Biblio

With the wild applications of machine learning (ML) technology, automatic speech recognition (ASR) has made great progress in recent years. Despite its great potential, there are various evasion attacks of ML-based ASR, which could affect the security of applications built upon ASR. Up to now, most studies focus on white-box attacks in ASR, and there is almost no attention paid to black-box attacks where attackers can only query the target model to get output labels rather than probability vectors in audio domain. In this paper, we propose an evasion attack against ASR in the above-mentioned situation, which is more feasible in realistic scenarios. Specifically, we first train a substitute model by using data augmentation, which ensures that we have enough samples to train with a small number of times to query the target model. Then, based on the substitute model, we apply Differential Evolution (DE) algorithm to craft adversarial examples and implement black-box attack against ASR models from the Speech Commands dataset. Extensive experiments are conducted, and the results illustrate that our approach achieves untargeted attacks with over 70% success rate while still maintaining the authenticity of the original data well.

Attribute reduction algorithms in rough set theory can be classified into two groups, i.e. heuristics algorithms and computational intelligence algorithms. The former has good search efficiency but it can not find the global optimal reduction. Conversely, the latter is possible to find global optimal reduction but usually suffers from premature convergence. To address this problem, this paper proposes a two-stage algorithm for finding high quality reduction. In first stage, a classical differential evolution algorithm is employed to rapidly approach the optimal solution. When the premature convergence is detected, a local search algorithm which is intuitively a forward-backward heuristics is launched to improve the quality of the reduction. Experiments were performed on six UCI data sets and the results show that the proposed algorithm can outperform the existing computational intelligence algorithms.

The increasing of malwares has presented a serious threat to the security of computer systems in recent years. Traditional signature-based anti-virus systems are not able to detect metamorphic and previously unseen malwares and it inspires people to use machine learning methods such as Naive Bayes and Decision Tree to identity malicious executables. Among these methods, detecting malwares by using Support Vector Machine (SVM) is one of the most effective approaches. However, the parameters of SVM have serious impacts on its classification performance. In order to find the optimal parameter combination and avoid the problem of falling into local optimal solution, many methods based on evolutionary algorithms are proposed, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE) and others. But these algorithms still face the problem of being trapped into local solution spaces in different degree. In this paper, an improved fireworks algorithm is presented and applied to search parameters of SVM: penalty factor c and kernel function parameter g. To research the performance of the proposed algorithm, numeric experiments are made and compared with some typical algorithms, the experimental results demonstrate it outperforms other algorithms.

This paper addresses analysis and design of multiple fault diagnosis for a class of Lipschitz nonlinear system. In order to automatically estimate multi-fault parameters efficiently, a new method of multi-fault diagnosis based on the differential evolution algorithm (DE) is proposed. Finally, a series of experiments validate the feasibility and effectiveness of the proposed method. The simulation show the high accuracy of the proposed strategies in multiple abrupt faults diagnosis.

This paper proposes a method of distinguishing stock market states, classifying them based on price variations of securities, and using an evolutionary algorithm for improving the quality of classification. The data represents buy/sell order queues obtained from rebuild order book, given as price-volume pairs. In order to put more emphasis on certain features before the classifier is used, we use a weighting scheme, further optimized by an evolutionary algorithm.