Detecting Malware with an Ensemble Method Based on Deep Neural Network

Malware detection plays a crucial role in computer security. Recent researches mainly use machine learning based methods heavily relying on domain knowledge for manually extracting malicious features. In this paper, we propose MalNet, a novel malware detection method that learns features automatically from the raw data. Concretely, we first generate a grayscale image from malware file, meanwhile extracting its opcode sequences with the decompilation tool IDA. Then MalNet uses CNN and LSTM networks to learn from grayscale image and opcode sequence, respectively, and takes a stacking ensemble for malware classification. We perform experiments on more than 40,000 samples including 20,650 benign files collected from online software providers and 21,736 malwares provided by Microsoft. The evaluation result shows that MalNet achieves 99.88% validation accuracy for malware detection. In addition, we also take malware family classification experiment on 9 malware families to compare MalNet with other related works, in which MalNet outperforms most of related works with 99.36% detection accuracy and achieves a considerable speed-up on detecting efficiency comparing with two state-of-the-art results on Microsoft malware dataset

Uptake selectivity of methanesulfonic acid (MSA) on fine particles over polynya regions of the Ross Sea, Antarctica

The uptake of methanesulfonic acid (MSA) on existing particles is a major route of the particulate MSA formation, however, MSA uptake on different particles is still lacking in knowledge. Characteristics of MSA uptake on different aerosol particles were investigated in polynya (an area of open sea water surrounded by ice) regions of the Ross Sea, Antarctica. Particulate MSA mass concentrations, as well as aerosol population and size distribution, were observed simultaneously for the first time to access the uptake of MSA on different particles. The results show that MSA mass concentration does not always reflect MSA particle population in the marine atmosphere. MSA uptake on aerosol particle increases the particle size and changes aerosol chemical composition, but it does not increase the particle population. The uptake rate of MSA on particles is significantly influenced by aerosol chemical properties. Sea salt particles are beneficial for MSA uptake, as MSA-Na and MSA-Mg particles are abundant in the Na and Mg particles, accounting for 0.43 +/- 0.21 and 0.41 +/- 0.20 of the total Na and Mg particles, respectively. However, acidic and hydrophobic particles suppress the uptake of MSA, as MSA-EC (elemental carbon) and MSA-SO42- particles account for only 0.24 +/- 0.68 and 0.26 +/- 0.47 of the total EC and SO42- particles, respectively. The results extend the knowledge of the formation and environmental behavior of MSA in the marine atmosphere.Peer reviewe

DMS sea-to-air fluxes and their influence on sulfate aerosols over the Southern Ocean, south-east Indian Ocean and north-west Pacific Ocean

Environmental context The ocean-produced dimethyl sulfide (DMS) molecule is thought to affect cloud formation and the solar radiation budget at the Earth's surface, hence playing an important role in regulating climate. In this study, we calculated the DMS sea-to-air flux across the Southern Ocean, south-east Indian Ocean and north-west Pacific Ocean, and analysed the influence of DMS fluxes on sulfate aerosols. These results improved our understanding of the effects of DMS emissions on sulfate compounds in the atmosphere over the global ocean. Oceanic dimethyl sulfide (DMS) is the most abundant biogenic sulfur compound emitted into the atmosphere and could indirectly regulate the global climate by impacting end product sulfate aerosols. DMS emissions and their influence on sulfate aerosols, i.e. methanesulfonic acid (MSA) and non-sea-salt sulfate (nss-SO42-), were investigated over the Atlantic Ocean and Indian Ocean sectors of the Southern Ocean (SO), the south-east Indian Ocean, and the north-west Pacific Ocean from February to April 2014 during the 30th Chinese National Antarctic Research Expedition. We found a strong large-scale DMS source in the marginal sea ice zone from 34 degrees W to 14 degrees E of the SO (south of 60 degrees S), in which the mean flux was 49.0 +/- 65.6 mu mol m(-2) d(-1) (0.6-308.3 mu mol m(-2) d(-1), n = 424). We also found a second large-scale DMS source in the South Subtropical Front (similar to 40 degrees S, up to 50.8 mu mol m(-2) d(-1)). An inconsistency between concentrations of atmospheric sulfate compounds and DMS emissions along the cruise track was observed. The horizontal advection of air masses was likely the main reason for this discrepancy. Finally, the biological exposure calculation results also indicated that it is very difficult to observe a straightforward relationship between oceanic biomass and atmospheric MSA

