Blockchain Based Detection of Android Malware using Ranked Permissions

Android mobile devices are a prime target for a huge number of cyber-criminals as they aim to create malware for disrupting and damaging the servers, clients, or networks. Android malware are in the form of malicious apps, that get downloaded on mobile devices via the Play Store or third-party app markets. Such malicious apps pose serious threats like system damage, information leakage, financial loss to user, etc. Thus, predicting which apps contain malicious behavior will help in preventing malware attacks on mobile devices. Identifying Android malware has become a major challenge because of the ever-increasing number of permissions that applications ask for, to enhance the experience of the users. And most of the times, permissions and other features defined in normal and malicious apps are generally the same. In this paper, we aim to detect Android malware using machine learning, deep learning, and natural language processing techniques. To delve into the problem, we use the Android manifest files which provide us with features like permissions which become the basis for detecting Android malware. We have used the concept of information value for ranking permissions. Further, we have proposed a consensus-based blockchain framework for making more concrete predictions as blockchain have high reliability and low cost. The experimental results demonstrate that the proposed model gives the detection accuracy of 95.44% with the Random Forest classifier. This accuracy is achieved with top 45 permissions ranked according to Information Value.

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SFDroid: Android Malware Detection using Ranked Static Features

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.a5804.0510121 ◽

2021 ◽

Vol 10 (1) ◽

pp. 142-152

Author(s):

Gourav Garg ◽

Ashutosh Sharma* ◽

Anshul Arora

Keyword(s):

Malware Detection ◽

Information Leakage ◽

Detection Accuracy ◽

Financial Loss ◽

Android Malware ◽

Android Malware Detection ◽

Proposed Model ◽

Support Threshold ◽

Malicious Apps ◽

Work First

Over the past few years, malware attacks have risen in huge numbers on the Android platform. Significant threats are posed by these attacks which may cause financial loss, information leakage, and damage to the system. Around 25 million smartphones were infected with malware within the first half of 2019 that depicts the seriousness of these attacks. Taking into account the danger posed by the Android malware to the users' community, we aim to develop a static Android malware detector named SFDroid that analyzes manifest file components for malware detection. In this work, first, the proposed model ranks the manifest features according to their frequency in normal and malicious apps. This helps us to identify the significant features present in normal and malware datasets. Additionally, we apply support thresholds to remove the unnecessary and redundant features from the rankings. Further, we propose a novel algorithm that uses the ranked features, and several machine learning classifiers to detect Android malware. The experimental results demonstrate that by using the Random Forest classifier at 10% support threshold, the proposed model gives a detection accuracy of 95.90% with 36 manifest components.

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R-MFDroid: Android Malware Detection using Ranked Manifest File Components

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.g8951.0510721 ◽

2021 ◽

Vol 10 (7) ◽

pp. 55-64

Author(s):

Kartik Khariwal* ◽

Rishabh Gupta ◽

Jatin Singh ◽

Anshul Arora

Keyword(s):

Information Gain ◽

Detection System ◽

Malware Detection ◽

Information Leakage ◽

Svm Classifier ◽

Detection Accuracy ◽

Android Malware ◽

Android Malware Detection ◽

Android Os ◽

Malicious Apps

With the increasing fame of Android OS over the past few years, the quantity of malware assaults on Android has additionally expanded. In the year 2018, around 28 million malicious applications were found on the Android platform and these malicious apps were capable of causing huge financial losses and information leakage. Such threats, caused due to these malicious apps, call for a proper detection system for Android malware. There exist some research works that aim to study static manifest components for malware detection. However, to the best of our knowledge, none of the previous research works have aimed to find the best set amongst different manifest file components for malware detection. In this work, we focus on identifying the best feature set from manifest file components (Permissions, Intents, Hardware Components, Activities, Services, Broadcast Receivers, and Content Providers) that could give better detection accuracy. We apply Information Gain to rank the manifest file components intending to find the best set of components that can better classify between malware applications and benign applications. We put forward a novel algorithm to find the best feature set by using various machine learning classifiers like SVM, XGBoost, and Random Forest along with deep learning techniques like classification using Neural networks. The experimental results highlight that the best set obtained from the proposed algorithm consisted of 25 features, i.e., 5 Permissions, 2 Intents, 9 Activities, 3 Content Providers, 4 Hardware Components, 1 Service, and 1 Broadcast Receiver. The SVM classifier gave the highest classification accuracy of 96.93% and an F1-Score of 0.97 with this best set of 25 features.

