A Hybrid Approach to Detect the Malicious Applications in Android-Based Smartphones Using Deep Learning

2020 ◽  
pp. 176-194
Author(s):  
Manokaran Newlin Rajkumar ◽  
Varadhan Venkatesa Kumar ◽  
Ramachandhiran Vijayabhasker
Keyword(s):  
Deep Learning ◽  
Mobile Application ◽  
Hybrid Approach ◽  
Malware Detection ◽  
Control Flow ◽  
Smart Phones ◽  
The People ◽  
Flow Information ◽  
Android Applications ◽  
Modern Era

This modern era of technological advancements facilitates the people to possess high-end smart phones with incredible features. With the increase in the number of mobile applications, we are witnessing the humongous increase in the malicious applications. Since most of the Android applications are available open source and used frequently in the smart phones, they are more vulnerable. Statistical and dynamical-based malware detection approaches are available to verify whether the mobile application is a genuine one, but only to a certain extent, as the level of mobile application scanning done by the said approaches are in general routine or a common, pre-specified pattern using the structure of control flow, information flow, API call, etc. A hybrid method based on deep learning methodology is proposed to identify the malicious applications in Android-based smart phones in this chapter, which embeds the possible merits of both the statistical-based malware detection approaches and dynamical-based malware detection approaches and minimizes the demerits of them.

A Hybrid Approach to Detect the Malicious Applications in Android-Based Smartphones Using Deep Learning

2021 ◽  
pp. 626-644
Author(s):  
Manokaran Newlin Rajkumar ◽  
Varadhan Venkatesa Kumar ◽  
Ramachandhiran Vijayabhasker
Keyword(s):  
Deep Learning ◽  
Mobile Application ◽  
Hybrid Approach ◽  
Malware Detection ◽  
Control Flow ◽  
Smart Phones ◽  
The People ◽  
Flow Information ◽  
Android Applications ◽  
Modern Era

This modern era of technological advancements facilitates the people to possess high-end smart phones with incredible features. With the increase in the number of mobile applications, we are witnessing the humongous increase in the malicious applications. Since most of the Android applications are available open source and used frequently in the smart phones, they are more vulnerable. Statistical and dynamical-based malware detection approaches are available to verify whether the mobile application is a genuine one, but only to a certain extent, as the level of mobile application scanning done by the said approaches are in general routine or a common, pre-specified pattern using the structure of control flow, information flow, API call, etc. A hybrid method based on deep learning methodology is proposed to identify the malicious applications in Android-based smart phones in this chapter, which embeds the possible merits of both the statistical-based malware detection approaches and dynamical-based malware detection approaches and minimizes the demerits of them.

scholarly journals DEEP LEARNING BASED MASK DETECTION IN SMART HOME ENTRIES DURING THE EPIDEMIC PROCESS

2020 ◽  
Vol XLIV-4/W3-2020 ◽  
pp. 159-163
Author(s):  
B. Cerit ◽  
R. Bayir
Keyword(s):  
Deep Learning ◽  
Mobile Application ◽  
Smart Home ◽  
Smart Homes ◽  
Illumination Level ◽  
Learning Method ◽  
Level Control ◽  
Control Card ◽  
The People ◽  
The World

Abstract. In this study, "smart home" systems were designed against Covid-19 virus, which negatively affects life all over the world, and viruses that may become epidemics later. Our homes need to be more hygienic and safe than yesterday. One of these hygiene rules is the masks that cover our nose and mouth. It is very important to use a mask to prevent further spread of the virus. Whether or not the people in smart homes are wearing masks at home will be diagnosed with the deep learning method. Hosts will be warned if they do not have masks. Brightness level control card and illuminator have been added to smart home entrances to better identify people's faces. With PID, the illumination level is fixed at the desired value, and with IOT technology, people can follow the illumination level at the smart home entrance from the mobile application.

