An Analysis of Android Malware Classification Services

The increasing number of Android malware forced antivirus (AV) companies to rely on automated classification techniques to determine the family and class of suspicious samples. The research community relies heavily on such labels to carry out prevalence studies of the threat ecosystem and to build datasets that are used to validate and benchmark novel detection and classification methods. In this work, we carry out an extensive study of the Android malware ecosystem by surveying white papers and reports from 6 key players in the industry, as well as 81 papers from 8 top security conferences, to understand how malware datasets are used by both. We, then, explore the limitations associated with the use of available malware classification services, namely VirusTotal (VT) engines, for determining the family of an Android sample. Using a dataset of 2.47 M Android malware samples, we find that the detection coverage of VT’s AVs is generally very low, that the percentage of samples flagged by any 2 AV engines does not go beyond 52%, and that common families between any pair of AV engines is at best 29%. We rely on clustering to determine the extent to which different AV engine pairs agree upon which samples belong to the same family (regardless of the actual family name) and find that there are discrepancies that can introduce noise in automatic label unification schemes. We also observe the usage of generic labels and inconsistencies within the labels of top AV engines, suggesting that their efforts are directed towards accurate detection rather than classification. Our results contribute to a better understanding of the limitations of using Android malware family labels as supplied by common AV engines.

Insights into elections: An ensemble bot detection coverage framework applied to the 2018 U.S. midterm elections

The participation of automated software agents known as social bots within online social network (OSN) engagements continues to grow at an immense pace. Choruses of concern speculate as to the impact social bots have within online communications as evidence shows that an increasing number of individuals are turning to OSNs as a primary source for information. This automated interaction proliferation within OSNs has led to the emergence of social bot detection efforts to better understand the extent and behavior of social bots. While rapidly evolving and continually improving, current social bot detection efforts are quite varied in their design and performance characteristics. Therefore, social bot research efforts that rely upon only a single bot detection source will produce very limited results. Our study expands beyond the limitation of current social bot detection research by introducing an ensemble bot detection coverage framework that harnesses the power of multiple detection sources to detect a wider variety of bots within a given OSN corpus of Twitter data. To test this framework, we focused on identifying social bot activity within OSN interactions taking place on Twitter related to the 2018 U.S. Midterm Election by using three available bot detection sources. This approach clearly showed that minimal overlap existed between the bot accounts detected within the same tweet corpus. Our findings suggest that social bot research efforts must incorporate multiple detection sources to account for the variety of social bots operating in OSNs, while incorporating improved or new detection methods to keep pace with the constant evolution of bot complexity.

PELAKSANAAN KEGIATAN PENGENDALIAN PENYAKIT PNEUMONIA PADA BALITA DI WILAYAH KERJA PUSKESMAS I PURWOKERTO TIMUR TAHUN 2018

By 2015 the WHO reports nearly six million children under five die and 16% of that number is caused by pneumonia as the number one killer of children under five in the world. Pneumonia is a respiratory disease that is still a public health problem. In 2016 found 3,005 cases of Pneumonia that occurred in Banyumas district. Pneumonia is a lot of happening in East Purwokerto area, which found 274 cases. The main purpose of this study is to provide an overview of the implementation of Pneumonia disease control activities in under-five children in terms of surveillance, determinants and control efforts. The research method was the descriptive method and the subjects were puskesmas office and 20 patients with Pneumonia. The general and specific data was collected through interview, observation, and field measurement. By 2015 until 2017 pneumonia sufferer detection coverage could not reach the target. The research result shows Pneumonia patient consist from 11 men (55%) and 19 women (45%). Pneumonia commonly occur toward 1-5 years old group of age. Pneumonia sufferers mostly occur in patients who have homes that do not meet the conditions of ventilation and lighting conditions that do not meet the requirements. The highest number of patients in the Arcawinangun area was 13 patients and the peak incidence of pneumonia occurred in April, ie 5 people (25%). Based on research, in addition to overcome Pneumonia, Puskesmas further increase the sufferer detection of Pneumonia's discovery and for the society if they will build a house or will renovate the house need to pay attention to the aspect of healthy house and provide a complete immunization toddler