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Modelling of bacterial removal in wastewater storage reservoir for irrigation purposes: a case study in Sicily, Italy

Water Science & Technology Water Supply ◽

10.2166/ws.2003.0059 ◽

2003 ◽

Vol 3 (4) ◽

pp. 169-175 ◽

Cited By ~ 2

Author(s):

S. Barbagallo ◽

F. Brissaud ◽

G.L. Cirelli ◽

S. Consoli ◽

P. Xu

Keyword(s):

Municipal Wastewater ◽

High Reliability ◽

Low Cost ◽

Public Awareness ◽

Storage System ◽

Wastewater Reuse ◽

Order Kinetic ◽

Aquifer Recharge ◽

Mediterranean Countries ◽

Storage Reservoirs

In arid and semiarid regions the reclamation and reuse of municipal wastewater can play a strategic role in alleviating water resources shortages. Public awareness is growing about the need to recycle and reuse water for increasing supply availability. Many wastewater reuse projects have been put in operation in European and Mediterranean countries adopting extensive treatment systems such as aquifer recharge, lagooning, constructed wetlands, and storage reservoirs, mainly for landscape and agricultural irrigation. In agricultural reuse systems, there is an increasing interest in extensive technologies because of their high reliability, and easy and low cost operation and maintenance. Wastewater storage reservoirs have become the option selected in many countries because of the advantages they present in comparison with other treatment alternatives, namely the coupling of two purposes, stabilization and seasonal regulation. This paper describes an example of a wastewater storage system, built in Caltagirone (Sicily, Italy). The storage results in a tertiary treatment of a continuous inlet flow of activated sludge effluents. The prediction of the microbiological water quality has been evaluated by means of a non-steady-state first-order kinetic model. Single and multiple regressions were applied to determine the main variables that most significantly affected die-off coefficients. The proposed model has been calibrated using the results of a field monitoring carried out during a period from March to October 2000.

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Multi-Blockchain-Based IoT Data Processing Techniques to Ensure the Integrity of IoT Data in AIoT Edge Computing Environments

Sensors ◽

10.3390/s21103515 ◽

2021 ◽

Vol 21 (10) ◽

pp. 3515

Author(s):

Sung-Ho Sim ◽

Yoon-Su Jeong

Keyword(s):

Low Cost ◽

Third Party ◽

Monitoring And Control ◽

Constant Size ◽

Computing Environments ◽

Iot Devices ◽

Cloud Servers ◽

The Cost ◽

Processing Techniques ◽

And Control

As the development of IoT technologies has progressed rapidly recently, most IoT data are focused on monitoring and control to process IoT data, but the cost of collecting and linking various IoT data increases, requiring the ability to proactively integrate and analyze collected IoT data so that cloud servers (data centers) can process smartly. In this paper, we propose a blockchain-based IoT big data integrity verification technique to ensure the safety of the Third Party Auditor (TPA), which has a role in auditing the integrity of AIoT data. The proposed technique aims to minimize IoT information loss by multiple blockchain groupings of information and signature keys from IoT devices. The proposed technique allows IoT information to be effectively guaranteed the integrity of AIoT data by linking hash values designated as arbitrary, constant-size blocks with previous blocks in hierarchical chains. The proposed technique performs synchronization using location information between the central server and IoT devices to manage the cost of the integrity of IoT information at low cost. In order to easily control a large number of locations of IoT devices, we perform cross-distributed and blockchain linkage processing under constant rules to improve the load and throughput generated by IoT devices.

