Pieter-Tjerk de Boer

Unified Frequency-Domain Analysis of Switched-Series-

A wide variety of voltage mixers and samplers are implemented with similar circuits employing switches, resistors, and capacitors. Restrictions on duty cycle, bandwidth, or output frequency are commonly used to obtain an analytical expression for the response of these circuits. This paper derives unified expressions without these restrictions. To this end, the circuits are decomposed into a polyphase multipath combination of single-ended or differential switched-series-RC kernels. Linear periodically time-variant network theory is used to find the harmonic transfer functions of the kernels and the effect of polyphase multipath combining. From the resulting transfer functions, the conversion gain, output noise, and noise figure can be calculated for arbitrary duty cycle, bandwidth, and output frequency. Applied to a circuit, the equations provide a mathematical basis for a clear distinction between a “mixing” and a “sampling” operating region while also covering the design space “in between.” Circuit simulations and a comparison with mixers published in literature are performed to support the analysis.

RC

We investigate the simulation of overflow of the total population of a Markovian two-node tandem queue model during a busy cycle, using importance sampling with a state-independent change of measure. We show that the only such change of measure that may possibly result in asymptotically efficient simulation for large overflow levels is exchanging the arrival rate with the smallest service rate. For this change of measure, we classify the models parameter space into regions of asymptotic efficiency, exponential growth of the relative error, and infinite variance, using both analytical and numerical techniques.

Passive Mixers and Samplers

Nowadays, network load is constantly increasing and high-speed infrastructures (1-10Gbps) are becoming increasingly common. In this context, flow-based intrusion detection has recently become a promising security mechanism. However, since flows do not provide any information on the content of a communication, it also became more difficult to establish a ground truth for flow-based techniques benchmarking. A possible approach to overcome this problem is the usage of synthetic traffic traces where the generation of malicious traffic is driven by models. In this paper, we propose a flow time series model of SSH brute-force attacks based on Hidden Markov Models. Our results show that the model successfully emulates an attacker behavior, generating meaningful flow time series.

Analysis of state-independent importance-sampling measures for the two-node tandem queue

In this paper, a method is presented for the efficient estimation of rare-event (buffer overflow) probabilities in queueing networks using importance sampling. Unlike previously proposed change of measures, the one used here is not static, i.e., it depends on the buffer contents at each of the network nodes. The optimal state-dependent change of measure is determined adaptively during the simulation, using the cross-entropy method. The adaptive state-dependent importance sampling algorithm proposed in this paper yields asymptotically efficient simulation of models for which it is shown (formally or otherwise) that no effective static change of measure exists. Simulation results for queueing models of communication systems are presented to demonstrate the effectiveness of the method.

Hidden Markov Model Modeling of SSH Brute-Force Attacks

The distribution of the remaining service time upon reaching some target level in an M/G/1 queue is of theoretical as well as practical interest. In general, this distribution depends on the initial level as well as on the target level, say, B. Two initial levels are of particular interest, namely, level “1” (i.e., upon arrival to an empty system) and level “B−1” (i.e., upon departure at the target level).In this paper, we consider a busy cycle and show that the remaining service time distribution, upon reaching a high level B due to an arrival, converges to a limiting distribution for B→∞. We determine this asymptotic distribution upon the “first hit” (i.e., starting with an arrival to an empty system) and upon “subsequent hits” (i.e., starting with a departure at the target) into a high target level B. The form of the limiting (asymptotic) distribution of the remaining service time depends on whether the system is stable or not. The asymptotic analysis in this paper also enables us to obtain good analytical approximations of interesting quantities associated with rare events, such as overflow probabilities.

Adaptive state-dependent importance sampling simulation of Markovian queueing networks

Stochastic model checking has been used recently to assess, among others, dependability measures for a variety of systems. However, the employed numerical methods, as, e.g., supported by model checking tools such as PRISM and MRMC, suffer from the state-space explosion problem. The main alternative is statistical model checking, which uses standard simulation, but this performs poorly when small probabilities need to be estimated. Therefore, we propose a method based on importance sampling to speed up the simulation process in cases where the failure probabilities are small due to the high speed of the system’s repair units. This setting arises naturally in Markovian models of highly dependable systems. We show that our method compares favourably to standard simulation, to existing importance sampling techniques and to the numerical techniques of PRISM.

The Remaining Service Time upon Reaching a High Level in M/G/1 Queues

Recently the mean-field method has been adopted for analysing systems consisting of a large number of interacting objects in computer science, biology, chemistry, etc. It allows for a quick and accurate analysis of such systems, while avoiding the state-space explosion problem. So far, the method has primarily been used for performance evaluation. In this paper, we use the mean-field method for model-checking. We define and motivate a logic MF-CSL for describing properties of systems composed of many identical interacting objects. The proposed logic allows describing both properties of the overall system and of a random individual object. Algorithms to check the satisfaction relation for all MF-CSL operators are proposed. Furthermore, we explain how the set of all time instances that fulfill a given MF-CSL formula for a certain distribution of objects can be computed.

Rare event simulation for highly dependable systems with fast repairs

We introduce an automated approach for applying rare event simulation to stochastic Petri net (SPN) models of highly reliable systems. Rare event simulation can be much faster than standard simulation because it is able to exploit information about the typical behaviour of the system. Previously, such information came from heuristics, human insight, or analysis on the full state space. We present a formal algorithm that obtains the required information from the high-level SPN-description, without generating the full state space. Essentially, our algorithm reduces the state space of the model into a (much smaller) graph in which each node represents a set of states for which the most likely path to failure has the same form. We empirically demonstrate the efficiency of the method with two case studies.

A logic for model-checking mean-field models

Due to new interactive TV services, synchronizing the playout of content on different TVs is becoming important. To synchronize, knowledge of delay differences is needed. In this study, a measurement system is developed to gain insight into the magnitude of delay differences of different TV setups in an automated fashion. This paper shows the measurement system, which is validated for precision and accuracy. Preliminary measurements results show that regular TV broadcasts differ up to 6 seconds in playout moment and that web based TV broadcasts can introduce more than a minute delay. Furthermore, we measured a broadcasting before encoding and modulation, which resulted in a time about 4 second before the fastest receiver. On a side note, while developing the measurement system we found out that GPS timing on consumer Android devices was inaccurate, with fluctuations of up to 1 second.

Automated rare event simulation for stochastic petri nets

Stochastic model checking has been used recently to assess, among others, dependability measures for a variety of systems. However, the employed numerical methods, as, e.g., supported by model checking tools such as PRISM and MRMC, suffer from the state-space explosion problem. The main alternative is statistical model checking, which uses standard simulation, but this performs poorly when small probabilities need to be estimated. Therefore, we propose a method based on importance sampling to speed up the simulation process in cases where the failure probabilities are small due to the high speed of the system’s repair units. This setting arises naturally in Markovian models of highly dependable systems. We show that our method compares favourably to standard simulation, to existing importance sampling techniques and to the numerical techniques of PRISM.