Set Of Permissions Research Articles

Безопасная реализация виртуальной сети на плоскости данных SDN

The paper continues the investigations on the implementation of virtual networks on the SDN data plane which is modeled by a graph of physical connections between network nodes. A virtual network is defined as a set of ordered host pairs (sender, receiver), and it is implemented by a set of host-host paths that uniquely determine the switch settings. The opportunities to transmit a packet are limited by the host weights (priorities): a packet can be only transmitted from a host to a host if the sender has at most the same priority as the recipient, and thus, a set of paths is permissible if its every subset connects permissible host pairs. In the paper, it is proven that differently from the case when every host pair is permissible, in the graph with priorities a permissible path implementation does not exist for every set of permissible hosts. Moreover, it is shown that is some cases when such an implementation exists, the implementation is not possible without paths with cycles where packets can move infinite and without duplicate paths when a host can get the same packet several times. Using the notion of a perfect set of paths a criterion is established when every permissible set of hosts can be safely implemented by a set of paths without cycles but possibly with duplicate paths.

Health risk assessment of levels of some toxic metals and polycyclic aromatic hydrocarbons of some commercial soft drinks commonly consumed among students of Nnamdi Azikiwe university, Awka, Anambra state, Nigeria

Food Safety is of growing concern globally, and more so considering the relatively high proportion of soft drinks in the beverage market worldwide, and its rate of consumption especially amongst the youths. It is essential to evaluate the levels of toxic metals and Polycyclic Aromatic Hydrocarbons (PAHs) residues of which when present in soft drinks above the permissible limits could pose serious public health risk. The aim of this present study was to assess the levels of heavy metals (Pb, Hg, Cd ,As) and Polycyclic aromatic Hydrocarbons(PAHs) in 16 commercial soft drinks marketed in Awka, using Atomic Absorption Spectroscopy(AAS) and Flame Ionization detector (GC-FID), respectively. The carcinogenic and non-carcinogenic risk assessment were performed using the method established by United State Environmental Protection agency(USEPA).The data obtained were compared with the permissible limit set for drinking water by United State Environmental Protection Agency(USEPA),World Health Organization (WHO),Standard Organization of Nigeria(SON) and Federal Environmental Protection Agency(FEPA). The highest mean level of 0.6116 mg/l is related to Pb and the lowest of 0.0028mg/l related to Hg. The mean levels of the metals increased in this order: Hg<As<Cd<Pb. For the PAHs, using Benzo(a)pyrene as the benchmark for other PAHs congeners because of its strong carcinogenic potency, is not detected in this study, and thus pose no health risk. The target hazard quotient (THQ) of Hg in the studied samples is less than 1. Pb levels in75% the studied samples had THQ>1, and thus, pose a health risk. The hazard index, calculated as the combined risk of heavy metal toxicity is greater than unity (HI>1).The incremental lifetime cancer risk (ILCR) values estimated for Pb, As and Cd in the sample were 6.25%,31.25% and 25% ,respectively, representing higher than acceptable risk value of (10-6 -10-4) . The results raise a concern for the public exposed with respect to As and Cd carcinogenic risk values. Â

ADAPTIVE GUARANTEED ESTIMATION OF A CONSTANT SIGNAL UNDER UNCERTAINTY OF MEASUREMENT ERRORS

In the guaranteed estimation problems under uncertainty relative to disturbances and meas-urement errors, the admissible sets of their possible values are determined. The solution is chosen due to conditions of guaranteed bounded estimates optimization corresponding to the worst realiza-tion of disturbances and measurement errors. The result of the guaranteed estimation is an unim-provable bounded estimate (information set), which turns to be overly pessimistic (reinsurance) if a prior admissible set of measurement errors is too large compared to their realized values. The admis-sible sets of disturbances and measurement errors can turn to be only rough upper estimates on a short observation interval. The goal of research is the accuracy enhancement problem of guaran-teed estimation when measurement errors are not realized in the worst way, i.e. the environment in which the object operates does not behave as aggressively as it is built in a priori data on the permis-sible set of error values. Research design. The problem of adaptive guaranteed estimation of a con-stant signal from noisy measurements is considered. The adaptive filtering problem is, according to the results of measurement processing, from the whole set of possible realizations of errors, to choose the one that would generate the measurement sequence. Results. An adaptive guaranteed estimation algorithm is presented. The adaptive algorithm construction is based on a multi-alternative method based on the Kalman filter bank. The method uses a set of filters, each of which is tuned to a specific hypoth-esis about the measurement error model. Filter residuals are used to compute estimates of realized measurement errors. The choice of the realization of possible errors is performed using a function that has the meaning of the residual variance over a short time interval. Conclusion. The computa-tional scheme of the adaptive algorithm, the numerical example, and comparative analysis of ob-tained estimates are presented.

