Tuesday, August 25, 2009

IEEE TRANSACTIONS ON MULTIMEDIA

GENERALIZED SEQUENCE-BASED AND REVERSE SEQUENCE-BASED MODELS FOR BROADCASTING HOT VIDEOS – 2009

It has been well recognized as an efficient approach for broadcasting popular videos by partitioning a video data stream into multiple segments and launching each segment through an individual channel simultaneously and periodically.

Based on the design premises, some recent studies, including skyscraper broadcasting (SkB), client-centric approach (CCA), greedy disk-conserving broadcasting (GDB), and reverse fast broadcasting (RFB) schemes, etc., have been reported. To study the client segment downloading process, this paper first introduces an applicable sequence-based broadcasting model that can be used to minimize the required buffer size.

By extending RFB, this paper further proposes a reverse sequence-based broadcasting model, which can generally improve the existing schemes such as SkB, CCA, GDB, and FB in terms of the relaxed client buffer size.

To have a deeper understanding on the proposed reverse model, the upper bound of the client buffer requirement is obtained through a comprehensive analysis, which is proved to be much smaller than the conventional sequence model by 25% to 50%. Based on the proposed reverse model, a reverse sequence-based broadcasting scheme is developed for achieving smaller delay than CCA and GDB.

Index Terms

Hot-video broadcasting, video-on-demand (VOD), buffers, cable TV.


IEEE TRANSACTIONS ON SOFTWARE ENGINEERING,

VOL. 35, NO. 3, MAY/JUNE 2009

CHARMY: A FRAMEWORK FOR DESIGNING AND VERIFYING ARCHITECTURAL SPECIFICATIONS PATRIZIO PELLICCIONE, PAOLA INVERARDI, AND HENRY MUCCINI

Introduced in the early stages of software development, the CHARMY framework assists the software architect in making and evaluating architectural choices. Rarely, the software architecture of a system can be established once and forever.

Most likely poorly defined and understood architectural constraints and requirements force the software architect to accept ambiguities and move forward to the construction of a suboptimal software architecture. CHARMY aims to provide an easy and practical tool for supporting the iterative modeling and evaluation of software architectures. From an UML-based architectural design, an executable prototype is automatically created.

CHARMY simulation and model checking features help in understanding the functioning of the system and discovering potential inconsistencies of the design. When a satisfactory and stable software architecture is reached, Java code conforming to structural software architecture constraints is automatically generated through suitable transformations. The overall approach is tool supported.

Index Terms

Software architectures, model checking.


IEEE RANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, VOL. 19, NO. 5, MAY 2008

COMPUTATION-EFFICIENT MULTICAST KEY DISTRIBUTION

Efficient key distribution is an important problem for secure group communications. The communication and storage complexity of multicast key distribution problem has been studied extensively. In this paper, we propose a new multicast key distribution scheme whose computation complexity is significantly reduced.

Instead of using conventional encryption algorithms, the scheme employs MDS codes, a class of error control codes, to distribute multicast key dynamically. This scheme drastically reduces the computation load of each group member compared to existing schemes employing traditional encryption algorithms.

Such a scheme is desirable for many wireless applications where portable devices or sensors need to reduce their computation as much as possible due to battery power limitations. Easily combined with any key-tree-based schemes, this scheme provides much lower computation complexity while maintaining low and balanced communication complexity and storage complexity for secure dynamic multicast key distribution.

Index Terms

Key distribution, multicast, MDS codes, erasure decoding, computation complexity.


IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 20, NO. 6, JUNE 2008

AN EFFICIENT CLUSTERING SCHEME TO EXPLOIT HIERARCHICAL DATA IN NETWORK TRAFFIC ANALYSIS

There is significant interest in the data mining and network management communities about the need to improve existing techniques for clustering multivariate network traffic flow records so that we can quickly infer underlying traffic patterns.

In this paper, we investigate the use of clustering techniques to identify interesting traffic patterns from network traffic data in an efficient manner.

We develop a framework to deal with mixed type attributes including numerical, categorical, and hierarchical attributes for a one-pass hierarchical clustering algorithm.

We demonstrate the improved accuracy and efficiency of our approach in comparison to previous work on clustering network traffic.

Index Terms

Traffic analysis, network management, network monitoring, clustering, classification and association rules, hierarchical clustering.


A SURVEY OF LEARNING-BASED TECHNIQUES OF EMAIL SPAM FILTERING - 2008

Email spam is one of the major problems of the today’s Internet, bringing financial damage to companies and annoying individual users. Among the approaches developed to stop spam, filtering is an important and popular one.

In this paper we give an overview of the state of the art of machine learning applications for spam filtering, and of the ways of evaluation and comparison of different filtering methods.

We also provide a brief description of other branches of anti-spam protection and discuss the use of various approaches in commercial and noncommercial anti-spam software solutions


BOTMINER: CLUSTERING ANALYSIS OF NETWORK TRAFFIC FOR PROTOCOL- AND STRUCTURE-INDEPENDENT BOTNET DETECTION

Botnets are now the key platform for many Internet attacks, such as spam, distributed denial-of-service (DDoS), identity theft, and phishing. Most of the current botnet detection approaches work only on specific botnet command and control (C&C) protocols (e.g., IRC) and structures (e.g., centralized), and can become ineffective as botnets change their C&C techniques.

In this paper, we present a general detection framework that is independent of botnet C&C protocol and structure, and requires no a priori knowledge of botnets (such as captured bot binaries and hence the botnet signatures, and C&C server names/addresses).

