scholarly journals Analysis of WhatsApp Mod User Awareness Information Security with Static Analysis Methods and Quantitative Methods

2021 ◽  
Vol 3 ◽  
pp. 213-222
Author(s):  
Banu Santoso ◽  
Muhammad Abdul Ghofur ◽  
Jeki Kuswanto
Keyword(s):  
Information Security ◽  
Static Analysis ◽  
Quantitative Methods ◽  
Analysis Methods ◽  
Awareness Information ◽  
User Awareness

Perkembangan teknologi yang semakin meningkat membuat penggunaan teknologi didunia juga semakin meningkat dan memberi dampak yang baik maupun buruk terhadap keamanan informasi yang ada pada dunia maya, keamanan informasi yang ada ini dapat tersebar dan diakses oleh orang yang tidak bertanggung jawab dengan memanfaatkan celah keamanan dari setiap media informasi yang ada pada dunia maya, Salah satu celah yang memungkinkan adanya tindak kejahatan dengan memanfaatkan aplikasi yang tidak resmi dimana pada aplikasi tersebut menawarkan fiturfitur yang lebih menarik sehingga para pengguna mau untuk menggunakan aplikasi tersebut. Aplikasi yang marak digunakan adalah diantaranya jenis jejaring sosial chatting, dimana pada kasus ini diangkat tema tentang pengguna whatsapp mod, dimana pada whatsapp mod tersebut ditawarkan beberapa fitur yang tidak ada pada aplikasi whatsapp yang resmi ada pada platform android. Hal ini dapat menjadi salah satu celah yang mana pengembangan aplikasi tidak dilakukan secara resmi, dimana data dan informasi yang disebarkan melalui media komunikasi whatsapp mod tersebut tidak dapat dijamin keamanannya. Maka dari itu dengan adanya penelitian ini diharapkan dapat memberikan nilai persentase terkait tingkat kewaspadaan pengguna whatsapp yang dapat dijadikan pembelajaran terkait keamanan informasi yang ada dengan memperhatikan hasil analisis statis terkait celah keamanan yang ada pada aplikasi whatsapp mod.

Network-Driven Analysis Methods and their Application to Drug Discovery

2011 ◽  
pp. 294-315
Author(s):  
Daniel Ziemek ◽  
Christoph Brockel
Keyword(s):  
Drug Discovery ◽  
Large Scale ◽  
Quantitative Methods ◽  
Primary Data ◽  
Success Rates ◽  
Parallel Methods ◽  
Development Face ◽  
Large Scale Data ◽  
Analysis Methods ◽  
Genome Scale

Drug discovery and development face tremendous challenges to find promising intervention points for important diseases. Any therapeutic agent targeting such an intervention point must prove its efficacy and safety in patients. Success rates measured from first studies in human to registration average around 10% only. Over the last decade, massive knowledge on biological systems has been accumulated and genome-scale primary data are produced at an ever increasing rate. In parallel, methods to use that knowledge have matured. This chapter will present some of the problems facing the pharmaceutical industry and elaborate on the current state of network-driven analysis methods. It will focus especially on semi-quantitative methods that are applicable to large-scale data analysis and point out their potential use in many relevant drug discovery challenges.

A New Dynamic Protocol Analysis Model

2013 ◽  
Vol 765-767 ◽  
pp. 1761-1765
Author(s):  
Fu Lin Li ◽  
Jie Yang ◽  
Hong Wei Zhou ◽  
Ying Liu
Keyword(s):  
Static Analysis ◽  
Theorem Proving ◽  
Protocol Analysis ◽  
Experimental Results ◽  
Analysis Model ◽  
Formal Validation ◽  
Analysis Methods

Traditional static analysis methods such as formal validation and theorem proving were used to analyze protocols security previously. These methods can not measure and evaluate actual security of protocols accurately for the setting and suppose are far from the actual conditions. This paper proposes a new dynamic protocol analysis model. The system based on the model can be used to active test in actual running conditions, analyze known protocols security, integrity, robustness, and analyze unknown protocols online, provide support for protocol designer. The systems structure, working flow and implementation of key modules are described. The experimental results validate the validity of the models design.

scholarly journals Analisis Aplikasi Malware pada Android dengan Metode Statik

2020 ◽  
Vol 3 (2) ◽  
Author(s):  
Samuel Sinambela ◽  
Aditya Robbi Pangestu ◽  
Rangga Feriyanto
Keyword(s):  
Static Analysis ◽  
Quantitative Methods ◽  
The Internet ◽  
Analysis Method ◽  
Internet Users