SemiDroid: a behavioral malware detector based on unsupervised machine learning techniques using feature selection approaches

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.

SOMDROID: android malware detection by artificial neural network trained using unsupervised learning

Android has gained its popularity due to its open-source and number of freely available apps in its official play store. Appropriate functioning of Android apps depends upon the permission or set of permissions which an app demands at the time of installation and run-time. By taking the advantage of these permissions or set of permissions, cybercriminals are developing malware-infected apps daily. In this study, we proposed a framework named as “SOMDROID”, that work on the principle of unsupervised machine learning algorithm. To develop an effective and efficient Android malware detection model, we collect 5,00,000 distinct Android apps from promised repositories and extract 1844 unique features. Further, to select significant features or feature sets, we applied six different feature ranking approaches in this study. With the selected feature or feature sets, we implement the Self-Organizing Map (SOM) algorithm of Kohonen and measure four distinct performance parameters, i.e., Intra-cluster distance, Inter-cluster distance, Accuracy and F-measure. Empirical result reveals that our proposed framework is able to detect 98.7% malware that belongs to unknown families and in addition to that the detection rate is higher by 2% when compared to commercial anti-virus scanners and frameworks proposed in the literature.

An implementation model for managing cloud-based longitudinal care plans for children with medical complexity.

We aimed to iteratively refine an implementation model for managing cloud-based longitudinal care plans (LCPs) for children with medical complexity (CMC). We conducted iterative 1-on-1 design sessions with CMC caregivers (ie, parents/legal guardians) and providers between August 2017 and March 2019. During audio-recorded sessions, we asked participants to walk through role-specific scenarios of how they would create, review, and edit an LCP using a cloud-based prototype, which we concurrently developed. Between sessions, we reviewed audio recordings to identify strategies that would mitigate barriers that participants reported relating to 4 processes for managing LCPs: (1) taking ownership, (2) sharing, (3) reviewing, and (4) editing. Analysis informed iterative implementation model revisions. We conducted 30 design sessions, with 10 caregivers and 20 providers. Participants emphasized that cloud-based LCPs required a team of owners: the caregiver(s), a caregiver-designated clinician, and a care coordinator. Permission settings would need to include universal accessibility for emergency providers, team-level permission options, and some editing restrictions for caregivers. Notifications to review and edit the LCP should be sent to team members before and after clinic visits and after hospital encounters. Mitigating double documentation barriers would require alignment of data fields between the LCP and electronic health record to maximize interoperability. These findings provide a model for how we may leverage emerging Health Insurance Portability and Accountability Act-compliant cloud computing technologies to support families and providers in comanaging health information for CMC. Utilizing these management strategies when implementing cloud-based LCPs has the potential to improve team-based care across settings.

PRIPRO—Privacy Profiles: User Profiling Management for Smart Environments

Smart environments are pervasive computing systems that provide higher comfort levels on daily routines throughout interactions among smart sensors and embedded computers. The lack of privacy within these interactions can lead to the exposure of sensitive data. We present PRIPRO (PRIvacy PROfiles), a management tool that includes an Android application that acts on the user’s smartphone by allowing or blocking resources according to the context, in order to address this issue. Back-end web server processes and imposes a protocol according to the conditions that the user selected beforehand. The experimental results show that the proposed solution successfully communicates with the Android Device Administration framework, and the device appropriately reacts to the expected set of permissions imposed according to the user’s profile with low response time and resource usage.

