Mal-Netminer: Malware Classification Approach based on Social Network Analysis of System Call Graph

As the security landscape evolves over time, where thousands of species of malicious codes are seen every day, antivirus vendors strive to detect and classify malware families for efficient and effective responses against malware campaigns. To enrich this effort, and by capitalizing on ideas from the social network analysis domain, we build a tool that can help classify malware families using features driven from the graph structure of their system calls. To achieve that, we first construct a system call graph that consists of system calls found in the execution of the individual malware families. To explore distinguishing features of various malware species, we study social network properties as applied to the call graph, including the degree distribution, degree centrality, average distance, clustering coefficient, network density, and component ratio. We utilize features driven from those properties to build a classifier for malware families. Our experimental results show that influence-based graph metrics such as the degree centrality are effective for classifying malware, whereas the general structural metrics of malware are less effective for classifying malware. Our experiments demonstrate that the proposed system performs well in detecting and classifying malware families within each malware class with accuracy greater than 96%.Comment: Mathematical Problems in Engineering, Vol 201

Digital system of quarry management as a SAAS solution: mineral deposit module

Purpose. Improving the efficiency of functioning the mining enterprises and aggregation of earlier obtained results into a unified digital system of designing and operative management by quarry operation. Methods. Both the traditional (analysis of scientific and patent literature, analytical methods of deposit parameters research, analysis of experience and exploitation of quarries, conducting the passive experiment and processing the statistical data) and new forms of scientific research - deposit modeling on the basis of classical and neural network methods of approximation – are used in the work. For the purpose of the software product realization on the basis of cloud technologies, there were used: for back-end implementation – server-based scripting language php; for the front-end – multi-paradigm programming language javascript, javascript framework jQuery and asynchronous data exchange technology Ajax. Findings. The target audience of the system has been identified, SWOT-analysis has been carried out, conceptual directions of 3D-quarry system development have been defined. The strategies of development and promotion of the software product, as well as the strategies of safety and reliability of the application both for the client and the owner of the system have been formulated. The modular structure of the application has been developed, and the system functions have been divided to implement both back-end and front-end applications. The Mineral Deposit Module has been developed: the geological structure of the deposit has been simulated and its block model has been constructed. It has been proved that the use of neural network algorithms does not give an essential increase in the accuracy of the block model for the deposits of 1 and 2 groups in terms of the geological structure complexity. The possibility and prospects of constructing the systems for subsoil users on the basis of cloud technologies and the concept of SaaS have been substantiated. Originality. For the first time, the modern software products for solving the problems of designing and operational management of mining operations have been successfully developed on the basis of the SaaS concept. Practical implications. The results are applicable for enterprises-subsoil users, working with deposits of 1 and 2 groups in terms of the geological structure complexity: design organizations, as well as mining and processing plants.Мета. Підвищення ефективності функціонування гірничорудних підприємств та агрегація раніше отриманих результатів в єдину цифрову систему проектування і оперативного управління роботою кар’єрів. Методика. У роботі використані як традиційні (аналіз науково-патентної літератури, аналітичні методи дослідження параметрів родовища, аналіз досвіду й експлуатації кар’єрів, проведення пасивного експерименту та статистичної обробки даних), так і нові форми наукового дослідження – моделювання родовища на основі класичних і нейромережевих методів апроксимації. Для реалізації програмного продукту на основі хмарних технологій використані: для реалізації back-end – серверна скриптова мова програмування php; для front-end – мультипарадігменна мова програмування javascript, javascript framework jQuery і технологія асинхронного обміну даними Ajax. Результати. Виявлено цільову аудиторію системи, проведено SWOT-аналіз, визначено концептуальні напрями розвитку системи 3D-кар’єр, розроблені стратегії розвитку та просування програмного продукту, розроблені стратегії безпеки й надійності додатки як для клієнта, так і власника системи. Розроблено модульну структуру програми, вироблено розподіл функцій системи для реалізації як back-end і front-end додатки. Розроблено модуль “Родовище”: проведено моделювання геологічної структури родовища та побудована його блокова модель. Доведено, що використання нейромережевих алгоритмів не дає принципового підвищення точності блокової моделі для родовищ 1 і 2 груп за складністю геологічної будови. Виявлено недоліки нейромережевих алгоритмів, такі як високі витрати обчислювальних ресурсів сервера і проблеми візуалізації великих масивів геоданих при використанні web-рішень, знайдені шляхи їх вирішення. Доведено можливість і перспективність побудови систем для надрокористувачів на основі хмарних технологій і концепції SaaS. Наукова новизна. Вперше на основі концепції ASP успішно побудовані сучасні програмні продукти для вирішення завдань проектування та оперативного керування гірничими роботами. Практична значимість. Результати корисні для підприємств-надрокористувачів, які працюють з родовищами 1 і 2 груп за складністю геологічної будови – проектних організацій і ГЗК.Цель. Повышение эффективности функционирования горнорудных предприятий и агрегация ранее полученных результатов в единую цифровую систему проектирования и оперативного управления работой карьеров. Методика. В работе использованы как традиционные (анализ научно-патентной литературы, аналитические методы исследования параметров месторождения, анализ опыта и эксплуатации карьеров, проведение пассивного эксперимента и статистической обработкой данных), так и новые формы научного исследования – моделирование месторождения на основе классических и нейросетевых методов аппроксимации. Для реализации программного продукта на основе облачных технологий использованы: для реализации back-end – серверный скриптовый язык программирования php; для front-end – мультипарадигменный язык программирования javascript, javascript framework jQuery и технология асинхронного обмена данными Ajax. Результаты. Выявлена целевая аудитория системы, проведен SWOT-анализ, определены концептуальные направления развития системы 3D-карьер, разработаны стратегии развития и продвижения программного продукта, разработаны стратегии безопасности и надежности приложения как для клиента, так и владельца системы. Разработана модульная структура приложения, произведено деление функций системы для реализации как back-end и front-end приложения. Разработан модуль “Месторождение”: проведено моделирование геологической структуры месторождения и построена его блочная модель. Доказано, что использование нейросетевых алгоритмов не дает принципиального повышения точности блочной модели для месторождений 1 и 2 групп по сложности геологического строения. Выявлены недостатки нейросетевых алгоритмов, такие как высокие затраты вычислительных ресурсов сервера и проблемы визуализации больших массивов геоданных при использовании web-решений, найдены пути их решения. Доказана возможность и перспективность построения систем для недропользователей на основе облачных технологий и концепции SaaS. Научная новизна. Впервые на основе концепции ASP успешно построены современные программные продукты для решения задач проектирования и оперативного управления горными работами. Практическая значимость. Результаты применимы для предприятий-недропользователей, работающих с месторождениями 1 и 2 групп по сложности геологического строения – проектных организаций и ГОКов.We express our profound gratitude to A.B. Naizabekov for his assistance in scientific research, to A.F. Tsekhovoy, P.A. Tsekhovoy, D.Sh. Akhmedov, V. V. Yankovenko and D.V. Nikitas for scientific advice in implementation of the program code. The research was carried out within the framework of the initiative research theme “Improving the Efficiency of Mining Enterprises” on the basis of the RSE at the Rudny Industrial Institute of the Ministry of Education and Science of the Republic of Kazakhstan

