Mario Lemes Proença

A mobile health monitoring solution for weight control

Obesity is a serious public health concern in the current society, mainly, in developed countries. Common and key treatments for obesity include dieting and frequent physical activity. But more important, it requires a strong individual discipline, motivation, and constant monitoring of the food intake. This paper presents SapoFitness, a mobile health application for a dietetic monitoring and assessment. SapoFitness is customized to its user keeping a daily personal health record (PHR) of his/her food intake and daily exercise. This PHR contains vital health information that will evaluate the nutritional state of the patient (user). The application supports continuous user monitoring and sends alerts/messages concerning his/her diet program taking into account his/her physical activity. SapoFitness is a challenged mobile application that delivers the action to the user, anytime and anywhere, motivating him for a healthier life style. The proposed system was evaluated in several Android-based mobile devices and it is ready for use.

Autonomous profile-based anomaly detection system using principal component analysis and flow analysis

An original anomaly detection system using principal component analysis is proposed.Our system was evaluated using real traffic data from a university.PCA proved effective in creating a digital signature of network traffic.Results pertaining to false alarm and accuracy rate are encouraging.Network anomalies were efficiently identified by our approach. Different techniques and methods have been widely used in the subject of automatic anomaly detection in computer networks. Attacks, problems and internal failures when not detected early may badly harm an entire Network system. Thus, an autonomous anomaly detection system based on the statistical method principal component analysis (PCA) is proposed. This approach creates a network profile called Digital Signature of Network Segment using Flow Analysis (DSNSF) that denotes the predicted normal behavior of a network traffic activity through historical data analysis. That digital signature is used as a threshold for volume anomaly detection to detect disparities in the normal traffic trend. The proposed system uses seven traffic flow attributes: bits, packets and number of flows to detect problems, and source and destination IP addresses and Ports, to provides the network administrator necessary information to solve them. Via evaluation techniques performed in this paper using real network traffic data, results showed good traffic prediction by the DSNSF and encouraging false alarm generation and detection accuracy on the detection schema using thresholds.

A seven-dimensional flow analysis to help autonomous network management

Abstract Due to the increasing need of more agility in information exchange, computer networks are continuously expanding both in magnitude and complexity of the management processes. An essential component of these processes is the anomaly detection and identification. Although there are several studies in this area, simple and efficient anomaly detection mechanisms are still required due to the lack of suitable approaches for large-scale network environments. In this paper, we present an anomaly detection system using a seven-dimensional flow analysis. The core of this system is composed by the Holt–Winters for Digital Signature (HWDS) method, an improvement of the traditional Holt–Winters, which characterizes the traffic of each one of the analyzed dimensions in order to generate profiles able to describe the network’s normal behavior, here called Digital Signature of Network Segment using Flow analysis (DSNSF). The low complexity of the presented approach enables fast anomaly detection, mitigating the impact on final users. The system not only warns the network administrator about the problem, but also provides the necessary information to identify and solve it. To measure the efficiency and accuracy of the system, we use real data collected from a large-scale network environment.

Unsupervised learning clustering and self-organized agents applied to help network management

Self-organized agents use multidimensional flow analysis to help network management.Traffic profiling and anomaly detection tasks are designed to operate autonomously.Reports are provided in real time to aid decision-making when anomalous events occur.A pattern matching technique calculates adaptive thresholds for anomaly detection.False alarm and accuracy rates are encouraging both in real and simulated traffic. Traffic monitoring and anomaly detection are essential activities for computer network management, since they provide relevant information about its current performance and contribute to network control. Although there are several studies in this area, diagnosis and resolution of anomalies are still challenging issues. From an expert system point of view, current solutions have not been sufficient to meet the requirements demanded for use in large-scale network environments, and thus a significant portion of budgets on the workforce are spent to network management. Based on this context, the focus of this paper consists of the development of a system able to proactively monitor the network and detect anomalous events, reducing manual intervention and the probability of errors in decision-making, regarding network management. The proposed approach characterizes the normal pattern of the network traffic and detects anomalous behavior, outage events and attacks by deviations from this pattern. For this purpose, an unsupervised learning methodology is used to extract features of traffic through IP flows attributes, collected from a network structure. Aiming to improve its efficiency, a modification of the Ant Colony Optimization metaheuristic is proposed, which through self-organized agents optimizes the analysis of multidimensional flows attributes and allows it to be completed in time to mitigate the impact on large-scale networks. In addition to notify the network manager about the anomalies, the system provides necessary information to identify and take action against them. The resulting detection system was tested with real and simulated data, achieving high detection rates while the false alarm rate remains low.

