Classifier learning with data-sets that suffer from imbalanced class distributions is a challenging problem in data mining community. This issue occurs when the number of examples that represent one class is much lower than the ones of the other classes. Its presence in many real-world applications has brought along a growth of attention from researchers. In machine learning, the ensemble of classifiers are known to increase the accuracy of single classifiers by combining several of them, but neither of these learning techniques alone solve the class imbalance problem, to deal with this issue the ensemble learning algorithms have to be designed specifically. In this paper, our aim is to review the state of the art on ensemble techniques in the framework of imbalanced data-sets, with focus on two-class problems. We propose a taxonomy for ensemble-based methods to address the class imbalance where each proposal can be categorized depending on the inner ensemble methodology in which it is based. In addition, we develop a thorough empirical comparison by the consideration of the most significant published approaches, within the families of the taxonomy proposed, to show whether any of them makes a difference. This comparison has shown the good behavior of the simplest approaches which combine random undersampling techniques with bagging or boosting ensembles. In addition, the positive synergy between sampling techniques and bagging has stood out. Furthermore, our results show empirically that ensemble-based algorithms are worthwhile since they outperform the mere use of preprocessing techniques before learning the classifier, therefore justifying the increase of complexity by means of a significant enhancement of the results.

A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches

Sphere Packings, Lattices and Groups

The Object Management Group's (OMG) Model Driven Architecture (MDA) strategy envisages a world where models play a more direct role in software production, being amenable to manipulation and transformation by machine. Model Driven Engineering (MDE) is wider in scope than MDA. MDE combines process and analysis with architecture. This article sets out a framework for model driven engineering, which can be used as a point of reference for activity in this area. It proposes an organisation of the modelling 'space' and how to locate models in that space. It discusses different kinds of mappings between models. It explains why process and architecture are tightly connected. It discusses the importance and nature of tools. It identifies the need for defining families of languages and transformations, and for developing techniques for generating/configuring tools from such definitions. It concludes with a call to align metamodelling with formal language engineering techniques.

Model Driven Engineering

Network anomaly detection is an important and dynamic research area. Many network intrusion detection methods and systems (NIDS) have been proposed in the literature. In this paper, we provide a structured and comprehensive overview of various facets of network anomaly detection so that a researcher can become quickly familiar with every aspect of network anomaly detection. We present attacks normally encountered by network intrusion detection systems. We categorize existing network anomaly detection methods and systems based on the underlying computational techniques used. Within this framework, we briefly describe and compare a large number of network anomaly detection methods and systems. In addition, we also discuss tools that can be used by network defenders and datasets that researchers in network anomaly detection can use. We also highlight research directions in network anomaly detection.

Network Anomaly Detection: Methods, Systems and Tools

Introduction to lie algebras and representation theory

In this paper we introduce a detection based security scheme for wireless sensor networks. Although sensor nodes have low computation and communication capabilities, they have specific properties such as their stable neighborhood information that allows for detection of anomalies in networking and transceiver behaviors of the neighboring nodes. We show that such characteristics can be exploited as key enablers for providing security to large scale sensor networks. In many attacks against sensor networks, the first step for an attacker is to establish itself as a legitimate node within the network. To make a sensor node capable of detecting an intruder a simple dynamic statistical model of the neighboring nodes is built in conjunction with a low-complexity detection algorithm by monitoring received packet power levels and arrival rates.

An intrusion detection system for wireless sensor networks

https://www.etsmtl.ca/ETS/media/ImagesETS/Labo/LIVIA/Publications/2012/Khreich_InfoSciences_2012.pdf

A survey of techniques for incremental learning of HMM parameters

https://etsmtl.ca/ets/media/imagesets/labo/livia/publications/2010/khreich_pr_2010.pdf

Iterative Boolean combination of classifiers in the ROC space: An application to anomaly detection with HMMs

In order to select and rank the best services in a cloud computing environment, the end-to-end quality of service (QoS) values of cloud services have to be computed. For a new SaaS provider, the deployment of its software application in the cloud is a challenging job. It has to find a hosting service (IaaS) that hosts its service. The primary goal of the SaaS provider is to make its service at the top of the ranked list of cloud services returned to end users through satisfying their QoS requirements. In this paper, we propose a mechanism to map the users' QoS requirements of cloud services to the right QoS specifications of SaaS then map them to best IaaS service that offers the optimal QoS guarantees. Then together SaaS and IaaS services can provide the best service offer to end users. As a result of the mapping, the end-to-end QoS values can be calculated. We propose a set of rules to perform the mapping process. We hierarchically model the QoS specifications of cloud services using the Analytic Hierarchy Process (AHP) method. The AHP based model helps to facilitate the mapping process across the cloud layers, and to rank the candidate cloud services for end users. We use a case study to illustrate and validate our solution approach.

An End-to-End QoS Mapping Approach for Cloud Service Selection

We present an innovative methodology for accelerating the elliptic curve point formulae over prime fields. This flexible technique uses the substitution of multiplication with squaring and other cheaper operations, by exploiting the fact that field squaring is generally less costly than multiplication. Applying this substitution to the traditional formulae, we obtain faster point operations in unprotected sequential implementations. We show the significant impact our methodology has in protecting against Simple Side-Channel Attacks (SSCA). We modify the ECC point formulae to achieve a faster atomic structure when applying atomicity side-channel protection. In contrast to previous atomic operations that assumed squarings are undistinguishable from multiplications, our new atomic structure offers true SSCA-protection because it includes squaring in its formulation. We also extend our implementation to parallel architectures such as SIMD (Single-Instruction Multiple-Data). With the introduction of a new coordinate system and with the flexibility of our methodology, we present, to our knowledge, the fastest formulae for SIMD-based schemes that are capable of executing 3 and 4 operations simultaneously. Finally, a new parallel SSCA-protected scheme is proposed for multiprocessor/parallel architectures by applying the atomic structure presented in this work. Our parallel and atomic operations are shown to be significantly faster than previous implementations.

Ali Miri

Papers

An intrusion detection system for wireless sensor networks

A survey of techniques for incremental learning of HMM parameters

Iterative Boolean combination of classifiers in the ROC space: An application to anomaly detection with HMMs

An End-to-End QoS Mapping Approach for Cloud Service Selection

Fast and Flexible Elliptic Curve Point Arithmetic over Prime Fields