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This research paper presents MLDroid—a web-based framework—which helps to detect malware from Android devices. Due to increase in the popularity of Android devices, malware developers develop malware on daily basis to threaten the system integrity and user’s privacy. The proposed framework detects malware from Android apps by performing its dynamic analysis. To detect malware from real-world apps, we trained our proposed framework by selecting features which are gained by implementing feature selection approaches. Further, these selected features help to build a model by considering different machine learning algorithms. Experiment was performed on 5,00,000 plus Android apps. Empirical result reveals that model developed by considering all the four distinct machine learning algorithms parallelly (i.e., deep learning algorithm, farthest first clustering, Y-MLP and nonlinear ensemble decision tree forest approach) and rough set analysis as a feature subset selection algorithm achieved the highest detection rate of 98.8% to detect malware from real-world apps.

With the exponential growth in Android apps, Android based devices are becoming victims of target attackers in the “silent battle” of cybernetics. To protect Android based devices from malware has become more complex and crucial for academicians and researchers. The main vulnerability lies in the underlying permission model of Android apps. Android apps demand permission or permission sets at the time of their installation. In this study, we consider permission and API calls as features that help in developing a model for malware detection. To select appropriate features or feature sets from thirty different categories of Android apps, we implemented ten distinct feature selection approaches. With the help of selected feature sets we developed distinct models by using five different unsupervised machine learning algorithms. We conduct an experiment on 5,00,000 distinct Android apps which belongs to thirty distinct categories. Empirical results reveals that the model build by considering rough set analysis as a feature selection approach, and farthest first as a machine learning algorithm achieved the highest detection rate of 98.8% to detect malware from real-world apps.

Cloud computing is a computer science paradigm that has grown significantly in recent years. It provides on-demand access to a diverse set of software, infrastructure and platform services through the internet. However, due to their diversity and functional similarities, selecting trustworthy cloud services is a challenge. The absence of adequate trust evaluation methods for cloud services has hampered the widespread adoption of cloud computing. To address this issue and assist customers in selecting trustworthy cloud services, this paper presents a framework Selection of Trustworthy Cloud Services (SelTCS). SelTCS selects services by combining objective and subjective trust. A novel objective trust assessment approach has been presented that prioritizes quality-of-service attributes according to user preferences. Also, a novel subjective trust assessment approach is proposed which evaluates trust as a combination of reputation assessment based on aggregated user feedback that employs a modified hypertext induced topic search-based algorithm for identifying and removing malicious users, and direct trust based on users’ own experiences gained through direct interactions. Experiments using the Quality of Web Services (QWS) version 1.0 and Epinions datasets reveal that SelTCS greatly enhances the accuracy of trust evaluation and is more effective than existing approaches at detecting malicious user ratings.

This paper proposes a novel hybrid multi-objective optimization algorithm named HMOSHSSA by synthesizing the strengths of Multi-objective Spotted Hyena Optimizer (MOSHO) and Salp Swarm Algorithm (SSA). HMOSHSSA utilizes the exploration capability of MOSHO to explore the search space effectively and leader and follower selection mechanism of SSA to achieve global best solution with faster convergence. The proposed algorithm is evaluated on 24 benchmark test functions, and its performance is compared with seven well-known multi-objective optimization algorithms. The experimental results demonstrate that HMOSHSSA acquires very competitive results and outperforms other algorithms in terms of convergence speed, search-ability and accuracy. Additionally, HMOSHSSA is also applied on seven well-known engineering problems to further verify its efficacy. The results reveal the effectiveness of proposed algorithm toward solving real-life multi-objective optimization problems.

