Malware Detection in Android Apps Using Static Analysis

Frequency of malware attacks because Android apps are increasing day by day. Current studies have revealed startling facts about data harvesting incidents, where user’s personal data is at stake. To preserve privacy of users, a permission induced risk interface MalApp to identify privacy violations rising from granting permissions during app installation is proposed. It comprises of multi-fold process that performs static analysis based on app’s category. First, concept of reverse engineering is applied to extract app permissions to construct a Boolean-valued permission matrix. Second, ranking of permissions is done to identify the risky permissions across category. Third, machine learning and ensembling techniques have been incorporated to test the efficacy of the proposed approach on a data set of 404 benign and 409 malicious apps. The empirical studies have identified that our proposed algorithm gives a best case malware detection rate of 98.33%. The highlight of interface is that any app can be classified as benign or malicious even before running it using static analysis.

Malware Detection in Android Apps Using Static Analysis

Frequency of malware attacks because Android apps are increasing day by day. Current studies have revealed startling facts about data harvesting incidents, where user’s personal data is at stake. To preserve privacy of users, a permission induced risk interface MalApp to identify privacy violations rising from granting permissions during app installation is proposed. It comprises of multi-fold process that performs static analysis based on app’s category. First, concept of reverse engineering is applied to extract app permissions to construct a Boolean-valued permission matrix. Second, ranking of permissions is done to identify the risky permissions across category. Third, machine learning and ensembling techniques have been incorporated to test the efficacy of the proposed approach on a data set of 404 benign and 409 malicious apps. The empirical studies have identified that our proposed algorithm gives a best case malware detection rate of 98.33%. The highlight of interface is that any app can be classified as benign or malicious even before running it using static analysis.

Detection Rate and Clinical Impact of PET/CT with 18F-FACBC in Patients with Biochemical Recurrence of Prostate Cancer: A Retrospective Bicentric Study

Our aim was to assess the detection rate (DR) of positron emission computed tomography (PET/CT) with anti-1-amino-3-[18F]-flurocyclobutane-1-carboxylic acid (18F-FACBC) in patients with biochemical recurrence (BCR) from prostate cancer (PC). As a secondary endpoint, we evaluated 18F-FACBC PET/CT’s impact on patients management. Clinical records of 81 patients submitted to 18F-FACBC PET/CT due to PC BCR in two Italian Nuclear Medicine Units were retrospectively assessed. DR was gauged in the whole cohort and stratifying patients by discrete intervals of PSA levels. PET/CT’s impact on clinical management was scored as (1) major if it entailed an intermodality change (e.g., from systemic to loco-regional therapy); (2) minor if it led to an intramodality change (e.g., modified radiotherapy field). PET/CT’s DR resulted in 76.9% in the whole cohort, with a positive predictive value of 96.7%. Stratified by PSA quartile intervals, PET/CT’s DR was 66.7%, 71.4%, 78.9% and 90% for PSA 0.2–0.57 ng/mL, 0.58–0.99 ng/mL, 1–1.5 ng/mL and >1.5 ng/mL without significant difference among groups (p = 0.81). The most common sites of relapse were prostate bed and pelvic lymph nodes (59.3%). PET/CT impacted on clinical management in 33/81 cases (40.7%), leading to a major change in 30 subjects (90.9%). 18F-FACBC PET/CT localized recurrence in patients with BCR, with meaningful DR also at low PSA levels and significantly impacted on clinical management.

On the Detection Capabilities of Signature-Based Intrusion Detection Systems in the Context of Web Attacks

Signature-based Intrusion Detection Systems (SIDS) play a crucial role within the arsenal of security components of most organizations. They can find traces of known attacks in the network traffic or host events for which patterns or signatures have been pre-established. SIDS include standard packages of detection rulesets, but only those rules suited to the operational environment should be activated for optimal performance. However, some organizations might skip this tuning process and instead activate default off-the-shelf rulesets without understanding its implications and trade-offs. In this work, we help gain insight into the consequences of using predefined rulesets in the performance of SIDS. We experimentally explore the performance of three SIDS in the context of web attacks. In particular, we gauge the detection rate obtained with predefined subsets of rules for Snort, ModSecurity and Nemesida using seven attack datasets. We also determine the precision and rate of alert generated by each detector in a real-life case using a large trace from a public webserver. Results show that the maximum detection rate achieved by the SIDS under test is insufficient to protect systems effectively and is lower than expected for known attacks. Our results also indicate that the choice of predefined settings activated on each detector strongly influences its detection capability and false alarm rate. Snort and ModSecurity scored either a very poor detection rate (activating the less-sensitive predefined ruleset) or a very poor precision (activating the full ruleset). We also found that using various SIDS for a cooperative decision can improve the precision or the detection rate, but not both. Consequently, it is necessary to reflect upon the role of these open-source SIDS with default configurations as core elements for protection in the context of web attacks. Finally, we provide an efficient method for systematically determining which rules deactivate from a ruleset to significantly reduce the false alarm rate for a target operational environment. We tested our approach using Snort’s ruleset in our real-life trace, increasing the precision from 0.015 to 1 in less than 16 h of work.