Seasonal variations in aerosol compositions at Great Wall Station in Antarctica

High volume aerosol samplers at Great Wall Station in Antarctica were used to collect 73 aerosol samples between January 2012 and November 2013. The main ions in these aerosol samples, Cl−, NO3−, SO4 2−, Na+, K+, Ca2+, Mg2+, NH4+, as well as methane sulfonic acid, were analyzed using ion chromatography. Trace metals in these samples, including Pb, Cu, Cd, V, Zn, Fe, and Al, were determined by inductively-coupled plasma mass spectrometry. Results showed that sea salt was the main component in aerosols at Great Wall Station. Most ions exhibited significant seasonal variations, with higher concentrations in summer and autumn than in winter and spring. Variations in ions and trace metals were related to several processes (or sources), including sea salt emission, secondary aerosol formation, and anthropogenic pollution from both local and distant sources. The sources of ions and trace metals were identified using enrichment factor, correlation, and factor analyses. Clearly, Na+, K+, Ca2+, and Mg2+ were from marine sources, while Cu, Pb, Zn, and Cd were from anthropogenic pollution, and Al and V were mainly from crustal sources

LSTM-Based Hierarchical Denoising Network for Android Malware Detection

Mobile security is an important issue on Android platform. Most malware detection methods based on machine learning models heavily rely on expert knowledge for manual feature engineering, which are still difficult to fully describe malwares. In this paper, we present LSTM-based hierarchical denoise network (HDN), a novel static Android malware detection method which uses LSTM to directly learn from the raw opcode sequences extracted from decompiled Android files. However, most opcode sequences are too long for LSTM to train due to the gradient vanishing problem. Hence, HDN uses a hierarchical structure, whose first-level LSTM parallelly computes on opcode subsequences (we called them method blocks) to learn the dense representations; then the second-level LSTM can learn and detect malware through method block sequences. Considering that malicious behavior only appears in partial sequence segments, HDN uses method block denoise module (MBDM) for data denoising by adaptive gradient scaling strategy based on loss cache. We evaluate and compare HDN with the latest mainstream researches on three datasets. The results show that HDN outperforms these Android malware detection methods,and it is able to capture longer sequence features and has better detection efficiency than N-gram-based malware detection which is similar to our method

Characteristics and Provenance Implications of Rare Earth Elements and Nd Isotope in PM2.5 in a Coastal City of Southeastern China

The source apportionment of fine particulate matters, especially PM2.5, has drawn great attention worldwide. Since rare earth elements (REEs) and Nd isotopes can serve as source tracers, in this study, the characteristics and provenance implications of REEs and Nd isotopes in PM2.5 of four seasons in Xiamen city, China, were investigated. The range of the ratios of ΣREE to PM2.5 was 1.04 × 10−5 to 8.06 × 10−4, and the mean concentration of REEs in PM2.5 were in the order of spring > autumn > winter > summer. According to the geoaccumulation index (Igeo), spring was the season in which anthropogenic sources had the greatest impact on the REEs in PM2.5. The chondrite-normalized REE distribution patterns exhibited light rare earth elements (LREEs, including La, Ce, Pr, Nd, Pm, Sm and Eu) enrichment and a flat heavy rare earth elements (HREEs, including Gd, Tb, Dy, Ho, Er, Tm, Yb and Lu) pattern. Significant negative Eu anomalies and no significant Ce anomalies were observed in the PM2.5. The results of La-Ce-Sm ternary plots indicated that the REEs in the PM2.5 might be related to both natural and anthropogenic sources. Combined with the Nd isotope, the 143Nd/144Nd versus Ce/Ce* plot further illustrated that the REEs in the PM2.5 seemed to mostly originate from multiple potential sources, in which vehicle exhaust emissions, coal burning and cement dust made a great contribution to REEs in PM2.5

