Convolutional neural network for malware detection in IoT Network

[EN] The network has exploded in popularity in the twenty-first century in the last few years, becoming one of the most extensively utilized and prominent technologies. Nowadays, cyberattacks occurring and the variety, size, and intensity of cyberattacks are increasing. In this work, the machine learning method is used to predict Intrusion in the Internet of Things. Attacks on networks connected to smart cities or on intelligent transportation systems endanger the security of these networks. Studies show that IoT attacks can cost the network millions of dollars. DDoS attacks by malicious botnets and nodes on the IoT network are among the most malicious attacks on the network and can disable IoT application servers

Implementasi Machine Learning Sebagai Pengenal Nominal Uang Rupiah dengan Metode YOLOv3

Jumlah disabilitas kesulitan melihat (Tunanetra) di atas 10 tahun sebanyak 6,36% dari total penduduk yang mengalami disabilitas yaitu 8,56% pada tahun 2015. Permasalahan yang dihadapi penyandang tunanetra dalam kehidupan sehari-hari salah satunya mengenali nominal uang rupiah. Walaupun pemerintah sudah membuat uang dengan emboss pada emisi 2016, tetapi masih kurang efektif karena uang yang beredar kadang dalam kondisi tidak rapih. Untuk mengatasi hal tersebut dapat dibantu dengan menggunakan teknologi Machine learning berbasis Yolov3 dalam mengenali nominal uang Rupiah. Metode YOLOv3 mempunyai keunggulan dalam kecepatan pelatihan model dan nilai akurasinya yang tinggi, dan memang dirancang untuk mengolah gambar. Dataset yang digunakan untuk membuat model machine learning dikumpulkan dari berbagai gambar uang rupiah nominal 1000, 2000, 5000, 10000, 20000, 50000, 10000 sebanyak 4200 gambar. Model yang sudah dibuat selanjutnya diimplementasikan kedalam bentuk aplikasi android. Aplikasi dijalankan seperti melakukan scan uang dan memberikan hasil berupa suara yang menyebutkan nominal uang tersebut secara otomatis. Model ini dievaluasi dengan Confusion Matrix menghasilkan nilai accuracy, precision dan recall sebesar 0.98. Berdasarkan Nilai akurasi tersebut, model yang dibuat dapat membantu penyandang tunanetra dalam mengenali nominal uang rupiah

Revisión de algoritmos de detección de malware ofuscado basados en machine learning

Malware developers increasingly evolve their techniques to effectively attack the system, one of these techniques is code obfuscation that makes it difficult to detect malware in the traditional mechanisms used by current antiviruses. Given this, it is proposed to review articles related to the detection of obfuscated malware with machine learning to choose the best analysis techniques for this type of malware and use the best algorithms for future experimentation with them.Los desarrolladores de malware cada vez evolucionan más sus técnicas para lograr atacar efectivamente el sistema, una de estas técnicas es la ofuscación de código que dificulta la detección de malware en los mecanismos tradicionales que utilizan los antivirus actuales. Ante ello se propone la revisión de artículos relacionados a detección de malware ofuscado con machine learning para elegir las mejores técnicas de análisis de este tipo de malware y emplear los mejores algoritmos para una futura experimentación con los mismos. &nbsp

An Efficient Malware Detection Approach for Malicious Android Application

Artificial intelligence is changing the game for cybersecurity, analyzing enormous amount of risky data, increasing response times and enlarging the abilities of under-resourced security tasks. While security as IT percentage grows at a fast pace, the cost of security beaches grows at a more rapid pace. The malware targeting Android is growing. Android systems holds more than 70 percent of the market share.This paper presents a simple APK analysis approach with the help of neural networks to identify malicious and benign application. The selected methodology is efficient in detecting malwares with an accuracy of 98.42% and false positive rate of 0.012

