1,367 research outputs found
apk2vec: Semi-supervised multi-view representation learning for profiling Android applications
Building behavior profiles of Android applications (apps) with holistic, rich
and multi-view information (e.g., incorporating several semantic views of an
app such as API sequences, system calls, etc.) would help catering downstream
analytics tasks such as app categorization, recommendation and malware analysis
significantly better. Towards this goal, we design a semi-supervised
Representation Learning (RL) framework named apk2vec to automatically generate
a compact representation (aka profile/embedding) for a given app. More
specifically, apk2vec has the three following unique characteristics which make
it an excellent choice for largescale app profiling: (1) it encompasses
information from multiple semantic views such as API sequences, permissions,
etc., (2) being a semi-supervised embedding technique, it can make use of
labels associated with apps (e.g., malware family or app category labels) to
build high quality app profiles, and (3) it combines RL and feature hashing
which allows it to efficiently build profiles of apps that stream over time
(i.e., online learning). The resulting semi-supervised multi-view hash
embeddings of apps could then be used for a wide variety of downstream tasks
such as the ones mentioned above. Our extensive evaluations with more than
42,000 apps demonstrate that apk2vec's app profiles could significantly
outperform state-of-the-art techniques in four app analytics tasks namely,
malware detection, familial clustering, app clone detection and app
recommendation.Comment: International Conference on Data Mining, 201
Towards Adversarial Malware Detection: Lessons Learned from PDF-based Attacks
Malware still constitutes a major threat in the cybersecurity landscape, also
due to the widespread use of infection vectors such as documents. These
infection vectors hide embedded malicious code to the victim users,
facilitating the use of social engineering techniques to infect their machines.
Research showed that machine-learning algorithms provide effective detection
mechanisms against such threats, but the existence of an arms race in
adversarial settings has recently challenged such systems. In this work, we
focus on malware embedded in PDF files as a representative case of such an arms
race. We start by providing a comprehensive taxonomy of the different
approaches used to generate PDF malware, and of the corresponding
learning-based detection systems. We then categorize threats specifically
targeted against learning-based PDF malware detectors, using a well-established
framework in the field of adversarial machine learning. This framework allows
us to categorize known vulnerabilities of learning-based PDF malware detectors
and to identify novel attacks that may threaten such systems, along with the
potential defense mechanisms that can mitigate the impact of such threats. We
conclude the paper by discussing how such findings highlight promising research
directions towards tackling the more general challenge of designing robust
malware detectors in adversarial settings
Security Management Framework for the Internet of Things
The increase in the design and development of wireless communication technologies
offers multiple opportunities for the management and control of cyber-physical systems
with connections between smart and autonomous devices, which provide the delivery
of simplified data through the use of cloud computing. Given this relationship with the
Internet of Things (IoT), it established the concept of pervasive computing that allows
any object to communicate with services, sensors, people, and objects without human
intervention. However, the rapid growth of connectivity with smart applications through
autonomous systems connected to the internet has allowed the exposure of numerous
vulnerabilities in IoT systems by malicious users.
This dissertation developed a novel ontology-based cybersecurity framework to
improve security in IoT systems using an ontological analysis to adapt appropriate
security services addressed to threats. The composition of this proposal explores
two approaches: (1) design time, which offers a dynamic method to build security
services through the application of a methodology directed to models considering
existing business processes; and (2) execution time, which involves monitoring the IoT
environment, classifying vulnerabilities and threats, and acting in the environment,
ensuring the correct adaptation of existing services.
The validation approach was used to demonstrate the feasibility of implementing the
proposed cybersecurity framework. It implies the evaluation of the ontology to offer
a qualitative evaluation based on the analysis of several criteria and also a proof of
concept implemented and tested using specific industrial scenarios. This dissertation
has been verified by adopting a methodology that follows the acceptance in the research
community through technical validation in the application of the concept in an industrial
setting.O aumento no projeto e desenvolvimento de tecnologias de comunicação sem fio oferece
múltiplas oportunidades para a gestão e controle de sistemas ciber-físicos com conexões
entre dispositivos inteligentes e autônomos, os quais proporcionam a entrega de dados
simplificados através do uso da computação em nuvem. Diante dessa relação com
a Internet das Coisas (IoT) estabeleceu-se o conceito de computação pervasiva que
permite que qualquer objeto possa comunicar com os serviços, sensores, pessoas e objetos
sem intervenção humana. Entretanto, o rápido crescimento da conectividade com as
aplicações inteligentes através de sistemas autônomos conectados com a internet permitiu
a exposição de inúmeras vulnerabilidades dos sistemas IoT para usuários maliciosos.
Esta dissertação desenvolveu um novo framework de cibersegurança baseada em
ontologia para melhorar a segurança em sistemas IoT usando uma análise ontológica
para a adaptação de serviços de segurança apropriados endereçados para as ameaças. A
composição dessa proposta explora duas abordagens: (1) tempo de projeto, o qual oferece
um método dinâmico para construir serviços de segurança através da aplicação de uma
metodologia dirigida a modelos, considerando processos empresariais existentes; e (2)
tempo de execução, o qual envolve o monitoramento do ambiente IoT, a classificação de
vulnerabilidades e ameaças, e a atuação no ambiente garantindo a correta adaptação dos
serviços existentes.
Duas abordagens de validação foram utilizadas para demonstrar a viabilidade da
implementação do framework de cibersegurança proposto. Isto implica na avaliação da
ontologia para oferecer uma avaliação qualitativa baseada na análise de diversos critérios
e também uma prova de conceito implementada e testada usando cenários específicos.
Esta dissertação foi validada adotando uma metodologia que segue a validação na
comunidade científica através da validação técnica na aplicação do nosso conceito em
um cenário industrial
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Evaluating the resilience and security of boundaryless, evolving socio-technical Systems of Systems
Security comparison of ownCloud, Nextcloud, and Seafile in open source cloud storage solutions
Cloud storage has become one of the most efficient and economical ways to store data over the web. Although most organizations have adopted cloud storage, there are numerous privacy and security concerns about cloud storage and collaboration. Furthermore, adopting public cloud storage may be costly for many enterprises. An open-source cloud storage solution for cloud file sharing is a possible alternative in this instance. There is limited information on system architecture, security measures, and overall throughput consequences when selecting open-source cloud storage solutions despite widespread awareness. There are no comprehensive comparisons available to evaluate open-source cloud storage solutions (specifically owncloud, nextcloud, and seafile) and analyze the impact of platform selections. This thesis will present the concept of cloud storage, a comprehensive understanding of three popular open-source features, architecture, security features, vulnerabilities, and other angles in detail. The goal of the study is to conduct a comparison of these cloud solutions so that users may better understand the various open-source cloud storage solutions and make more knowledgeable selections. The author has focused on four attributes: features, architecture, security, and vulnerabilities of three cloud storage solutions ("ownCloud," "Nextcloud," and "Seafile") since most of the critical issues fall into one of these classifications. The findings show that, while the three services take slightly different approaches to confidentiality, integrity, and availability, they all achieve the same purpose. As a result of this research, the user will have a better understanding of the factors and will be able to make a more informed decision on cloud storage options
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