Mobile Application for Displaying Voice Message on LCD Screen

2018 ◽  
Vol 6 (9) ◽  
pp. 33
Author(s):  
Rohit Kuthe ◽  
Nilesh Sonkusare ◽  
L. H. Patil
Keyword(s):  
Mobile Application ◽  
Authorized User ◽  
The People ◽  
Important Message ◽  
Electronic Display ◽  
Paper Work ◽  
Short Span ◽  
Voice Message

This paper talks about an innovative and rather an interesting manner of intimating the message to the people using the wireless electronic display on the screen. This will help us in passing any message almost immediately without any delay just by sending an SMS which is better and more reliable than the old traditional way of passing the message on a screen. Our aim is to reduce the amount of paper work and make use the possible technological resources.  In this paper, we are trying to implement our system in such a way that it can display message send from the authorized user to the various receiving ends. So spreading of important message or screen will take place within the very short span of time to respective mobile application. Means user or registered person will be able to send the message from anywhere and this message will be displayed on a screen at the respective place.

Identifikasi Perubahan Fungsi Ruang pada Rumah Tinggal Joglo Studi kasus : Rumah Joglo di Desa Keji,Kecamatan Muntilan, Kabupaten Magelang, Jawa Tengah

PRAXIS ◽  
2019 ◽  
Vol 2 (1) ◽  
pp. 49
Author(s):  
Maria Damiana Nestri Kiswari
Keyword(s):  
Traditional Architecture ◽  
Central Java ◽  
The People ◽  
Modern Era

Abstract A house is a building that has function to live in a certain period. The house has some spaces and rooms that accomodate all inhabitans’activities. In Javanese culture, philosophy of house is more than a place where all the people stay and live, communicate each other. The spaces and rooms of the house have specific meanings. Joglo is a name of Javanese traditional house partiularly in Central Java. As a traditional Javanese houses in the modern era, the existence of Joglo houses is interesting to be studied. The study is to identify the room arrangement and the change in function of spaces and rooms in Joglo house. It was conducted on one house in Keji Village, Muntilan District, Magelang Regency. The house is a residence of the former headman of Keji village. It has been choosen because it has Joglo tipical roof and its appeareance is still traditional house. This study uses a descriptive quality method which is by observing and defining the spaces and the rooms in the Joglo house along with their functions and activities inside. By studying this Joglo house, an overview and understanding of the changes in the spaces and room in the traditional architecture of Central Java in the present time will be obtained. Keywords: Joglo house, space and room, change in function Abstrak Rumah merupakan bangunan yang memiliki fungsi untuk bertempat tinggal dalam jangka waktu tertentu. Sehingga sebagai tempat tinggal rumah memiliki ruang-ruang untuk menampung aktivitas penghuninya. Dalam budaya Jawa, fisosofi tentang rumah merupakan tempat yang memiliki makna lebih dari sekedar tempat bernaung dan berkumpul keluarga. Joglo merupakan bentuk arsitektur dari rumah tinggal tradisional di Jawa khususnya Jawa Tengah. Sebagai rumah tradisional Jawa, keberadaan rumah Joglo yang masih ada di jaman sekarang ini, menjadi menarik untuk dipelajari tatanan ruang-ruangnya dan perubahan dari fungsi ruang-ruang tersebut. Untuk mempelajari dan memahami aristektur Joglo dan perubahan fungsi ruang yang ada di dalamnya, dilakukan penelitian terhadap salah satu rumah tinggal di Desa Keji, Kecamatan Muntilan, Kabupaten Magelang. Penelitian ini dengan menggunakan metoda deskriptif kualitati yaitu dengan mengamati dan mengidentifikasi ruang-ruang yang ada di rumah Joglo beserta fungsi dan aktivitasnya. Dengan meneliti rumah Joglo ini akan didapatkan gambaran dan pemahaman terhadap perubahan fungsi ruang-ruang yang ada dalam arsitektur tradisional khususnya Jawa Tengah. Kata kunci : rumah joglo, fungsi ruang, perubahan fungsi

Android Malware Detection Techniques: A Literature Review

2020 ◽  
Vol 14 ◽  
Author(s):  
Meghna Dhalaria ◽  
Ekta Gandotra
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Malware Detection ◽  
Future Research ◽  
Android Malware ◽  
Detection Techniques ◽  
Android Malware Detection ◽  
Future Research Directions ◽  
To Come ◽  
Tools And Techniques

