Adversarial Samples on Android Malware Detection Systems for IoT Systems.

Many IoT (Internet of Things) systems run Android systems or Android-like systems. With the continuous development of machine learning algorithms, the learning-based Android malware detection system for IoT devices has gradually increased. However, these learning-based detection models are often vulnerable to adversarial samples. An automated testing framework is needed to help these learning-based malware detection systems for IoT devices perform security analysis. The current methods of generating adversarial samples mostly require training parameters of models and most of the methods are aimed at image data. To solve this problem, we propose a testing framework for learning-based Android malware detection systems (TLAMD) for IoT Devices. The key challenge is how to construct a suitable fitness function to generate an effective adversarial sample without affecting the features of the application. By introducing genetic algorithms and some technical improvements, our test framework can generate adversarial samples for the IoT Android application with a success rate of nearly 100% and can perform black-box testing on the system.

TLTD: A Testing Framework for Learning-Based IoT Traffic Detection Systems.

With the popularization of IoT (Internet of Things) devices and the continuous development of machine learning algorithms, learning-based IoT malicious traffic detection technologies have gradually matured. However, learning-based IoT traffic detection models are usually very vulnerable to adversarial samples. There is a great need for an automated testing framework to help security analysts to detect errors in learning-based IoT traffic detection systems. At present, most methods for generating adversarial samples require training parameters of known models and are only applicable to image data. To address the challenge, we propose a testing framework for learning-based IoT traffic detection systems, TLTD. By introducing genetic algorithms and some technical improvements, TLTD can generate adversarial samples for IoT traffic detection systems and can perform a black-box test on the systems.

Suspect screening and target quantification of human pharmaceutical residues in the surface water of Wuhan, China, using UHPLC-Q-Orbitrap HRMS.

In this study we developed a systematic method for suspect screening and target quantification of the human pharmaceutical residues in water, via solid phase extraction (SPE) followed by liquid chromatography-high resolution mass spectrometry (LC-HRMS). We then proceeded to study the occurrences and distribution of the pharmaceuticals in the surface waters of Wuhan, China, by analyzing water samples from lakes, rivers and municipal sewage. Initially, 33 human pharmaceuticals were identified from East Lake without using purchasing standards. Of these, 29 were later confirmed by using standards, and quantified using the aforementioned SPE pretreatment method and LC-HRMS analysis in full MS scan mode. The 29 compounds included 8 antibiotics, 9 metabolites, and 12 miscellaneous pharmaceuticals. The highest proportions of pharmaceutical residues were detected downstream of the Yangtze River and in the lakes close to the central city. Metformin, cotinine, and trans-3-hydroxy cotinine, were frequently encountered in all the surface water samples. High concentrations (>120 ng/l) of caffeine, metformin, theobromine, and valsartan were detected in the surface water samples; the removal rates of these compounds in the municipal sewage treatment plant were also high. In contrast, although the concentrations of 4-AAA and metoprolol acid in the surface water were high, the removal rates of these residues in the sewage treatment plant were low.

Identification of Organic Iodine Compounds and Their Transformation Products in Edible Iodized Salt Using Liquid Chromatography-High Resolution Mass Spectrometry.

The consumption of edible iodized salt is a key strategy to control and eliminate iodine deficiency disorders worldwide. We herein report the identification of the organic iodine compounds present in different edible iodized salt products using liquid chromatography combined with high resolution mass spectrometry. A total of 38 organic iodine compounds and their transformation products (TPs) were identified in seaweed iodine salt from China. Our experiments confirmed that the TPs were generated by the replacement of I atoms from organic iodine compounds with Cl atoms. Furthermore, the organic iodine compound contents in 4 seaweed iodine salt samples obtained from different manufacturers were measured, with significant differences in content being observed. We expect that the identification of organic iodine compounds in salt will be important for estimating the validity and safety of edible iodized salt products.

A Decision-Based Modified Total Variation Diffusion Method for Impulse Noise Removal.

Impulsive noise removal usually employs median filtering, switching median filtering, the total variation L1 method, and variants. These approaches however often introduce excessive smoothing and can result in extensive visual feature blurring and thus are suitable only for images with low density noise. A new method to remove noise is proposed in this paper to overcome this limitation, which divides pixels into different categories based on different noise characteristics. If an image is corrupted by salt-and-pepper noise, the pixels are divided into corrupted and noise-free; if the image is corrupted by random valued impulses, the pixels are divided into corrupted, noise-free, and possibly corrupted. Pixels falling into different categories are processed differently. If a pixel is corrupted, modified total variation diffusion is applied; if the pixel is possibly corrupted, weighted total variation diffusion is applied; otherwise, the pixel is left unchanged. Experimental results show that the proposed method is robust to different noise strengths and suitable for different images, with strong noise removal capability as shown by PSNR/SSIM results as well as the visual quality of restored images.