Anomaly Detection Aiming Pro-Active Management of Computer Network Based on Digital Signature of Network Segment

Detecting anomalies accurately is fundamental to rapid diagnosis and repair of problems. This paper proposes a novel Anomaly detection system based on the comparison of real traffic and DSNS (Digital Signature of Network Segment), generated by BLGBA (Baseline for Automatic Backbone Management) model, within a hysteresis interval using the residual mean and on the correlation of the detected deviations. Extensive experimental results on real network servers confirmed that our system is able to detect anomalies on the monitored devices, avoiding the high false alarms rate.

Network anomaly detection using IP flows with Principal Component Analysis and Ant Colony Optimization

It is remarkable how proactive network management is in such demand nowadays, since networks are growing in size and complexity and Information Technology services cannot be stopped. In this manner, it is necessary to use an approach which proactively identifies traffic behavior patterns which may harm the networks normal operations. Aiming an automated management to detect and prevent potential problems, we present and compare two novel anomaly detection mechanisms based on statistical procedure Principal Component Analysis and the Ant Colony Optimization metaheuristic. These methods generate a traffic profile, called Digital Signature of Network Segment using Flow analysis (DSNSF), which is adopted as normal network behavior. Then, this signature is compared with the real network traffic by using a modification of the Dynamic Time Warping metric in order to recognize anomalous events. Thus, a seven-dimensional analysis of IP flows is performed, allowing the characterization of bits, packets and flows traffic transmitted per second, and the extraction of descriptive flow attributes, like source IP address, destination IP address, source TCP/UDP port and destination TCP/UDP port. The systems were evaluated using a real network environment and showed promising results. Moreover, the correspondence between true-positive and false-positive rates demonstrates that the systems are able to enhance the detection of anomalous behavior by maintaining a satisfactory false-alarm rate. Display Omitted Anomaly detection issue is addressed based on network traffic profiling.Proposal and comparison of detection methods belonging to distinct algorithm classes.Detection mechanism constructed over an adaptation of a pattern matching technique.Use of real and simulated traffic to evaluate the proposed methods.Traffic patterns that may harm the network operations are proactively identified.

Holt-Winters statistical forecasting and ACO metaheuristic for traffic characterization

Due to modernization, expansion of computer networks has become an inevitable process. However, this growth is also accompanied by increased complexity, which makes it necessary to use resources that assist the management of these networks. In this paper, we propose a traffic characterization using two-dimensional flow analysis for modeling the behavior traffic pattern, here called Digital Signature of Network Segment Using Flow Analysis (DSNSF). To accomplish this task we have used the improved Holt-Winters forecasting and Ant Colony Optimization metaheuristic methods. The DSNSF obtained by each model are compared to a real traffic of packets and bits and then subjected to specific evaluations in order to measure its accuracy.

Hybrid heuristic-waterfilling game theory approach in MC-CDMA resource allocation

Abstract: This paper discusses the power allocation with fixed rate constraint problem in multi-carrier code division multiple access (MC-CDMA) networks, that has been solved through game theoretic perspective by the use of an iterative water-filling algorithm (IWFA). The problem is analyzed under various interference density configurations, and its reliability is studied in terms of solution existence and uniqueness. Moreover, numerical results reveal the approach shortcoming, thus a new method combining swarm intelligence and IWFA is proposed to make practicable the use of game theoretic approaches in realistic MC-CDMA systems scenarios. The contribution of this paper is twofold: (i) provide a complete analysis for the existence and uniqueness of the game solution, from simple to more realist and complex interference scenarios; (ii) propose a hybrid power allocation optimization method combining swarm intelligence, game theory and IWFA. To corroborate the effectiveness of the proposed method, an outage probability analysis in realistic interference scenarios, and a complexity comparison with the classical IWFA are presented.

Parameterized Anomaly Detection System with Automatic Configuration

This work proposes a parameterized anomaly detection system, based on the method known as profile based. The analysis of network elements is performed in two levels: (i) analysis of Simple Network Management Protocol (SNMP) objects data using a hysteresis-based algorithm to detect behavior deviations; (ii) analysis of alerts generated in the first level using a dependency graph, which represents the relationships between the SNMP objects. The proposed system is also able to configure its own parameters automatically, aiming to meet the network administrator needs. Tests were performed in a real network environment and great results were obtained.

BASELINE TO HELP WITH NETWORK MANAGEMENT

This paper presents a model for automatic generation of a baseline which characterizes the traffic of network segments. The use of the baseline concept allows the manager to: identify limitations and crucial points of the network; learn about the actual status of use of the network resources; be able to gain better control of the use of network resources and to establish thresholds for the generation of more accurate and intelligent alarms, better suited to the actual characteristics of the network. Moreover, some results obtained with the practical use of the baseline in the management of network segments, are also presented. The results obtained validate the experiment and show, in practice, significant advantages in their use for