Machine Learning Aided Static Malware Analysis: A Survey and Tutorial

Malware analysis and detection techniques have been evolving during the last decade as a reflection to development of different malware techniques to evade network-based and host-based security protections. The fast growth in variety and number of malware species made it very difficult for forensics investigators to provide an on time response. Therefore, Machine Learning (ML) aided malware analysis became a necessity to automate different aspects of static and dynamic malware investigation. We believe that machine learning aided static analysis can be used as a methodological approach in technical Cyber Threats Intelligence (CTI) rather than resource-consuming dynamic malware analysis that has been thoroughly studied before. In this paper, we address this research gap by conducting an in-depth survey of different machine learning methods for classification of static characteristics of 32-bit malicious Portable Executable (PE32) Windows files and develop taxonomy for better understanding of these techniques. Afterwards, we offer a tutorial on how different machine learning techniques can be utilized in extraction and analysis of a variety of static characteristic of PE binaries and evaluate accuracy and practical generalization of these techniques. Finally, the results of experimental study of all the method using common data was given to demonstrate the accuracy and complexity. This paper may serve as a stepping stone for future researchers in cross-disciplinary field of machine learning aided malware forensics.Comment: 37 Page

Web-based Geographical Visualization of Container Itineraries

Around 90% of the world cargo is transported in maritime containers, but only around 2% are physically inspected. This opens the possibility for illicit activities. A viable solution is to control containerized cargo through information-based risk analysis. Container route-based analysis has been considered a key factor in identifying potentially suspicious consignments. Essential part of itinerary analysis is the geographical visualization of the itinerary. In the present paper, we present initial work of a web-based system’s realization for interactive geographical visualization of container itinerary.JRC.G.4-Maritime affair

Advanced Information Systems and Technologies

This book comprises the proceedings of the V International Scientific Conference "Advanced Information Systems and Technologies, AIST-2017". The proceeding papers cover issues related to system analysis and modeling, project management, information system engineering, intelligent data processing computer networking and telecomunications. They will be useful for students, graduate students, researchers who interested in computer science

Advanced Information Systems and Technologies

This book comprises the proceedings of the V International Scientific Conference "Advanced Information Systems and Technologies, AIST-2017". The proceeding papers cover issues related to system analysis and modeling, project management, information system engineering, intelligent data processing computer networking and telecomunications. They will be useful for students, graduate students, researchers who interested in computer science

GRASE: Granulometry Analysis with Semi Eager Classifier to Detect Malware

Technological advancement in communication leading to 5G, motivates everyone to get connected to the internet including ‘Devices’, a technology named Web of Things (WoT). The community benefits from this large-scale network which allows monitoring and controlling of physical devices. But many times, it costs the security as MALicious softWARE (MalWare) developers try to invade the network, as for them, these devices are like a ‘backdoor’ providing them easy ‘entry’. To stop invaders from entering the network, identifying malware and its variants is of great significance for cyberspace. Traditional methods of malware detection like static and dynamic ones, detect the malware but lack against new techniques used by malware developers like obfuscation, polymorphism and encryption. A machine learning approach to detect malware, where the classifier is trained with handcrafted features, is not potent against these techniques and asks for efforts to put in for the feature engineering. The paper proposes a malware classification using a visualization methodology wherein the disassembled malware code is transformed into grey images. It presents the efficacy of Granulometry texture analysis technique for improving malware classification. Furthermore, a Semi Eager (SemiE) classifier, which is a combination of eager learning and lazy learning technique, is used to get robust classification of malware families. The outcome of the experiment is promising since the proposed technique requires less training time to learn the semantics of higher-level malicious behaviours. Identifying the malware (testing phase) is also done faster. A benchmark database like malimg and Microsoft Malware Classification challenge (BIG-2015) has been utilized to analyse the performance of the system. An overall average classification accuracy of 99.03 and 99.11% is achieved, respectively