We start from the definition and essential properties of botnets. We define a botnet as a coordinated group of malware instances that are controlled via C&C communication channels. The essential properties of a botnet are that the bots communicate with some C&C servers/peers, perform malicious activities, and do so in a similar or correlated way.

Accordingly, our detection framework clusters similar communication traffic and similar malicious traffic, and performs cross cluster correlation to identify the hosts that share both similar communication patterns and similar malicious activity patterns. These hosts are thus bots in the monitored network.

We have implemented our BotMiner prototype system and evaluated it using many real network traces. The results show that it can detect real-world botnets (IRC-based, HTTP-based, and P2P botnets including Nugache and Storm worm), and has a very low false positive rate.


IEEE/ACM TRANSACTIONS ON NETWORKING, VOL. 16, NO. 1,

FEBRUARY 2008

DUAL-LINK FAILURE RESILIENCY THROUGH BACKUP LINK MUTUAL EXCLUSION

Networks employ link protection to achieve fast recovery from link failures. While the first link failure can be protected using link protection, there are several alternatives for protecting against the second failure.

This paper formally classifies the approaches to dual-link failure resiliency. One of the strategies to recover from dual-link failures is to employ link protection for the two failed links independently, which requires that two links may not use each other in their backup paths if they may fail simultaneously.

Such a requirement is referred to as backup link mutual exclusion (BLME) constraint and the problem of identifying a backup path for every link that satisfies the above requirement is referred to as the BLME problem.

This paper develops the necessary theory to establish the sufficient conditions for existence of a solution to the BLME problem. Solution methodologies for the BLME problem is developed using two approaches by:

1) formulating the backup path selection as an integer linear program;

2) developing a polynomial time heuristic based on minimum cost path routing.

The ILP formulation and heuristic are applied to six networks and their performance is compared with approaches that assume precise knowledge of dual-link failure. It is observed that a solution exists for all of the six networks considered. The heuristic approach is shown to obtain feasible solutions that are resilient to most dual-link failures, although the backup path lengths may be significantly higher than optimal. In addition, the paper illustrates the significance of the knowledge of failure location by illustrating that network with higher connectivity may require lesser capacity than one with a lower connectivity to recover from dual-link failures.

Index Terms

Backup link mutual exclusion, dual-link failures, link protection, optical networks.


IEEE TRANSACTIONS ON NEURAL NETWORKS,
VOL. 20, NO. 3, MARCH 2009

ADAPTIVE NEURAL NETWORK TRACKING CONTROL OF MIMO NONLINEAR SYSTEMS WITH UNKNOWN DEAD ZONES AND CONTROL DIRECTIONS

In this paper, adaptive neural network (NN) tracking control is investigated for a class of uncertain multiple-input–multiple- output (MIMO) nonlinear systems in triangular control structure with unknown nonsymmetric dead zones and control directions.

The design is based on the principle of sliding mode control and the use of Nussbaum-type functions in solving the problem of the completely unknown control directions. It is shown that the dead-zone output can be represented as a simple linear system with a static time-varying gain and bounded disturbance by introducing characteristic function.

By utilizing the integral-type Lyapunov function and introducing an adaptive compensation term for the upper bound of the optimal approximation error and the dead-zone disturbance, the closed-loop control system is proved to be semiglobally uniformly ultimately bounded, with tracking errors converging to zero under the condition that the slopes of unknown dead zones are equal. Simulation results demonstrate the effectiveness of the approach.

Index Terms

Adaptive control, dead zone, neural network (NN) control, Nussbaum function, sliding mode control.


A FRAMEWORK FOR THE CAPACITY EVALUATION OF MULTIHOP WIRELESS NETWORKS, 2009

The specific challenges of multihop wireles networks lead to a strong research effort on efficient protocols design where the offered capacity is a key objective. More specifically, routing strategy largely impacts the network capacity, i.e. the throughput offered to each flow.

In this work, we propose a complete framework to compute the upper and the lower bounds of the network capacity according to a physical topology and a given routing protocol. The radio resource sharing principles of CSMA-CA is modeled as a set of linear constraints with two models of fairness. The first one assumes that nodes have a fair access to the channel, while the second one assumes that on the radio links.

We then develop a pessimistic and an optimistic scenarios for radio resource sharing, yielding a lower bound and an upper bound on the network capacity for each fairness case. Our approach is independent of the network topology and the routing protocols, and provides therefore a relevant framework for their comparison.

We apply our models to a comparative analysis of a well-known flat routing protocol OLSR against two main self-organized structure approaches, VSR and localized CDS.

Index Terms

Network capacity, multihop wireless networks, upper and lower bounds, linear programing


IEEE TRANSACTIONS ON BROADCASTING, VOL. 55, NO. 2, JUNE 2009

CONTINUOUS FLOW WIRELESS DATA BROADCASTING FOR HIGH-SPEED ENVIRONMENTS

With the increasing popularity of wireless networks and mobile computing, data broadcasting has emerged as an efficient way of delivering data to mobile clients having a high degree of commonality in their demand patterns.

This paper proposes an adaptive wireless push system that operates efficiently in environments characterized by high broadcasting speeds and a-priori unknown client demands for data items.

The proposed system adapts to the demand pattern of the client population in order to reflect the overall popularity of each data item.

We propose a method for feedback collection by the server so that the client population can enjoy a performance increase in proportion to the broadcasting speed used by the server.

Simulation results are presented which reveal satisfactory performance in environments with a-priori unknown client demands and under various high broadcasting speeds.

Index Terms

Adaptive systems, data broadcasting, high-speed, learning automata.

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