Purpose: This paper provides the basics of Android malware, its evolution and tools and techniques for malware analysis. Its main aim is to present a review of the literature on Android malware detection using machine learning and deep learning and identify the research gaps. It provides the insights obtained through literature and future research directions which could help researchers to come up with robust and accurate techniques for classification of Android malware. Design/Methodology/Approach: This paper provides a review of the basics of Android malware, its evolution timeline and detection techniques. It includes the tools and techniques for analyzing the Android malware statically and dynamically for extracting features and finally classifying these using machine learning and deep learning algorithms. Findings: The number of Android users is expanding very fast due to the popularity of Android devices. As a result, there are more risks to Android users due to the exponential growth of Android malware. On-going research aims to overcome the constraints of earlier approaches for malware detection. As the evolving malware are complex and sophisticated, earlier approaches like signature based and machine learning based are not able to identify these timely and accurately. The findings from the review shows various limitations of earlier techniques i.e. requires more detection time, high false positive and false negative rate, low accuracy in detecting sophisticated malware and less flexible. Originality/value: This paper provides a systematic and comprehensive review on the tools and techniques being employed for analysis, classification and identification of Android malicious applications. It includes the timeline of Android malware evolution, tools and techniques for analyzing these statically and dynamically for the purpose of extracting features and finally using these features for their detection and classification using machine learning and deep learning algorithms. On the basis of the detailed literature review, various research gaps are listed. The paper also provides future research directions and insights which could help researchers to come up with innovative and robust techniques for detecting and classifying the Android malware.

Emerging Challenges and Opportunities of Mobile Commerce in India: A Study on Societal Perspective

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Utkarsh Kumar ◽  
Anil Kumar Gope ◽  
Shweta Singh
Keyword(s):  
Growth Rate ◽  
Societal Perspective ◽  
Mobile Applications ◽  
Mobile Application ◽  
Mobile Commerce ◽  
Mobile Banking ◽  
Black And White ◽  
The People ◽  
Challenges And Opportunities

In India, the position of mobile banking was in saga and this time, it is in pic position. The speedof reaching the people is going high and high. This is time of wireless world and sense of prestige; no doubt the mobile commerce is contributing to enhance the beauty of life and playing the role of metaphor and has become the part and parcel of our life. This growth has changed people to do business in mobile commerce (М- Commerce). Peoples are transferring to M-Commerce to attain good and fast transaction into market and saving their precious time. M-Commerce has become distinguished in Indian people, quickly during last few years. Due to large number of mobile application, growth rate in mobile penetration in India is increasing with the rapid speed. The mobile users has shifted to use the android phone from simple and black and white phone and taking the service of internet, the role of telecom companies is also important in the being popular of mobile commerce. Although many people have started E-Commerce but still a separate part of the society feel uncomfortable and hesitate to use M-Commerce because of security problems, payment issues and complexity of mobile applications. This paper identifies facts about the feasibility of MCommercein India today its growth and the Strength and opportunity, weakness and threats lying ahead.

Are e-learning Webinars the future of medical education? An exploratory study of a disruptive innovation in the COVID-19 era

2020 ◽  
pp. 1-10
Author(s):  
Colin J. McMahon ◽  
Justin T. Tretter ◽  
Theresa Faulkner ◽  
R. Krishna Kumar ◽  
Andrew N. Redington ◽  
...  
Keyword(s):  
Deep Learning ◽  
Carbon Footprint ◽  
Quantitative Research ◽  
Survey Design ◽  
Hybrid Approach ◽  
Disruptive Innovation ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
E Learning ◽  
The Impact

Abstract Objective: This study investigated the impact of the Webinar on deep human learning of CHD. Materials and methods: This cross-sectional survey design study used an open and closed-ended questionnaire to assess the impact of the Webinar on deep learning of topical areas within the management of the post-operative tetralogy of Fallot patients. This was a quantitative research methodology using descriptive statistical analyses with a sequential explanatory design. Results: One thousand-three-hundred and seventy-four participants from 100 countries on 6 continents joined the Webinar, 557 (40%) of whom completed the questionnaire. Over 70% of participants reported that they “agreed” or “strongly agreed” that the Webinar format promoted deep learning for each of the topics compared to other standard learning methods (textbook and journal learning). Two-thirds expressed a preference for attending a Webinar rather than an international conference. Over 80% of participants highlighted significant barriers to attending conferences including cost (79%), distance to travel (49%), time commitment (51%), and family commitments (35%). Strengths of the Webinar included expertise, concise high-quality presentations often discussing contentious issues, and the platform quality. The main weakness was a limited time for questions. Just over 53% expressed a concern for the carbon footprint involved in attending conferences and preferred to attend a Webinar. Conclusion: E-learning Webinars represent a disruptive innovation, which promotes deep learning, greater multidisciplinary participation, and greater attendee satisfaction with fewer barriers to participation. Although Webinars will never fully replace conferences, a hybrid approach may reduce the need for conferencing, reduce carbon footprint. and promote a “sustainable academia”.