Non-invasive laparoscopic detection of small tumors of the digestive tract using inductive sensors of proximity

The precise location of gastric and colorectal tumors is of paramount importance for the oncological surgeon as it dictates the limits of resection and the extent of lymphadenectomy. However, this task proves sometimes to be very challenging, especially in the laparoscopic setting when the tumors are small, have a soft texture, and do not invade the serosa. In this view, our research team has developed a new instrument adapted to minimally-invasive surgery, and manipulated solely by the operating surgeon which has the potential to locate precisely tumors of the digestive tract. It consists of an inductive proximity sensor and an electronic block encapsulated into an autoclavable stainless-steel cage that works in tandem with an endoscopic hemostatic clip whose structure was modified to increase detectability. By scanning the serosal side of the colon or stomach, the instrument is capable to accurately pinpoint the location of the clip placed previously during diagnostic endoscopy on the normal bowel mucosa, adjacent to the tumor. In the current in-vivo experiments performed on large animals, the modified clips were transported without difficulties to the point of interest and attached to the mucosa of the bowel. Using a laparoscopic approach, the detection rate of this system reached 65% when the sensor scanned the bowel at a speed of 0.3 cm/s, and applying slight pressure on the serosa. This value increased to 95% when the sensor was guided directly on the point of clip attachment. The detection rate dropped sharply when the scanning speed exceeded 1 cm/s and when the sensor-clip distance exceeded the cut-off value of 3 mm. In conclusion, the proposed detection system demonstrated its potential to offer a swift and convenient solution for the digestive laparoscopic surgeons, however its detection range still needs to be improved to render it useful for the clinical setting.

Artificial Intelligence-Aid Colonoscopy Vs. Conventional Colonoscopy for Polyp and Adenoma Detection: A Systematic Review of 7 Discordant Meta-Analyses

Objectives: Multiple meta-analyses which investigated the comparative efficacy and safety of artificial intelligence (AI)-aid colonoscopy (AIC) vs. conventional colonoscopy (CC) in the detection of polyp and adenoma have been published. However, a definitive conclusion has not yet been generated. This systematic review selected from discordant meta-analyses to draw a definitive conclusion about whether AIC is better than CC for the detection of polyp and adenoma.Methods: We comprehensively searched potentially eligible literature in PubMed, Embase, Cochrane library, and China National Knowledgement Infrastructure (CNKI) databases from their inceptions until to April 2021. Assessment of Multiple Systematic Reviews (AMSTAR) instrument was used to assess the methodological quality. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist was used to assess the reporting quality. Two investigators independently used the Jadad decision algorithm to select high-quality meta-analyses which summarized the best available evidence.Results: Seven meta-analyses met our selection criteria finally. AMSTAR score ranged from 8 to 10, and PRISMA score ranged from 23 to 26. According to the Jadad decision algorithm, two high-quality meta-analyses were selected. These two meta-analyses suggested that AIC was superior to CC for colonoscopy outcomes, especially for polyp detection rate (PDR) and adenoma detection rate (ADR).Conclusion: Based on the best available evidence, we conclude that AIC should be preferentially selected for the route screening of colorectal lesions because it has potential value of increasing the polyp and adenoma detection. However, the continued improvement of AIC in differentiating the shape and pathology of colorectal lesions is needed.