In the present computer era, the vulnerabilities inherent in the Internet architecture enable various kinds of attacks. Distributed Denial of Service (DDoS) is one of such prominent attack that is a lethal threat to Internet domain that harnesses its computing and communication resources. The increase in network traffic rates of legitimate traffic and its flow similarity with attack traffic has made the DDoS detection very difficult despite deployment of diversified defense solutions. The ISPs are bound to invest heavily to counter such problems which has a significant impact on company finances. To provide uninterrupted quality services to the end users, ISPs needs to deploy a distributed solution for timely detection and discrimination of attack and behaviorally similar flash events (FE) traffic. Such distributed defense systems can be deployed at source-end, intermediate network-end or at the victim-end location. Since the volume of traffic to be analyzed is very large, the detection accuracy and low computational complexity of the proposed defense solution is always a challenging problem. This paper proposes an ISP level distributed, collaborative and automated (D-CAD) defense system for detecting DDoS attacks and FEs, and has the capability to effectively distinguishing the two. Additionally, D-CAD defense system is also capable of categorizing FE traffic and has low computational complexity. The proposed system is validated in novel software defined networks (SDN) using Mininet emulator. The results show that D-CAD defense system outperformed its existing counterparts on various detection system evaluation metrics.

The objectives of the study were to study the incidence of various degrees of severity of thrombocytopenia in septic shock, the risk factors for its development and the correlation with clinical outcome. Complete blood counts, chemistry panel, arterial lactate, serum cortisol, APACHE II score, logistic organ dysfunction score and SOFA score were determined in 69 septic shock patients within 24 hours of admission or onset of septic shock. We followed the patients until they died or for six months to determine the mortality rate. The incidence of thrombocytopenia in our study group was 55%. Patients with thrombocytopenia had significantly higher serum creatinine, SOFA score, vasopressor requirement, lower P a O 2 /FiO 2 ratio and higher mortality than those without thrombocytopenia (P <0.05). Higher SOFA score, low P a O 2 /FiO 2 ratio and high vasopressor dose were independent risk factors for development of thrombocytopenia. The presence of thrombocytopenia had significant correlation with SOFA score (P=0.008). On receiver-operator characteristic curve analysis, platelet count was found to be predictive of increased mortality (area under curve=0.56). Thrombocytopenic patients had 1.4 times the risk of mortality and lower survival probability at six months (log rank test P=0.03). In conclusion, thrombocytopenia is common in septic shock and is associated with worse clinical outcome. Higher SOFA score, low P a O 2 /FiO 2 ratio and high vasopressor dose are independent risk factors for development of thrombocytopenia in septic shock.

A bstract We measure the branching fractions and CP asymmetries for the singly Cabibbo-suppressed decays D 0 → π + π − η , D 0 → K + K − η , and D 0 → ϕη , using 980 fb − 1 of data from the Belle experiment at the KEKB e + e − collider. We obtain B D 0 → π + π − η = 1.22 ± 0.02 stat ± 0.02 syst ± 0.03 B ref × 10 − 3 , B D 0 → K + K − η = 1.80 − 0.06 + 0.07 stat ± 0.04 syst ± 0.05 B ref × 10 − 4 , B D 0 → ϕη = 1.84 ± 0.09 stat ± 0.06 syst ± 0.05 B ref × 10 − 4 , where the third uncertainty ( B ref ) is from the uncertainty in the branching fraction of the reference mode D 0 → K − π + η . The color-suppressed decay D 0 → ϕη is observed for the first time, with very high significance. The results for the CP asymmetries are A CP D 0 π + π − η = 0.9 ± 1.2 stat ± 0.5 syst % , A CP D 0 → K + K − η = − 1.4 ± 3.3 stat ± 1.1 syst % , ACP D 0 → ϕη = − 1.9 ± 4.4 stat ± 0.6 syst % . The results for D 0 → π + π − η are a significant improvement over previous results. The branching fraction and A CP results for D 0 → K + K − η , and the ACP result for D 0 → ϕη , are the first such measurements. No evidence for CP violation is found in any of these decays.