scholarly journals Deep Learning-Based Approaches to Optimize the Electricity Contract Capacity Problem for Commercial Customers

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2181
Author(s):  
Rafik Nafkha ◽  
Tomasz Ząbkowski ◽  
Krzysztof Gajowniczek
Keyword(s):  
Deep Learning ◽  
Power Grid ◽  
Hybrid Approach ◽  
Electricity Consumption ◽  
Maximum Power ◽  
Learning Approaches ◽  
Two Stage ◽  
Load Optimization ◽  
Optimal Capacity ◽  
Maximal Load

The electricity tariffs available to customers in Poland depend on the connection voltage level and contracted capacity, which reflect the customer demand profile. Therefore, before connecting to the power grid, each consumer declares the demand for maximum power. This amount, referred to as the contracted capacity, is used by the electricity provider to assign the proper connection type to the power grid, including the size of the security breaker. Maximum power is also the basis for calculating fixed charges for electricity consumption, which is controlled and metered through peak meters. If the peak demand exceeds the contracted capacity, a penalty charge is applied to the exceeded amount, which is up to ten times the basic rate. In this article, we present several solutions for entrepreneurs based on the implementation of two-stage and deep learning approaches to predict maximal load values and the moments of exceeding the contracted capacity in the short term, i.e., up to one month ahead. The forecast is further used to optimize the capacity volume to be contracted in the following month to minimize network charge for exceeding the contracted level. As confirmed experimentally with two datasets, the application of a multiple output forecast artificial neural network model and a genetic algorithm (two-stage approach) for load optimization delivers significant benefits to customers. As an alternative, the same benefit is delivered with a deep learning architecture (hybrid approach) to predict the maximal capacity demands and, simultaneously, to determine the optimal capacity contract.

scholarly journals An Efficient DenseNet-Based Deep Learning Model for Malware Detection

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 344
Author(s):  
Jeyaprakash Hemalatha ◽  
S. Abijah Roseline ◽  
Subbiah Geetha ◽  
Seifedine Kadry ◽  
Robertas Damaševičius
Keyword(s):  
Deep Learning ◽  
Detection System ◽  
Malware Detection ◽  
Class Imbalance ◽  
Learning Model ◽  
Computational Time ◽  
Learning Approaches ◽  
Security Threat ◽  
Static And Dynamic Analysis ◽  
Deep Learning Model

Recently, there has been a huge rise in malware growth, which creates a significant security threat to organizations and individuals. Despite the incessant efforts of cybersecurity research to defend against malware threats, malware developers discover new ways to evade these defense techniques. Traditional static and dynamic analysis methods are ineffective in identifying new malware and pose high overhead in terms of memory and time. Typical machine learning approaches that train a classifier based on handcrafted features are also not sufficiently potent against these evasive techniques and require more efforts due to feature-engineering. Recent malware detectors indicate performance degradation due to class imbalance in malware datasets. To resolve these challenges, this work adopts a visualization-based method, where malware binaries are depicted as two-dimensional images and classified by a deep learning model. We propose an efficient malware detection system based on deep learning. The system uses a reweighted class-balanced loss function in the final classification layer of the DenseNet model to achieve significant performance improvements in classifying malware by handling imbalanced data issues. Comprehensive experiments performed on four benchmark malware datasets show that the proposed approach can detect new malware samples with higher accuracy (98.23% for the Malimg dataset, 98.46% for the BIG 2015 dataset, 98.21% for the MaleVis dataset, and 89.48% for the unseen Malicia dataset) and reduced false-positive rates when compared with conventional malware mitigation techniques while maintaining low computational time. The proposed malware detection solution is also reliable and effective against obfuscation attacks.

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