The concentration and health risk assessment of trace elements in commercial soft drinks from Iran marketed

ABSTRACT The aim of the present study was to assess the concentration of the trace elements Al, As, Cd, Ba, Cr, Cu, Hg, Ni, Pb, Mn, Fe and Zn in 150 commercial soft drinks marketed of Iran, using inductive couple plasma–optical emission spectroscopy (ICP-OES). The data obtained were compared with the permissible limit set for drinking water by the Codex Alimantarius (CA), United States Environmental Protection Agency (USEPA), Food and Agriculture Organization/World Health Organization (FAO/WHO) and National Standard of Iran (NSI). The highest mean level was 84.11 ± 27.06 μg/L is related to Fe and the lowest was <detection limit to As and Hg. The concentrations of Pb, Ni, Cr, Cu, Ba, Hg, Cd and As in samples were lower than the limit set by USEPA, FAO/WHO, CA and NSI. In contrast, the concentrations of Fe, Al, Mn and Zn soft drink samples were higher than the limit set by USEPA, FAO/WHO, CA and NSI. Monte Carlo simulation results indicated that the trend of potential non-carcinogenic risks in all samples was Cd > Cr > Pb > Ni > Al > Ba. Target hazard quotient index of toxic elements, for adults and children, was considerably lower than the safe risk limits THQ > 1.

Feature selection for malicious android applications using Symmetrical Uncert Attribute Eval method

The fast growth of tablets, smartphones has led to increase the usage of mobile applications. The Android apps have more popularity, however, the applications downloaded from third-party markets could be malware that may threaten the users’ privacy. Several works used techniques to detect normal apps from malicious apps based on mining requested permissions. However, there are some set of permissions that can occur in benign and malignant applications. Redundant features could reduce the detection rate and increase the false positive rate. In this paper, we have proposed feature selection methods to identify clean and malicious applications based on selecting a set combination of permission patterns using different classification algorithms such as sequential minimal optimization (SMO), decision Tree (J48) and Naive Bayes. The experimental results show that sequential minimal optimization (SMO) combining with SymmetricalUncertAttributeEval method achieved the highest accuracy rate of 0.88, with lowest false positive rate of 0.085 and highest precision of 0.910. And the findings prove that feature selection methods enhanced the result of classification.

Two-phase guidance law for impact time control under physical constraints

An impact-time-control guidance law is required for the simultaneous attack of suicide attack unmanned aerial vehicles. Based on the nonlinear model, a two-phase guidance strategy is proposed. The impact time is derived in a simple analytical form of initial states and switching states, and it can be controlled by switching at an appropriate range. Firstly, a two-phase guidance law is designed to make the magnitude of the heading error decrease monotonically from its initial value to zero. And then, the feasible interval of the switching ranges and of the impact times under the acceleration constraint are given analytically in sequence. Furthermore, a general form of two-phase guidance law is proposed, which allows the magnitude of the heading error to increase in the first phase, to improve the applicability of the methodology. Having the same structure as proportional navigation guidance with a time-varying gain, the proposed algorithms are simple and easy to implement. The corresponding feedback form is presented for realistic implementation. When a predefined impact time is taken within its permissible set, the constraints on the acceleration and field-of-view will not be violated during the interception. Finally, simulations validate the effectiveness of the methodology in impact time control and salvo attack.

Cadmium Uptake and Relationship to Feeding Habits of Freshwater Fish from the Ayeyarwady River, Mandalay, Myanmar.

Background.Pollution of the aquatic ecosystem by heavy metals is increasing due to anthropogenic activities. Cadmium (Cd) can accumulate in soil, be taken up by plants, and passed on in the food chain to animals and humans.Objectives.The present study was conducted to analyze the uptake of Cd in muscles of sampled fish with different feeding habits and to compare levels of Cd in fish from the Ayeyarwady River, Myanmar with international standards.Methods.The acid digestion procedure was used for sample preparation. Cadmium concentrations in fish samples were determined by flame atomic absorption spectrophotometry (Perkin Elmer AAanalyst 800 and Winlab-32 software).Results.In herbivorous fish species, Cd content ranged from 0.07 (Catla catla) to 0.086 mg/kg (Osteobrama belangeri). In carnivorous fish species, Cd ranged from 0.060 (Mystus leucophasis) to 0.083 mg/kg (Wallago attu). In omnivorous fish species, Cd ranged from 0.07 (Botia histrionica) to 0.084 mg/kg (Gudusia variegata). Cadmium content did not differ significantly across the three types of feeding habits (p>0.05).Discussion.The accumulation of Cd in the muscle of studied fish was lower than the permissible limit set down by the European Union in 2001 (0.1 ppm), but above the limits set down by the Food and Agriculture Organization/World Health Organization, European Commission (0.05 ppm) and within the limit of United States Food and Drug Administration (0.01–0.21 ppm). The data obtained in the present study indicate that levels of Cd were not significantly different across fish species with different feeding habits.Conclusions.The examined fish samples were not fully safe for human consumption due to high levels of Cd.Competing Interests.The authors declare no competing financial interests.

