SQL Injection Vulnerability Detection Using Deep Learning: A Feature-based Approach

SQL injection (SQLi), a well-known exploitation technique, is a serious risk factor for database-driven web applications that are used to manage the core business functions of organizations. SQLi enables an unauthorized user to get access to sensitive information of the database, and subsequently, to the application’s administrative privileges. Therefore, the detection of SQLi is crucial for businesses to prevent financial losses. There are different rules and learning-based solutions to help with detection, and pattern recognition through support vector machines (SVMs) and random forest (RF) have recently become popular in detecting SQLi. However, these classifiers ensure 97.33% accuracy with our dataset. In this paper, we propose a deep learning-based solution for detecting SQLi in web applications. The solution employs both correlation and chi-squared methods to rank the features from the dataset. Feed-forward network approach has been applied not only in feature selection but also in the detection process. Our solution provides 98.04% accuracy over 1,850+ recorded datasets, where it proves its superior efficiency among other existing machine learning solutions

Analisis sentimen review aplikasi berita online pada google play menggunakan metode Algoritma Naive Bayes Classifier dan Support Vector Machines

ABSTRAK Media berita onlie sebagai media massa yang paling banyak dikonsumsi publik yang bisa menggunguli media era sebelumnya misalnya media elektronika dan media cetak. Keunggulan media onine dibanding media cetak biasanya yaitu praktis, real time dan up to date. Penilaian umum atas layanan dan berita yang diberikan sangat penting untuk menjaga dan meningkatkan kinerja media berita online. Adapun evaluasi publik dapat di lihat melalui page Google Play dalam kolom opini user. Analisis sentimen bisa menganalis opini tersebut,dengan proses menganalisa dan mengekstrasi data teks yang tidak struktur untuk menghasilkan informasi sentimen yang terdapat dalam kalimat opini pada aplikasi. Dalam penelitian analisis sentimen ini, mengimplementasikan algoritma Naive Bayes dan SVM (Support Vektor Machines). Hasil eksperimen menunjukkan bahwa keauratan SVM (Support Vektor Machines) 94.06 % lebih mengunguli dari pada Naive Bayes 91.58%. ABSTRACT Online news media is the most widely consumed mass media by the public, which can outperform the media of the previous era, such as electronic media and print media. The advantage of online media compared to print media is that it is practical, real time and up to date. General assessment of services and news provided is very important to maintain and improve the performance of online news media. The public evaluation can be seen via the Google Play page in the user opinion column. Sentiment analysis can analyze these opinions, with the process of analyzing and extracting unstructured text data to produce sentiment information contained in opinion sentences in the application. In this sentiment analysis research, implementing the Naive Bayes algorithm and SVM (Support Vector Machines). The experimental results show that the accuracy of SVM (Support Vector Machines) 94.06% outperforms Naive Bayes 91.58%. مستخلص البحث الإنترنت هي أكثر وسائل الإعلام استخدامًا من قبل الجمهور ، والتي يمكن أن تتفوق على وسائل الإعلام في الحقبة السابقة ، على سبيل المثال ، الوسائط الإلكترونية والوسائط المطبوعة. تتمثل ميزة الوسائط عبر الإنترنت مقارنة بالوسائط المطبوعة في أنها عملية وفي الوقت الفعلي ومحدثة. التقييم العام للخدمات والأخبار المقدمة مهم للغاية للحفاظ على أداء وسائل الإعلام الإخبارية على الإنترنت وتحسينه. يمكن مشاهدة التقييم العام عبر صفحة في عمود رأي المستخدم. يمكن لتحليل المشاعر تحليل هذه الآراء ، من خلال عملية تحليل واستخراج البيانات النصية غير المهيكلة لإنتاج معلومات المشاعر الواردة في جمل الرأي في التطبيق. في بحث تحليل المشاعر هذا ، تم تنفيذ خوارزمية و (آلات المتجهات الداعمة). أظهرت النتائج التجريبية أن دقة (آلات المتجهات الداعمة) تبلغ ٩٤,٦.٪ تفوقت عل ٩١,٥٨

Sentiment analysis of persian movie reviews using deep learning

Sentiment analysis aims to automatically classify the subject’s sentiment (e.g., positive, negative, or neutral) towards a particular aspect such as a topic, product, movie, news, etc. Deep learning has recently emerged as a powerful machine learning technique to tackle the growing demand for accurate sentiment analysis. However, the majority of research efforts are devoted to English-language only, while information of great importance is also available in other languages. This paper presents a novel, context-aware, deep-learning-driven, Persian sentiment analysis approach. Specifically, the proposed deep-learning-driven automated feature-engineering approach classifies Persian movie reviews as having positive or negative sentiments. Two deep learning algorithms, convolutional neural networks (CNN) and long-short-term memory (LSTM), are applied and compared with our previously proposed manual-feature-engineering-driven, SVM-based approach. Simulation results demonstrate that LSTM obtained a better performance as compared to multilayer perceptron (MLP), autoencoder, support vector machine (SVM), logistic regression and CNN algorithms

What people complain about drone apps? a large-scale empirical study of Google play store reviews

Within the past few years, there has been a tremendous increase in the number of UAVs (Unmanned Aerial Vehicle) or drones manufacture and purchase. It is expected to proliferate further, penetrating into every stream of life, thus making its usage inevitable. The UAV’s major components are its physical hardware and programming software, which controls its navigation or performs various tasks based on the field of concern. The drone manufacturers launch the controlling app for the drones in mobile app stores. A few drone manufacturers also release development kits to aid drone enthusiasts in developing customized or more creative apps. Thus, the app stores are also expected to be flooded with drone-related apps in the near future. With various active research and studies being carried out in UAV’s hardware field, no effort is dedicated to studying/researching the software side of UAV. Towards this end, a large-scale empirical study of UAV or drone-related apps of the Google Play Store Platform is conducted. The study consisted of 1,825 UAV mobile apps, across twenty-five categories, with 162,250 reviews. Some of the notable findings of the thesis are (a) There are 27 major types of issues the drone app users complain about, (b) The top four complaints observed are Functional Error (27.9%), Device Compatibility (16.8%), Cost (16.2%) and Connection/Sync (15.6%), (c) The top four issues for which the UAV manufactures or Drone app developers provide feedback to user complaints are Functional Error (40.9%), Cost (33.3%), Device Compatibility (23.1%) and ConnectionSync (16%), (d) Developers respond to the most frequently occurring complaints rather than the most negatively impacting ones

Deep Learning for Sentiment Analysis on Google Play Consumer Review

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