Development of Android-Based Learning Media

This study aims to develop a learning media. This research was a type of development research by adapting the ADDIE development model (Analysis, Design, Development, Implementation). The object of this research was students in class IPA SMAN 1 Suwawa with a total of 117 students. The entire content of the material, media, and language in the learning media had to undergo an expert validation process to create draft II. Draft II then underwent product revisions in a limited trial and created draft III. Then, in a field trial with a total of 117 students, the response received a "very strong" percentage with a percentage of 94.50%, creating the final product improvement for researchers. The results of the research were creating learning media in the form of Android-based applications in employment subjects with details on the homepage feature, Basic Competency & Learning Objectives feature, learning material feature, Practice Questions feature, and About featurePenelitian ini bertujuan untuk mengembangkan sebuah media pembelajaran. Penelitian ini merupakan jenis penelitian pengembangan dengan mengadaptasi model pengembangan ADDIE (Analysis, Design, Development, Implementation). Objek penelitian ini adalah siswa kelas IPA SMAN 1 Suwawa yang berjumlah 117 siswa. Seluruh isi materi, media, dan bahasa dalam media pembelajaran harus melalui proses validasi ahli untuk membuat draf II. Draf II kemudian mengalami revisi produk dalam uji coba terbatas dan menghasilkan draf III. Kemudian, dalam uji coba lapangan dengan total 117 siswa, respon mendapat persentase “sangat kuat” dengan persentase 94,50%, menciptakan peningkatan produk akhir bagi peneliti. Hasil penelitian adalah pembuatan media pembelajaran berupa aplikasi berbasis Android pada mata pelajaran ketenagakerjaan dengan detail pada fitur homepage, fitur Kompetensi Dasar & Tujuan Pembelajaran, fitur materi pembelajaran, fitur Soal Latihan, dan fitur About

Android Malware Detection using Machine Learning Techniques

Android is the world\u27s most popular and widely used operating system for mobile smartphones today. One of the reasons for this popularity is the free third-party applications that are downloaded and installed and provide various types of benefits to the user. Unfortunately, this flexibility of installing any application created by third parties has also led to an endless stream of constantly evolving malware applications that are intended to cause harm to the user in many ways. In this project, different approaches for tackling the problem of Android malware detection are presented and demonstrated. The data analytics of a real-time detection system is developed. The detection system can be used to scan through installed applications to identify potentially harmful ones so that they can be uninstalled. This is achieved through machine learning models. The effectiveness of the models using two different types of features, namely permissions and signatures, is explored. Exploratory data analysis and feature engineering are first implemented on each dataset to reduce a large number of features available. Then, different data mining supervised classification models are used to classify whether a given app is malware or benign. The performance metrics of different models are then compared to identify the technique that offers the best results for this purpose of malware detection. It is observed in the end that the signatures-based approach is more effective than the permissions-based approach. The kNN classifier and Random Forest classifier are both equally effective in terms of the classification models

Snowball-Miner: Integration of Deep Learning for Extraction of Cyber Threat Intelligence from Dark Web

In Cyber threat intelligence is a crucial component in defending against cybersecurity threats. Cyber security dark web, security Blogs, Hackers’ community, news forums, Open-Source Intelligence (OSINT) are known as the harbor of illicit activities and serve as a breeding ground for cybercriminals. Extracting actionable intelligence from the dark web is challenging due to its anonymous and encrypted nature. State-of-art work proposed machine learning and deep learning approach to aggregate the dark web for cyber threat intelligence from data present in the dark web.  This paper proposes, a novel approach utilizing Snowball-Miner for cyber threat intelligence discovery from the dark web. The model is trained on a diverse dataset consisting of dark web forums, hidden .onion based marketplaces and other underground platforms using Snowball-crawler. However, we have employed hybrid convolutional model CNN-LSTM and CNN-GRU adopting doc2vec word embedding to classify into four domains viz Energy Sector, Finance, Illicit Activities and illegal Services. From our experiment it emerged that, CNN-LSTM outperforms as 96.37% for classification of domain specific threat documents. Furthermore, after data preparation we implemented NLP technique and extracted the domain specific Indicator of Compromise (IoCs) using RegEx parser and Subject, Object and Verb (SOV) semantics dependency analysis. Finally, we have integrated IoCs and Threat keywords with respective domains to generate domain specific threat intelligence which enhance the quality of the domain specific CTI based on R-dimension (Relevance)

Academic Program for the Relationship with Companies: An M-Learning Strategy to Promote University-Business Collaborations