The permissible and the forbidden

In many economic situations, neither prices nor authority rules. Rather, individual behavior is governed by social norms that specify what is allowed (socially acceptable) and what is forbidden (socially unacceptable). These norms can emerge in a decentralized way and can serve as a method to bring order to economic situations. The key component of our solution concept is a uniform permissible set which plays a role parallel to that of a price system in competitive equilibrium. The concept is analyzed and applied to a variety of economic and social settings.

An Android application risk evaluation framework based on minimum permission set identification

Android utilizes a security mechanism that requires apps to request permission for accessing sensitive user data, e.g., contacts and SMSs, or certain system features, e.g., camera and Internet access. However, Android apps tend to be overprivileged, i.e., they often request more permissions than necessary. This raises the security problem of overprivilege. To alleviate the overprivilege problem, this paper proposes MPDroid, an approach that combines static analysis and collaborative filtering to identify the minimum permissions for an Android app based on its app description and API usage. Given an app, MPDroid first employs collaborative filtering to identify the initial minimum permissions for the app. Then, through static analysis, the final minimum permissions that an app really needs are identified. Finally, it evaluates the overprivilege risk by inspecting the app’s extra privileges, i.e., the unnecessary permissions requested by the app. Experiments are conducted on 16,343 popular apps collected from Google Play. The results show that MPDroid outperforms the state-of-the-art approach significantly.

AppAware: a policy visualization model for mobile applications

Purpose Privacy policies emerge as the main mechanism to inform users on the way their information is managed by online service providers, and still remain the dominant approach for this purpose. The literature notes that users find difficulties in understanding privacy policies because they are usually written in technical or legal language even, although most users are unfamiliar with them. These difficulties have led most users to skip reading privacy policies and blindly accept them. This study aims to address this challenge this paper presents AppAware, a multiplatform tool that intends to improve the visualization of privacy policies for mobile applications. Design/methodology/approach AppAware formulates a visualized report with the permission set of an application, which is easily understandable by a common user. AppAware aims to bridge the difficulty to read privacy policies and android’s obscure permission set with a new privacy policy visualization model. Thus, we propose AppAware parser, a mobile add-on that acts complementary with AppAware and helps mobile device users to monitor the applications they installed to their smart device. Findings To validate AppAware, the authors conducted a survey through questionnaire aiming to evaluate AppAware in terms of installability, usability and viability-purpose. The results demonstrate that AppAware is assessed above average by the users in all categories. Originality/value In the best of the authors’ knowledge, there is no such approach as AppAware as an application nor AppAware parser as add-on.

Analysis of methods for organizing user rights in GNU\Linux systems

This article is about user’s rights to files or directories. The GNU\Linux systems have many great security features, but one of the most important is the file permissions system. Methods for organizing user rights in GNU\Linux systems, such as granting rights to each user individually and grouping users with the same or similar rights into groups, are discussed. The concept of operating system user and superuser is formed. The benefits of grouping users by access level have been identified. The criteria for dividing users into groups are analyzed, depending on the format of computer use, the structure of the organization that owns a particular computer and the number and positions of people who will work with it. Also, the benefit of the mechanism of organizing users into groups to manage service users of services running in the system is considered. It is said that any file and directory in Linux has an owner user and an owner group. That is, any file and directory belongs to some system user and some group. In addition, any file and directory has three access rights groups: one for the owner user, one for members of the owner group, and one for all other users on the system. Each group consists of the rights to read, write and execute the file for execution. The format of saving rights to a file or directory and the mechanism of checking access to a file or directory are considered. An explanation is given of what the right to read, the right to modify and the right to execute means for the file and for the directory. It is with the help of these sets of permissions that the permissions of files in linux are established. Each user can only have full access to files that he owns or those that he is allowed to access. Only the Root user can work with all files, regardless of their set of permissions.

Incremental Research on Cyber Security metrics in Android applications by implementing the ML algorithms in Malware Classification and Detection

Cyber attacks are prevailing to be a great headache for the technical advancements especially when dealt with mobile usage in an android application environment. For a new user, it is difficult to identify the set of harmful permissions. This could be an advantage for malware intruders to access the data or infect the mobile device by introducing malware applications. Thus the face of Cybersecurity has changed in recent times with the advent of new technologies such as the Cloud, the internet of things, mobile/wireless, and wearable technology. The technological advances in data science which help develop contemporary cybersecurity solutions are storage, computing, and behavior. In this paper, the possible investigations are done on the cyber attacks in android by adopting the various malware classification and detection techniques. Various Classifications and Detections are done on various malware prevailing in the android applications.