Use of hormonal subdoses applied in Bai Hui acupuncture in estrus synchronization protocols for goats

The objective of this study was to evaluate the efficacy of delivering reduced doses of hormones via the Bai Hui acupoint in estrus synchronization in goats. A total of 40 goats received intravaginal sponges with medroxyprogesterone acetate for 7 days. The goats were then randomly distributed into 5 treatment: T1 - application of 132.5 ?g of cloprostenol and 300 IU of equine chorionic gonadotropin (eCG), both by intramuscular injection (IM); T2 - application of 39.75 ?g cloprostenol at the Bai Hui acupoint, and 300 IU of eCG by IM; T3 - application of 132.5 ?g of cloprostenol by IM, and 90 IU of eCG at the Bai Hui acupoint; T4 - application of 39.75?g of cloprostenol and 90 UI of eCG, both in Bai Hui and T5 acupuncture: application of 39.75?g of cloprostenol and 90 UI of eCG, both applied in false acupoint. The goats were subjected to an estrus synchronization protocol and monitored for estrus detection, coverage and evaluation of reproductive parameters to detect entry into estrus. The data were subjected to normality tests, followed by appropriate statistical analyses of each variable. There was no significant difference (P > 0.05) in the percentage of animals in estrus (95.00 ± 11.18%), interval between sponge removal and beginning of estrus (49.72 ± 8.93 h), interval between sponge removal and end of estrus (76.84 ± 11.98 h), duration of estrus (27.08 ± 8.68 h), size of the largest follicle (6.82 ± 0.44 mm), interval between sponge removal and ovulation (78.28 ± 10.82 h), time from ovarian onset to estrus (28.52 ± 5.44 h), follicular growth rate (0.86 ± 0.29 mm/day), number of ovulations (1.32 ± 0.23), plasma progesterone concentration at 7 days after ovulation (10.28 ± 1.65 ng.mL-1), and gestation rate at 30 days after the beginning of estrus (75 ± 12.5%). However, the cost of the synchronization protocol per animal was 43.42% lower in treatments 4 and 5 (30% of the doses) than in treatment 1 (100% of the dose). Ovulation and estrus were efficiently synchronized with the use of 39.75 ?g of sodium cloprostenol and 90 UI of eCG, applied at the Bai Hui acupoint or at a false acupoint.

Target Detection Coverage Algorithm Based on 3D-Voronoi Partition for Three-Dimensional Wireless Sensor Networks

The detection of target events is an important research area in the field of wireless sensor networks (WSNs). In recent years, many researchers have discussed the problem of WSN target coverage in a two-dimensional (2D) coordinate system. However, the target detection problem in a 3D coordinate system has not been investigated extensively, and it is difficult to improve the network coverage ratio while ensuring reliable performance of WSN. In addition, sensor nodes that are initially deployed randomly cannot achieve accurate target coverage in practice. Moreover, it is necessary to consider the energy consumption factor owing to the limited energy of the sensor node itself. Hence, with the objective of addressing the target event coverage problem of WSNs in 3D space applications, this paper proposes a target detection coverage algorithm based on 3D-Voronoi partitioning for WSNs (3D-VPCA) in order to ensure reliable performance of the entire network. First, we extend Voronoi division based on the 2D plane, which allows 3D-Voronoi partitioning of sensor nodes in 3D regions. Then, it is optimized according to the 3D-Voronoi neighbouring node partitioning characteristics and combined with the improved algorithm. Next, we set the priority coverage mechanism and introduce the correlation force between the target point and the sensor node in the algorithm, so that the sensor node can move to the target position for accurate coverage. Finally, we carry out related simulation experiments to evaluate the performance and accuracy of the proposed algorithm. The results show that the proposed algorithm can effectively improve the coverage performance of the network while ensuring a high overall coverage ratio.