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Electrostatic bellow muscle actuators and energy harvesters that stack up

Science Robotics ◽

10.1126/scirobotics.aaz5796 ◽

2021 ◽

Vol 6 (51) ◽

pp. eaaz5796

Author(s):

I. D. Sîrbu ◽

G. Moretti ◽

G. Bortolotti ◽

M. Bolignari ◽

S. Diré ◽

...

Keyword(s):

High Performance ◽

High Reliability ◽

Low Cost ◽

Artificial Muscle ◽

Pressure Head ◽

Robotic Systems ◽

Maximum Pressure ◽

Term Operation ◽

Energy Harvesters ◽

Out Of Plane

Future robotic systems will be pervasive technologies operating autonomously in unknown spaces that are shared with humans. Such complex interactions make it compulsory for them to be lightweight, soft, and efficient in a way to guarantee safety, robustness, and long-term operation. Such a set of qualities can be achieved using soft multipurpose systems that combine, integrate, and commute between conventional electromechanical and fluidic drives, as well as harvest energy during inactive actuation phases for increased energy efficiency. Here, we present an electrostatic actuator made of thin films and liquid dielectrics combined with rigid polymeric stiffening elements to form a circular electrostatic bellow muscle (EBM) unit capable of out-of-plane contraction. These units are easy to manufacture and can be arranged in arrays and stacks, which can be used as a contractile artificial muscle, as a pump for fluid-driven soft robots, or as an energy harvester. As an artificial muscle, EBMs of 20 to 40 millimeters in diameter can exert forces of up to 6 newtons, lift loads over a hundred times their own weight, and reach contractions of over 40% with strain rates over 1200% per second, with a bandwidth over 10 hertz. As a pump driver, these EBMs produce flow rates of up to 0.63 liters per minute and maximum pressure head of 6 kilopascals, whereas as generator, they reach a conversion efficiency close to 20%. The compact shape, low cost, simple assembling procedure, high reliability, and large contractions make the EBM a promising technology for high-performance robotic systems.

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Taguchi experiment to design for low cost, high reliability surface mounted components

10.1109/rmcae.1992.245498 ◽

2003 ◽

Author(s):

M.H. Pohlenz

Keyword(s):

High Reliability ◽

Low Cost

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Brief Communication: A low-cost Arduino<sup>®</sup>-based wire extensometer for earth flow monitoring

Natural Hazards and Earth System Science ◽

10.5194/nhess-17-881-2017 ◽

2017 ◽

Vol 17 (6) ◽

pp. 881-885 ◽

Cited By ~ 8

Author(s):

Luigi Guerriero ◽

Giovanni Guerriero ◽

Gerardo Grelle ◽

Francesco M. Guadagno ◽

Paola Revellino

Keyword(s):

High Reliability ◽

Low Cost ◽

Mitigation Measures ◽

Thermal Drift ◽

Data Logger ◽

Test Configuration ◽

Flow Monitoring ◽

Earth Flow ◽

Flow Displacement ◽

Additional Monitoring

Abstract. Continuous monitoring of earth flow displacement is essential for the understanding of the dynamic of the process, its ongoing evolution and designing mitigation measures. Despite its importance, it is not always applied due to its expense and the need for integration with additional sensors to monitor factors controlling movement. To overcome these problems, we developed and tested a low-cost Arduino-based wire-rail extensometer integrating a data logger, a power system and multiple digital and analog inputs. The system is equipped with a high-precision position transducer that in the test configuration offers a measuring range of 1023 mm and an associated accuracy of ±1 mm, and integrates an operating temperature sensor that should allow potential thermal drift that typically affects this kind of systems to be identified and corrected. A field test, conducted at the Pietrafitta earth flow where additional monitoring systems had been installed, indicates a high reliability of the measurement and a high monitoring stability without visible thermal drift.

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