Demand management by active consumers in intelligent electric power systems

The approaches to demand management by active consumers through forming a given schedule of power consumption in the required period of time based on the solution of the optimization problem in the form of maximizing the power of connected controlled electric receivers of various types are presented. The model of demand management by active consumers is justified, taking into account the following factors: load sensitivity for connecting a transformer substation to a change in consumer load; load priority; consistent load reduction levels with flexible performance and power control; permissible set of electric receivers in accordance with the technological process, network schedule, other logical conditions corresponding to adjacency lists. An algorithm has been developed for limiting power on the part of active consumers based on the widespread use of digital data processing technologies, modern technical means of measurement, control and switching of end consumers in real time. The presented research results indicate the validity of the demand management method by active consumers in the normal mode of intelligent electric power systems and the possibility of its practical implementation in an industrial enterprise with reference to the technological process.

The Best of Both Worlds: Mitigating Trade-offs Between Accuracy and User Burden in Capturing Mobile App Privacy Preferences

Abstract In today’s data-centric economy, data flows are increasingly diverse and complex. This is best exemplified by mobile apps, which are given access to an increasing number of sensitive APIs. Mobile operating systems have attempted to balance the introduction of sensitive APIs with a growing collection of permission settings, which users can grant or deny. The challenge is that the number of settings has become unmanageable. Yet research also shows that existing settings continue to fall short when it comes to accurately capturing people’s privacy preferences. An example is the inability to control mobile app permissions based on the purpose for which an app is requesting access to sensitive data. In short, while users are already overwhelmed, accurately capturing their privacy preferences would require the introduction of an even greater number of settings. A promising approach to mitigating this trade-off lies in using machine learning to generate setting recommendations or bundle some settings. This article is the first of its kind to offer a quantitative assessment of how machine learning can help mitigate this trade-off, focusing on mobile app permissions. Results suggest that it is indeed possible to more accurately capture people’s privacy preferences while also reducing user burden.

Demand management by active consumers in intelligent electric power systems

The approaches to demand management by active consumers through forming a given schedule of power consumption in the required period of time based on the solution of the optimization problem in the form of maximizing the power of connected controlled electric receivers of various types are presented. The model of demand management by active consumers is justified, taking into account the following factors: load sensitivity for connecting a transformer substation to a change in consumer load; load priority; consistent load reduction levels with flexible performance and power control; permissible set of electric receivers in accordance with the technological process, network schedule, other logical conditions corresponding to adjacency lists. An algorithm has been developed for limiting power on the part of active consumers based on the widespread use of digital data processing technologies, modern technical means of measurement, control and switching of end consumers in real time. The presented research results indicate the validity of the demand management method by active consumers in the normal mode of intelligent electric power systems and the possibility of its practical implementation in an industrial enterprise with reference to the technological process.

КОМПАКТНЫЕ ЛЮМИНЕСЦЕНТНЫЕ ЛАМПЫ КАК ИСТОЧНИКИ ЭЛЕКТРОМАГНИТНЫХ ПОЛЕЙ РАДИОЧАСТОТНОГО ДИАПАЗОНА (ЭКСПЕРИМЕНТАЛЬНОЕ ИССЛЕДОВАНИЕ)

The objects of this study are compact fluorescent lamps with an electronic ballast and the hygienic assessment of electromagnetic fields created by compact fluorescent lamps. As part of the work, the selection of samples of compact fluorescent lamps for the experimental study of electromagnetic fields was carried out. The background levels of electric and magnetic fields with a frequency of 50 Hz and high-frequency range were measured. Experimental studies of the spectrum and intensity of electromagnetic fields created by compact fluorescent lamps were carried out with different variants of lamp placement (in a cartridge without a light armature, in a local lighting fixture, in general lighting fixtures – chandeliers). The study showed that compact fluorescent lamps equipped with electronic ballast create electromagnetic fields of radio frequency range in the surrounding space. The main frequency of radiation in the examined lamp samples ranged from 32 to 58 kHz. The intensity of electric fields created by compact fluorescent lamps may exceed the permissible level set for consumer goods.