Design of Discrete-time Chaos-Based Systems for Hardware Security Applications

Security of systems has become a major concern with the advent of technology. Researchers are proposing new security solutions every day in order to meet the area, power and performance specifications of the systems. The additional circuit required for security purposes can consume significant area and power. This work proposes a solution which utilizes discrete-time chaos-based logic gates to build a system which addresses multiple hardware security issues. The nonlinear dynamics of chaotic maps is leveraged to build a system that mitigates IC counterfeiting, IP piracy, overbuilding, disables hardware Trojan insertion and enables authentication of connecting devices (such as IoT and mobile). Chaos-based systems are also used to generate pseudo-random numbers for cryptographic applications.The chaotic map is the building block for the design of discrete-time chaos-based oscillator. The analog output of the oscillator is converted to digital value using a comparator in order to build logic gates. The logic gate is reconfigurable since different parameters in the circuit topology can be altered to implement multiple Boolean functions using the same system. The tuning parameters are control input, bifurcation parameter, iteration number and threshold voltage of the comparator. The proposed system is a hybrid between standard CMOS logic gates and reconfigurable chaos-based logic gates where original gates are replaced by chaos-based gates. The system works in two modes: logic locking and authentication. In logic locking mode, the goal is to ensure that the system achieves logic obfuscation in order to mitigate IC counterfeiting. The secret key for logic locking is made up of the tuning parameters of the chaotic oscillator. Each gate has 10-bit key which ensures that the key space is large which exponentially increases the computational complexity of any attack. In authentication mode, the aim of the system is to provide authentication of devices so that adversaries cannot connect to devices to learn confidential information. Chaos-based computing system is susceptible to process variation which can be leveraged to build a chaos-based PUF. The proposed system demonstrates near ideal PUF characteristics which means systems with large number of primary outputs can be used for authenticating devices

Measuring Generalization and Overfitting in Machine Learning

Due to the prevalence of machine learning algorithms and the potential for their decisions to profoundly impact billions of human lives, it is crucial that they are robust, reliable, and understandable. This thesis examines key theoretical pillars of machine learning surrounding generalization and overfitting, and tests the extent to which empirical behavior matches existing theory. We develop novel methods for measuring overfitting and generalization, and we characterize how reproducible observed behavior is across differences in optimization algorithm, dataset, task, evaluation metric, and domain.First, we examine how optimization algorithms bias machine learning models towards solutions with varying generalization properties. We show that adaptive gradient methods empirically find solutions with inferior generalization behavior compared to those found by stochastic gradient descent. We then construct an example using a simple overparameterized model that corroborates the algorithms’ empirical behavior on neural networks. Next, we study the extent to which machine learning models have overfit to commonly reused datasets in both academic benchmarks and machine learning competitions. We build new test sets for the CIFAR-10 and ImageNet datasets and evaluate a broad range of classification models on the new datasets. All models experience a drop in accuracy, which indicates that current accuracy numbers are susceptible to even minute natural variations in the data distribution. Surprisingly, despite several years of adaptively selecting the models to perform well on these competitive benchmarks, we find no evidence of overfitting. We then analyze data from the machine learning platform Kaggle and find little evidence of substantial overfitting in ML competitions. These findings speak to the robustness of the holdout method across different data domains, loss functions, model classes, and human analysts.Overall, our work suggests that the true concern for robust machine learning is distribution shift rather than overfitting, and designing models that still work reliably in dynamic environments is a challenging but necessary undertaking

Discovery in Physics

Volume 2 covers knowledge discovery in particle and astroparticle physics. Instruments gather petabytes of data and machine learning is used to process the vast amounts of data and to detect relevant examples efficiently. The physical knowledge is encoded in simulations used to train the machine learning models. The interpretation of the learned models serves to expand the physical knowledge resulting in a cycle of theory enhancement

Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm

Abstract— Online transportation has become a basic requirement of the general public in support of all activities to go to work, school or vacation to the sights. Public transportation services compete to provide the best service so that consumers feel comfortable using the services offered, so that all activities are noticed, one of them is the search for the shortest route in picking the buyer or delivering to the destination. Node Combination method can minimize memory usage and this methode is more optimal when compared to A* and Ant Colony in the shortest route search like Dijkstra algorithm, but can’t store the history node that has been passed. Therefore, using node combination algorithm is very good in searching the shortest distance is not the shortest route. This paper is structured to modify the node combination algorithm to solve the problem of finding the shortest route at the dynamic location obtained from the transport fleet by displaying the nodes that have the shortest distance and will be implemented in the geographic information system in the form of map to facilitate the use of the system. Keywords— Shortest Path, Algorithm Dijkstra, Node Combination, Dynamic Location (key words