[EN] The interest in including mobile learning (m-learning) in training processes has grown considerably. In recent years, there is evidence of a progressive research production on the subject; especially in Latin America, it is seen as an alternative to bridge the educational gaps. In the context of higher education in Colombia, most university students work full-time and study at night, it makes it difficult for them to do internships and makes them feel vulnerable when taking on job positions associated with their profession. Our goal is to create a virtual internship laboratory supported by a mobile application developed specifically for this purpose. It combines Problem-Based Learning (PBL) with Action- and Decision-oriented Research (ADR). Through a descriptive qualitative study, a mobile application is designed and validated to promote the university-business relationship, it was named Academic Program for the Relationship with Companies (PARCE, i.e., Programa Académico de Relacionamiento con Empresas). The introduction contextualizes university education in Colombia and the receptivity of mobile learning, subsequently, the article describes the technical characteristics and qualities of the developed mobile application, as well as the methodology, approach, and data collection process. The information is analyzed by descriptive statistics categorized by dimensions to assess the impact of the mobile application on internship processes in a higher education institution. The academic performance was positively assessed with respect to the development of skills and professional abilities acquired and enhanced thanks to the flexibility of the mobile application and the PARCE program. Although it has been designed especially for internship processes, it is adaptable to different areas of knowledge and educational levels. The most recurrent rating of the program, on a scale from 1 to 5, by entrepreneurs was 4.82

Integrated information gain with extra tree algorithm for feature permission analysis in android malware classification

The rapid growth of free applications in the android market has led to the fast spread of malware apps since users store their sensitive personal information on their mobile devices when using those apps. The permission mechanism is designed as a security layer to protect the android operating system by restricting access to local resources of the system at installation time and run time for updated versions of the android operating system. Even though permissions provide a secure layer to users, they can be exploited by attackers to threaten user privacy. Consequently, exploring the patterns of those permissions becomes necessary to find the relevant permission features that contribute to classifying android apps. However, with the era of big data and the rapid explosion of malware along with many unnecessary requested permissions, it has become a challenge to recognize the patterns of permissions from these data due to the irrelevant and redundant features that affect the classification performance and increase the complexity cost overhead. Ensemble-based Extra Tree - Feature Selection (FS-EX) algorithm was proposed in this study to explore the permission patterns by selecting a minimal-sized subset of highly discriminant permission features capable of discriminating against malware samples from nonmalware samples. The integrated Information Gain with Ensemble-based Extra Tree - Feature Selection (FS-IGEX) algorithm is proposed to assign weight values to permission features instead of binary values to determine the impact of weighted attribute variables on the classification performance. The two proposed methods based on Ensemble Extra Tree Feature Selection were evaluated on five datasets with various sample sizes and feature space using nine machine learning classifiers. Comparison studies were carried out between FS-EX subsets and the dataset of Full Permission features (FP) and the two approaches of the FS-IGEX method - the Permission-Binary (PB) approach and the Permission-Weighted (PW) approach. The permissions with PB were represented with binary values, whereas permissions with PW were represented with weighted values. The results demonstrated that the approach with the FS-EX was promising in obtaining the most prominent permission features related to the class target and attaining the same or close classification results in terms of accuracy with the highest accuracy mean of 96%, as compared to the FP. In addition, the PW approach of the FS-IGEX method had highly influential weighted permission features that could classify apps as malware and non-malware with the highest accuracy mean of 93%, compared to the PB approach of the FS-IGEX method and the FP

Malware Detection In Android Using Machine Learning

In an era that is increasingly fast with advanced technology, smartphones are a priority and a necessity for everyone. These gadgets are developing every day towards more advanced and appropriate ways of use. However, security is one of the causes of concern for many smartphone users. Safety is an important aspect that is highly regarded and taken seriously by some parties, and if this safety issue is taken for granted and not taken care of, it will cause problems to the people surrounding. Just like the security issue of smartphone users, which is now increasingly prevalent with one of the biggest threats to all gadgets, which is the malware issue. Studies have shown that there is an increase from year to year regarding malware that is more focused on attacking and damaging the victim's smartphone, especially for Android users. Many Android users have been affected by this malware problem and various solutions have been implemented. This study aims to examine the ways and methods of detecting malware that has attacked the Android operating system, and suggest the detection of a malware detection system by using machine learning techniques. The results show that machine learning is a more promising approach with 90% accuracy in experiments that have been conducted for machine learning methods for higher malware detection and prove that this malware detection system can detect Android malware more efficiently