Modelling and simulation are essential ingredients of the analysis and design process in power electronics. It helps a design engineer gain an increased understanding of circuit operation. Accordingly, for a set of specifications given, the designer will choose a particular topology, select component types and values, estimate circuit performance etc. Typically hierarchical modelling, analysis and simulation rather than full detailed simulation of the system provides a crucial insight and understanding. The combination of these insights with hardware prototyping and experiments constitutes a powerful and effective approach to design. Obtaining the mathematical model of the power electronic systems is a major task before any analysis or synthesis or simulation can be performed. There are circuit oriented simulators which uses inbuilt mathematical models for components. Simulation with equation solver needs mathematical models for simulation which are trimmed according to user requirement. There are various methods in the literature to obtain these mathematical models. However, the issues of multi-domain system modelling and causality of the energy variables are not sufficiently addressed. Further, specifically to power converter systems, the issue of switching power models with fixed causality is not addressed. Therefore, our research focuses on obtaining solutions to the above using relatively untouched bond graph method to obtain models for power electronic systems. The power electronic system chosen for the present work is Switched Mode Power Converters (SMPC’s) and in particular PWM DC-DC converters. Bond graph is a labelled and directed graphical representation of physical systems. The basis of bond graph modelling is energy/power flow in a system. As energy or power flow is the underlying principle for bond graph modelling, there is seamless integration across multiple domains. As a consequence, different domains (such as electrical, mechanical, thermal, fluid, magnetic etc.) can be represented in a unified way. The power or the energy flow is represented by a half arrow, which is called the power bond or the energy bond. The causality for each bond is a significant issue that is inherently addressed in bond graph modelling. As every bond involves two power variables, the decision of setting the cause variable and the effect variable is by natural laws. This has a significant bearing in the resulting state equations of the system. Proper assignment of power direction resolves the sign-placing problem when connecting sub-model structures. The causality will dictate whether a specific power variable is a cause or the effect. Using causal bars on either ends of the power bond, graphically indicate the causality for every bond. Once the causality gets assigned, bond graph displays the structure of state space equations explicitly. The first problem we have encountered in modelling power electronic systems with bond graph is power switching. The essential part of any switched power electronic system is a switch. Switching in the power electronic circuits causes change in the structure of the system. This results in change in dynamic equations of the circuit according to position of the switch. We have proposed the switched power junctions (SPJ) to represent switching phenomena in power electronic systems. The switched power junctions are a generalization of the already existing 0-junction and 1-junction concepts of the bond graph element set. The SPJ’s models ideal switching. These elements maintain causality invariance for the whole system for any operational mode of the system. This means that the state vector of the resulting state equation of the system does not change for any operating mode. As SPJs models ideal power switching, the problem of stiff systems and associated numerical stability problems while simulating the system is eliminated. Further, it maintains one to one correspondence with the physical system displaying all the feasible modes of operation at the same time on the same graph. Using these elements, the switched mode power converters (SMPC's) are modelled in bond graph. Bond graph of the converter is the large signal model of the converter. A graphical procedure is proposed that gives the averaged large signal, steady state and small signal ac models. The procedure is suitable for the converters operating in both Continuous Conduction Mode (CCM) and in Discontinuous Conduction Mode (DCM). For modelling in DCM, the concept of virtual switch is used to model the converter using bond graph. Using the proposed method, converters of any complexity can be modelled incorporating all the advantages of bond graph modelling. Magnetic components are essential part of the power electronic systems. Most common parts are the inductor, transformer and coupled inductors which contain both the electric and magnetic domains. Gyrator-Permeance approach is used to model the magnetic components. Gyrator acts as an interface between electric and magnetic domain and capacitor model the permeance of the magnetic circuits. Components like inductor, tapped inductor, transformer, and tapped transformer are modelled. Interleaved converters with coupled inductor, zero ripple phenomena in coupled inductor converters as well as integrated magnetic Cuk converter are also modelled. Modelling of integrated magnetic converters like integrated magnetic forward converter, integrated magnetic boost converter are also explored. To carry out all the simulations of proposed bond graph models, bond graph toolbox is developed using MATLAB/SIMULINK. The MATLAB/SIMULINK is chosen since it is general simulation platform widely available. Therefore all the analysis and simulation can be carried out using facilities available in MATLAB/SIMULINK. Symbolic equation extraction toolbox is also developed which extracts state equations from bond graph model in SIMULINK in symbolic form.

Today, malware authors not only write malicious software but also employ obfuscation, polymorphism, packing and endless such evasive techniques to escape detection by Anti-Virus Products (AVP). Besides the individual behavior of malware, the relations that exist among them play an important role for improving malware detection. This work aims to enable malware analysts at F-Secure Labs to explore various such relationships between malicious URLs and file samples in addition to their individual behavior and activity. The current detection methods at F-Secure Labs analyze unknown URLs and file samples independently without taking into account the correlations that might exist between them. Such traditional classification methods perform well but are not efficient at identifying complex multi-stage malware that hide their activity. The interactions between malware may include any type of network activity, dropping, downloading, etc. For instance, an unknown downloader that connects to a malicious website which in turn drops a malicious payload, should indeed be blacklisted. Such analysis can help block the malware infection at its source and also comprehend the whole infection chain. The outcome of this proof-of-concept study is a system that detects new malware using graph modelling to infer their relationship to known malware as part of the malware classification services at F-Secure. / Idag, skadliga program inte bara skriva skadlig programvara men också använda förvirring, polymorfism, packning och ändlösa sådana undan tekniker för att fly detektering av antivirusprodukter (AVP). Förutom individens beteende av skadlig kod, de relationer som finns mellan dem spelar en viktig roll för att förbättra detektering av skadlig kod. Detta arbete syftar till att ge skadliga analytiker på F-Secure Labs att utforska olika sådana relationer mellan skadliga URL: er och fil prover i Förutom deras individuella beteende och aktivitet. De aktuella detektionsmetoder på F-Secure Labs analysera okända webbadresser och fil prover oberoende utan med beaktande av de korrelationer som kan finnas mellan dem. Sådan traditionella klassificeringsmetoder fungerar bra men är inte effektiva på att identifiera komplexa flerstegs skadlig kod som döljer sin aktivitet. Interaktioner mellan malware kan innefatta någon typ av nätverksaktivitet, släppa, nedladdning, etc. Till exempel, en okänd loader som ansluter till en skadlig webbplats som i sin tur släpper en skadlig nyttolast, bör verkligen vara svartlistad. En sådan analys kan hjälpa till att blockera malware infektion vid källan och även förstå hela infektion kedja. Resultatet av denna proof-of-concept studien är ett system som upptäcker ny skadlig kod med hjälp av diagram modellering för att sluta deras förhållande till kända skadliga program som en del av de skadliga klassificerings tjänster på F-Secure.