Rapid Identification of Unknown Organic Iodine in Small-Volume Complex Biological Samples Based on Nanospray Mass Spectrometry Coupled with in-Tube Solid Phase Microextraction.

A new method for rapid screening of unknown organic iodine (OI) in small-volume complex biological samples was developed using in-tube solid phase microextraction (SPME) nanospray mass spectrometry (MS). The method proposed a new identification scheme for OI based on nanospray high-resolution mass spectrometry (HR-MS). The mass ranges of OI ions were confirmed using the t-MS2 scan mode first; then, the possible precursor ions of OI were selected and identified orderly in full MS/ddMS2 and t-MS2 scan modes. Besides, in-tube SPME was used for the pretreatment of small-volume biological samples, and it was the first time in-tube SPME combined with nanospray MS for OI identification. The whole analysis procedure took only 8 min and consumed 50 µL per sample. Using the new method, six kinds of OI added to urine and an unknown OI C12H23O11I in human milk were successfully identified. Moreover, the proposed identification scheme is also suitable for other ambient mass spectrometry (AMS) to determine unknown compounds with characteristic fragment ions.

Extended dissipative state estimation for uncertain discrete-time Markov jump neural networks with mixed time delays.

This paper is concerned with the problem of extended dissipativity-based state estimation for uncertain discrete-time Markov jump neural networks with finite piecewise homogeneous Markov chain and mixed time delays. The aim of this paper is to present a Markov switching estimator design method, which ensures that the resulting error system is extended stochastically dissipative. A triple-summable term is introduced in the constructed Lyapunov function and the reciprocally convex approach is utilized to bound the forward difference of the triple-summable term. The extended dissipativity criterion is derived in form of linear matrix inequalities. Numerical simulations are conducted to demonstrate the effectiveness of the proposed method.

Kernel Recursive Least-Squares Temporal Difference Algorithms with Sparsification and Regularization.

By combining with sparse kernel methods, least-squares temporal difference (LSTD) algorithms can construct the feature dictionary automatically and obtain a better generalization ability. However, the previous kernel-based LSTD algorithms do not consider regularization and their sparsification processes are batch or offline, which hinder their widespread applications in online learning problems. In this paper, we combine the following five techniques and propose two novel kernel recursive LSTD algorithms: (i) online sparsification, which can cope with unknown state regions and be used for online learning, (ii) L 2 and L 1 regularization, which can avoid overfitting and eliminate the influence of noise, (iii) recursive least squares, which can eliminate matrix-inversion operations and reduce computational complexity, (iv) a sliding-window approach, which can avoid caching all history samples and reduce the computational cost, and (v) the fixed-point subiteration and online pruning, which can make L 1 regularization easy to implement. Finally, simulation results on two 50-state chain problems demonstrate the effectiveness of our algorithms.

[Recent progress in multiple sequence alignment].

Multiple sequence alignment is one of the basic techniques in bioinformatics, and it plays a vital role in structure modeling, functional site prediction, and phylogenetic analysis. In this paper, we review the methodologies and recent advances in the multiple protein sequence alignment, e.g., speeding up the calculation of distances among sequences and employing the iterative refinement and consistency-based scoring function, with emphasis on the use of additional sequence and structural information for improving alignment quality.

A Poisson-based adaptive affinity propagation clustering for SAGE data.

Serial analysis of gene expression (SAGE) is a powerful tool to obtain gene expression profiles. Clustering analysis is a valuable technique for analyzing SAGE data. In this paper, we propose an adaptive clustering method for SAGE data analysis, namely, PoissonAPS. The method incorporates a novel clustering algorithm, Affinity Propagation (AP). While AP algorithm has demonstrated good performance on many different data sets, it also faces several limitations. PoissonAPS overcomes the limitations of AP using the clustering validation measure as a cost function of merging and splitting, and as a result, it can automatically cluster SAGE data without user-specified parameters. We evaluated PoissonAPS and compared its performance with other methods on several real life SAGE datasets. The experimental results show that PoissonAPS can produce meaningful and interpretable clusters for SAGE data.