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A Monte Carlo simulation study on class enumeration with latent class regression models. / Latent class regression (LCR) is a statistical method used to identify qualitatively different groups or latent classes within a heterogeneous population and commonly used in the behavioural, health, and social sciences. Despite the vast applications, an agreed fit index to correctly determine the number of latent classes is hotly debated. To add, there are also conflicting views on whether covariates should or should not be included into the class enumeration process. We conduct a simulation study to determine the impact of covariates on the class enumeration accuracy as well as study the performance of several commonly used fit indices under different population models and modelling conditions. Our results indicate that of the eight fit indices considered, the aBIC and BLRT proved to be the best performing fit indices for class enumeration. Furthermore, we found that covariates should not be included into the enumeration procedure. Our results illustrate that an unconditional LCA model can enumerate equivalently as well as a conditional LCA model with its true covariate specification. Even with the presence of large covariate effects in the population, the unconditional model is capable of enumerating with high accuracy. As noted by Nylund and Gibson (2016), a misspecified covariate specification can easily lead to an overestimation of latent classes. Therefore, we recommend to perform class enumeration without covariates and determine a set of candidate latent class models with the aBIC. Once that is determined, the BLRT can be utilized on the set of candidate models and confirm whether results obtained by the BLRT match the results of the aBIC. By separating the enumeration procedure of the BLRT, it still allows one to use the BLRT but reduce the heavy computational burden that is associated with this fit index. Subsequent analysis can then be pursued accordingly after the number of latent classes is determined. / Thesis / Master of Science (MSc)

The main objective of this thesis is to examine the trade-offs between security and efficiency within symmetric ciphers. This includes the influence that block ciphers have on the new generation of word-based stream ciphers. By incorporating block-cipher like components into their designs, word-based stream ciphers have experienced hundreds-fold improvement in speed over bit-based stream ciphers, without any observable security degradation. The thesis also emphasizes the importance of keying issues in block and stream ciphers, showing that by reusing components of the principal cipher algorithm in the keying algorithm, security can be enhanced without loss of key-agility or expanding footprint in software memory. Firstly, modern block ciphers from four recent cipher competitions are surveyed and categorized according to criteria that includes the high-level structure of the block cipher, the method in which non-linearity is instilled into each round, and the strength of the key schedule. In assessing the last criterion, a classification by Carter [45] is adopted and modified to improve its consistency. The classification is used to demonstrate that the key schedule of the Advanced Encryption Standard (AES) [62] is surprisingly flimsy for a national standard. The claim is supported with statistical evidence that shows the key schedule suffers from bit leakage and lacks sufficient diffusion. The thesis contains a replacement key schedule that reuses components from the cipher algorithm, leveraging existing analysis to improve security, and reducing the cipher's implementation footprint while maintaining key agility. The key schedule is analyzed from the perspective of an efficiency-security tradeoff, showing that the new schedule rectifies an imbalance towards e±ciency present in the original. The thesis contains a discussion of the evolution of stream ciphers, focusing on the migration from bit-based to word-based stream ciphers, from which follows a commensurate improvement in design flexibility and software performance. It examines the influence that block ciphers, and in particular the AES, have had upon the development of word-based stream ciphers. The thesis includes a concise literature review of recent styles of cryptanalytic attack upon stream ciphers. Also, claims are refuted that one prominent word-based stream cipher, RC4, suffers from a bias in the first byte of each keystream. The thesis presents a divide and conquer attack against Alpha1, an irregularly clocked bit-based stream cipher with a 128-bit state. The dominating aspect of the divide and conquer attack is a correlation attack on the longest register. The internal state of the remaining registers is determined by utilizing biases in the clocking taps and launching a guess and determine attack. The overall complexity of the attack is 261 operations with text requirements of 35,000 bits and memory requirements of 2 29.8 bits. MUGI is a 64-bit word-based cipher with a large Non-linear Feedback Shift Register (NLFSR) and an additional non-linear state. In standard benchmarks, MUGI appears to su®er from poor key agility because it is implemented on an architecture for which it is not designed, and because its NLFSR is too large relative to the size of its master key. An unusual feature of its key initialization algorithm is described. A variant of MUGI, entitled MUGI-M, is proposed to enhance key agility, ostensibly without any loss of security. The thesis presents a new word-based stream cipher called Dragon. This cipher uses a large internal NLFSR in conjunction with a non-linear filter to produce 64 bits of keystream in one round. The non-linear filter looks very much like the round function of a typical modern block cipher. Dragon has a native word size of 32 bits, and uses very simple operations, including addition, exclusive-or and s-boxes. Together these ensure high performance on modern day processors such as the Intel Pentium family. Finally, a set of guidelines is provided for designing and implementing symmetric ciphers on modern processors, using the Intel Pentium 4 as a case study. Particular attention is given to understanding the architecture of the processor, including features such as its register set and size, the throughput and latencies of its instruction set, and the memory layouts and speeds. General optimization rules are given, including how to choose fast primitives for use within the cipher. The thesis describes design decisions that were made for the Dragon cipher with respect to implementation on the Intel Pentium 4. Block Ciphers, Word-based Stream Ciphers, Cipher Design, Cipher Implementa- tion, -

Nous abordons dans ces travaux l’identification non intrusive des charges des bâtiments résidentiels ainsi que la prédiction de leur état futur. L'originalité de ces travaux réside dans la méthode utilisée pour obtenir les résultats voulus, à savoir l'analyse statistique des données(algorithmes de classification). Celle-ci se base sur des hypothèses réalistes et restrictives sans pour autant avoir de limitation sur les modèles comportementaux des charges (variations de charges ou modèles) ni besoin de la connaissance des changements d'état des charges. Ainsi, nous sommes en mesure d’identifier et/ou de prédire l'état des charges consommatrices d'énergie (et potentiellement contrôlables) en se basant uniquement sur une phase d'entrainement réduite et des mesures de puissance active agrégée sur un pas de mesure de dix minutes, préservant donc la vie privée des habitants.Dans cette communication, après avoir décrit la méthodologie développée pour classifier les charges et leurs états, ainsi que les connaissances métier fournies aux algorithmes, nous comparons les résultats d’identification pour cinq algorithmes tirés de l'état de l'art et les utilisons comme support d'application à la prédiction. Les algorithmes utilisés se différencient par leur capacité à traiter des problèmes plus ou moins complexe (notamment la prise en compte de relations entre les charges) et se ne révèlent pas tous appropriés à tout type de charge dans le bâtiment résidentiel / Smart metering is one of the fundamental units of a smart grid, as many further applicationsdepend on the availability of fine-grained information of energy consumption and production.Demand response techniques can be substantially improved by processing smart meter data to extractrelevant knowledge of appliances within a residence. The thesis aims at finding generic solutions for thenon-intrusive load monitoring and future usage prediction of residential loads at a low sampling rate.Load monitoring refers to the dis-aggregation of individual loads from the total consumption at thesmart meter. Future usage prediction of appliances are important from the energy management point ofview. In this work, state of the art multi-label temporal classification techniques are implemented usingnovel set of features. Moreover, multi-label classifiers are able to take inter-appliance correlation intoaccount. The methods are validated using a dataset of residential loads in 100 houses monitored over aduration of 1-year.