Abstrak: Terkadang pengguna android tidak hati-hati saat berselancar diinternet dan tanpa sadar sebuah malware telah terinstal di perangkat mereka. Malware inilah yang nantinya akan menjadi jalan bagi penyerang untuk mengambil sesuatu di perangkat korban. Maka dari itu, diperlukannya analisis untuk mengetahui apa saja akses yang dibutuhkan ketika sebuah Malware terunduh di perangkat korban. Tujuan Penelitian adalah untuk menganalisis mendapatkan informasi malware yang terdapat pada aplikasi. Metode yang dipakai dalam penelitian ini menggunakan metode kuantitatif. Sample malware akan diambil melalui iklan dan menganalisis malware tersebut menggunakan metode analisis static yang hanya membaca informasi malware tanpa harus menginstalnya. Hasil dari analisis, terdeteksi Malware dan memiliki perizinan yang tidak pada tempatnya. Seperti yang terlihat, aplikasi game tidak memerlukan izin untuk melihat panggilan yang berlangsung. Maka dari itu, diperlukannya kejelian pengguna agar tidak sembarangan mengunduh atau mengklik sesuatu ketika berinternet. User juga harus update pada keamanan perangkat yang dimiliki.Kata kunci: Malware, Analisis Statik, AndroidAbstract: Sometimes Android users are not careful when surfing the internet and unknowingly have malware installed on their devices. This malware is what will later become a way for attackers to take something on the victim's device. Therefore, analysis is needed to see what access is needed when malware is downloaded on the victim's device. The research objective is to analyze the malware information contained in the application. The method used in this research is quantitative methods. Malware samples will be taken through advertisements and analyze the malware using a static analysis method that only reads malware information without having to install it. As a result of the analysis, the malware was detected and had inappropriate permissions. As you can see, the game application is not allowed to see the ongoing calls. Therefore, users need carefulness, so they do not download or click something when surfing the internet. Users also have to update on their own devicesKeywords: Malware, Static Analysis, Android  

A Research on Detection and Identification of Harmful Gas Utilizing Cataluminescence-Based Sensor Array

2013 ◽  
Vol 288 ◽  
pp. 109-113
Author(s):  
Li Shan Zhou
Keyword(s):  
Metal Oxides ◽  
Static Analysis ◽  
Sensor Array ◽  
Working Temperature ◽  
Analysis Methods ◽  
Detection And Identification ◽  
Nanosized Catalysts ◽  
Promising Perspective

A novel cataluminescence(CTL)-based sensor array consisting of 9 types of catalytic nanomaterials was developed for the determination and identification of harmful gas. The sensing nanomaterials, including nano-sized metal oxides, carbonates and decorated nanoparticles, have been selected carefully. A 3 x 3 array was integrated by depositing these nanosized catalysts onto the ceramic chip. Dynamic and static analysis methods were utilized to characterize the performance of the sensor array to 4 kinds of harmful gas. Each compound gives its unique CTL pattern after interact with the sensor array, which can be employed to recognize ether, acetone, chloroform, and toluene. PCA was conducted to classify the harmful gas and the plots showed that the groups were well classified. In addition, the patterns obtained at different working temperature and the analytical characteristics of array were investigated. The CTL-based sensor array shows promising perspective for the recognition and discrimination of harmful gas.

A Research of Virus Detection Combined Dynamic and Static Analysis Methods

2011 ◽  
Vol 187 ◽  
pp. 625-630
Author(s):  
Chun Yu Miao ◽  
Li Na Chen
Keyword(s):  
Neural Network ◽  
Static Analysis ◽  
Detection System ◽  
Probabilistic Neural Network ◽  
Virus Detection ◽  
Support Vector ◽  
Svm Classifier ◽  
Analysis Methods ◽  
Individual Classifier ◽  
Theory Of Evidence

we present a virus detection system based on the D-S theory of evidence, in which the dynamic and static analysis methods are combined. The detection engine applies two types of classifier, support vector amchine and probabilistic neural network to detect the virus. For SVM classifier, we extract the feature vector by monitoring the samples. And the static feature of samples is used in the probabilistic neural network classifier. Finally, the D-S theory of evidence is used to combine the contribution of each individual classifier to give the final decision.experiments show the presented method is more efficiently of the virus detections.

scholarly journals Review on methods of imrpoving information security posture of the company by increasing end user awareness

2021 ◽  
Vol 143 (3) ◽  
pp. 185-189
Author(s):  
D. Berdysheva ◽  
◽  
A. Askhatuly ◽  
D. Yedilkhan ◽  
◽  
...  
Keyword(s):  
Information Security ◽  
End User ◽  
User Awareness
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