Underway Measurement of Dissolved Inorganic Carbon (DIC) in Estuarine Waters

Dissolved inorganic carbon (DIC) is an important parameter of the marine carbonate system. Underway analyses of DIC are required to describe spatial and temporal changes of DIC in marine systems. In this study, we developed a microvolume flow detection method for the underway determination of DIC in marine waters, using gas-diffusion flow analysis in conjunction with electrical conductivity (EC) measurement. Only an acid carrier reagent (0.2 mol.L−1) and an ultrapure water acceptor are required for the DIC monitoring system. In this system, a sampling loop (100 µL) is used to quantify the injection sample volume, allowing micro-sample volume detection. The water sample reacts with the acid reagent to convert carbonate and bicarbonate species into CO2. The water sample is then carried into a gas-diffusion assembly, where the CO2 diffuses from the sampling stream into the acceptor stream. CO2 in the acceptor is detected subsequently by an electrical conductivity. The limit of DIC detection using ultrapure water is 0.16 mM. A good repeatability is obtained, with a relative standard deviation (RSD) of 0.56% (1 mM, n = 21). The time interval for detecting one sample is 5 min. During the observation period, measurements can be switched between standard solutions and water samples automatically. Accuracy and precision of the instrument is sufficient for the underway observation of marine DIC in estuarine waters

Mapping Multi-Temporal Population Distribution in China from 1985 to 2010 Using Landsat Images via Deep Learning

Fine knowledge of the spatiotemporal distribution of the population is fundamental in a wide range of fields, including resource management, disaster response, public health, and urban planning. The United Nations’ Sustainable Development Goals also require the accurate and timely assessment of where people live to formulate, implement, and monitor sustainable development policies. However, due to the lack of appropriate auxiliary datasets and effective methodological frameworks, there are rarely continuous multi-temporal gridded population data over a long historical period to aid in our understanding of the spatiotemporal evolution of the population. In this study, we developed a framework integrating a ResNet-N deep learning architecture, considering neighborhood effects with a vast number of Landsat-5 images from Google Earth Engine for population mapping, to overcome both the data and methodology obstacles associated with rapid multi-temporal population mapping over a long historical period at a large scale. Using this proposed framework in China, we mapped fine-scale multi-temporal gridded population data (1 km × 1 km) of China for the 1985–2010 period with a 5-year interval. The produced multi-temporal population data were validated with available census data and achieved comparable performance. By analyzing the multi-temporal population grids, we revealed the spatiotemporal evolution of population distribution from 1985 to 2010 in China with the characteristic of concentration of the population in big cities and the contraction of small- and medium-sized cities. The framework proposed in this study demonstrates the feasibility of mapping multi-temporal gridded population distribution at a large scale over a long period in a timely and low-cost manner, which is particularly useful in low-income and data-poor areas

Characteristics of Particulate Carbon in Precipitation during the Rainy Season in Xiamen Island, China

Measuring wet deposition of organic carbon (OC) and elemental carbon (EC) aerosol is crucial for the understanding of their circulation and climate effect. To further understand the wet deposition of particulate carbon (OC and EC), precipitation samples were collected from April to August 2014 on Xiamen Island in China. EC and water insoluble organic carbon (WIOC) concentrations were analyzed using a thermal optical method to investigate temporal variations and wet deposition fluxes. The average EC and WIOC concentrations were 7.3 μgC·L−1 and 495.3 μgC·L−1, respectively, which are both comparable to the results reported in European areas. EC and WIOC concentrations were higher in spring than in summer. Higher EC concentrations were found in April, which were probably associated with the transport of air masses from northern continental areas. Higher WIOC concentrations were found in May and were mainly attributed to air masses from the South China Sea. Lower concentrations of EC and WIOC in the summer were primarily due to the clean air masses transported from the ocean. The wet deposition flux was calculated as the product of concentration and precipitation amount. Average wet deposition fluxes of EC and WIOC were estimated to be 0.6 mgC·m−2·month−1 and 36.7 mgC·m−2·month−1, respectively. Wet deposition fluxes of EC and WIOC exhibited similar concentration trends. The largest flux in EC wet deposition occurred in April (1.8 mgC·m−2·month−1), while the largest flux in WIOC wet deposition occurred in May (63.1 mgC·m−2·month−1)