Rank. The results show that our approach outperforms Call-Rank in terms of detection accuracy and detection time.

2014

A graph-based approach for interference free integration of commercial off-the-shelf elements in pervasive computing systems

Authors
Soares, C; Moreira, RS; Morla, R; Torres, J; Sobral, P;

Publication
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF GRID COMPUTING AND ESCIENCE

Abstract
Commercial off-the-shelf devices and applications are expected to be pivotal in the coming massive deployment of pervasive computing technology in home settings. The integration of these devices and applications in the same household may result in unplanned interactions involving users and entertainment, communication, and health-related devices and applications. These unplanned interactions are a serious concern when, for example, communication or entertainment applications interfere with the behavior of health-related devices. This paper presents a novel graph-based approach for representing the expected behavior of commercial off-the-shelf devices and applications, their interactions, and for detecting interference in pervasive computing systems. A set of home care scenarios is used to assess the applicability of this approach. We then provide two setups where this approach can be applied: (i) in a pre-deployment setup, where simulation is used to detect possible instances of interference, and (ii) at run-time, collecting observations from devices and applications and detecting interference as it occurs. For pre-deployment and simulation we use Opensim to recreate a home household. For run-time, we use Simple Network Management Protocol for systems state introspection and a sliding window mechanism to process the collected data-stream. Crown Copyright

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