Background: Pembrolizumab (P) is an anti-PD-1 antibody that blocks the interaction between programmed cell death protein 1 (PD-1) on T-cells and PD-L1 and PD-L2 on tumour cells. A phase Ib trial of P plus chemotherapy was undertaken to evaluate the safety and efficacy. Methods: Patients with advanced, metastatic solid tumours were enrolled onto one of six treatment arms. Pembrolizumab was given: with gemcitabine (G), G+docetaxel (D), G+nab-paclitaxel (NP), G+vinorelbine (V) or irinotecan (I) until progression or toxicity, or with liposomal doxorubicin (LD) for up to 15 cycles, progression or toxicity. Safety monitoring and response assessments were conducted. Results: Forty-nine patients were enrolled and treated. The most common adverse events were transaminitis, cytopenias, rash, diarrhoea, fatigue, nausea and vomiting. Arm 2 was closed due to poor accrual. The recommended phase II dose (RP2D) was determined for Arms 1, 3a, 4, 5 and 6. There were eight partial responses across multiple tumour types. Conclusions: Standard dose P can be safely combined with G, G+NP, G+V, I and LD. Efficacy was observed in multiple tumour types and evaluation to determine if response and duration of response are more robust than what would be expected for chemotherapy or immunotherapy alone requires further validation.

A bstract Using a data sample of 980 fb − 1 collected with the Belle detector at the KEKB asymmetric-energy e + e − collider, we study the processes of Ξ c 0 → Λ K ¯ ∗ 0 , Ξ c 0 → Σ 0 K ¯ ∗ 0 , and Ξ c 0 → Σ + K ∗ − for the first time. The relative branching ratios to the normalization mode of Ξ c 0 → Ξ − π + are measured to be B Ξ c 0 → Λ K ¯ ∗ 0 / B Ξ c 0 → Ξ − π + = 0.18 ± 0.02 stat . ± 0.01 syst . , B Ξ c 0 → Σ 0 K ¯ ∗ 0 / B Ξ c 0 → Ξ − π + = 0.69 ± 0.03 stat . ± 0.03 syst . , B Ξ c 0 → Σ + K ∗ − / B Ξ c 0 → Ξ − π + = 0.34 ± 0.06 stat . ± 0.02 syst . , where the uncertainties are statistical and systematic, respectively. We obtain B Ξ c 0 → Λ K ¯ ∗ 0 = 3.3 ± 0.3 stat . ± 0.2 syst . ± 1.0 ref . × 10 − 3 , B Ξ c 0 → Σ 0 K ¯ ∗ 0 = 12.4 ± 0.5 stat . ± 0.5 syst . ± 3.6 ref . × 10 − 3 , B Ξ c 0 → Σ + K ∗ 0 = 6.1 ± 1.0 stat . ± 0.4 syst . ± 1.8 ref . × 10 − 3 , where the uncertainties are statistical, systematic, and from B Ξ c 0 → Ξ − π + , respectively. The asymmetry parameters α Ξ c 0 → Λ K ¯ ∗ 0 and α Ξ c 0 → Σ + K ∗ − are 0 . 15 ± 0 . 22(stat . ) ± 0 . 04(syst . ) and − 0 . 52 ± 0 . 30(stat . ) ± 0 . 02(syst . ), respectively, where the uncertainties are statistical followed by systematic.

We present a search for the lepton-flavor-violating decays$$ {B}_s^0 $$B s 0 → ℓ ∓ τ ± , where ℓ = e, μ , using the full data sample of 121 fb − 1 collected at the Υ(5 S ) resonance with the Belle detector at the KEKB asymmetric-energy e + e − collider. We use$$ {B}_s^0{\\overline{B}}_s^0 $$B s 0 B ¯ s 0 events in which one$$ {B}_s^0 $$B s 0 meson is reconstructed in a semileptonic decay mode and the other in the signal mode. We find no evidence for$$ {B}_s^0 $$B s 0 → ℓ ∓ τ ± decays and set upper limits on their branching fractions at 90% confidence level as$$ \\mathcal{B} $$B ($$ {B}_s^0 $$B s 0 → e ∓ τ ± ) < 14 × 10 − 4 and$$ \\mathcal{B} $$B ($$ {B}_s^0 $$B s 0 → μ ∓ τ ± ) < 7 . 3 × 10 − 4 . Our result represents the first upper limit on the$$ {B}_s^0 $$B s 0 → e ∓ τ ± decay rate.