Fluorescein Leakage and Optical Coherence Tomography Angiography Features of Microaneurysms in Diabetic Retinopathy

Background and Objective. To correlate optical coherence tomography angiography (OCTA) characteristics of diabetic microaneurysms (MAs) with leakage status on fluorescein angiography (FA). Patients and Methods. 167 MAs from 39 diabetic eyes were analyzed using OCTA and FA simultaneously. The characteristics of MAs on OCTA en face, OCT en face, and OCT B-scan with flow overlay were evaluated and correlated with fluorescein leakage status. Results. Thirty-six, fifty-two, and seventy-nine MAs showed no, mild, and severe leakage on FA, respectively. Most MAs (61.7%) were centered in the inner nuclear layer. Cystoid spaces were observed adjacent to 60 (35.9%) MAs. MAs with severe leakage had a statistically higher flow proportion compared to MAs with no or mild leakage (both P < 0.001 ). Only 112 MAs (67.1%) were visualized in the OCTA en face images, while 165 MAs (98.8%) could be visualized in the OCT en face images. The location of MAs did not associate significantly with FA leakage status. The presence of nearby cystoid spaces and higher flow proportion by OCT B-scan with flow overlay correlated significantly with FA leakage status. Conclusion. The flow proportion of MAs observed on OCT B-scans with flow overlay might be a potential biomarker to identify leaking MAs. A combination of OCT B-scan, OCT en face, and OCTA en face images increased the detection rate of diabetic MAs in a noninvasive way.

Descriptive study of chest x-ray examination in mandatory annual health examinations at the workplace in Japan

The utility of chest x-ray examination (CXR) in mandatory annual health examinations for occupational health is debatable in Japan. This study aimed to provide basic data to consider future policies for mandatory annual health examinations in the workplace. A nationwide descriptive survey was performed to determine the rate of detection of tuberculosis, lung cancer, and other diseases through CXR in organizations associated with National Federation of Industrial Health Association. The rate of finding on CXR conducted during annual health examinations in FY2016 was evaluated. Data regarding diagnosis based on follow-up examination findings were obtained and compared with the national statistics. In addition, CXR findings were compared with the results of low-dose lung computed tomography performed at the Hitachi Health Care Center. From 121 surveyed institutions, 88 institutions with 8,669,403 workers were included. For all ages, 1.0% of examinees required follow-up examination. Among 4,764,985 workers with diagnosis data, the tuberculosis detection rate was 1.8–5.3 per 100,000 persons. For Lung cancer, 3,688,396 workers were surveyed, and 334 positive cases were detected. The lung cancer detection rate using CXR was 9.1–24.4 per 100,000 persons. From 164 cases with information regarding the clinical stage, 72 (43.9%) had Stage I lung cancer. From 40,045 workers who underwent low-dose computed tomography multiple times, 31 lung cancer cases, all with Stage I disease, were detected (detection rate: 77.4 per 100,000 persons). Our findings suggest that CXR plays a little role in the detection of active tuberculosis. With regard to LC screening, the detection rate of LC by CXR was lower, approximately 50%, than the expected rate (41.0 per 100,000 persons) of LC morbidity based on the age–sex distribution of this study population. However, the role of CXR for LC screening cannot be mentioned based on this result, because assessment of mortality reduction is essential to evaluate the role.

erm (T)-Mediated Macrolide-Lincosamide Resistance in Streptococcus suis

Macrolide and lincosamide resistance due to the presence of erm (T) have posed a challenge for the treatment of Gram-positive pathogens. Because of the low detection rate of erm (T) gene among the S. suis population due to the fitness cost of the erm (T)-carrying plasmid and ICE, the presence of erm (T) in S. suis and its potential transmission to other Gram-positive pathogens will be of important significance.

Detection Rate of Bird Species and What It Depends on: Tips for Field Surveys

Bird counting inevitably suffers from imperfect detection, which varies across species, habitats, period of the day, and seasons. Although various modeling techniques have recently been developed to account for this phenomenon, the biological basis of natural variation in detection remains insufficiently known. This study examined the bird species’ detection rate throughout the day, considering their body mass and diet type, concerning the environment and weather characteristics. Species detection rates were significantly affected by the number of individuals of that species but were unrelated to body mass. Overall, species with the highest detection rate were Corn bunting, Blackbird, European robin, House sparrow and Common chiffchaff. Granivores-insectivores and insectivores showed significant differences in detection rates throughout the day among habitats, with higher detection rates in grasslands during the afternoon. Insectivores showed higher detection rates in farmland during midday (warmest time of the day). Granivores, omnivores and scavengers did not show changes in detection rates in different day periods. Such patterns in daily detection rates were significant even when considering abundance and total species richness in each community. Finally, cloudiness was unrelated to the overall detection rate of birds, while temperature and wind affected detection rates in some guilds. Our findings provide some advice for choosing a suitable ornithological sampling method by considering the avian communities composition in combination with the type of environment, the diet of bird species, and the period of the day.