Hidden and Uncontrolled - On the Emergence of Network Steganographic Threats

Network steganography is the art of hiding secret information within innocent network transmissions. Recent findings indicate that novel malware is increasingly using network steganography. Similarly, other malicious activities can profit from network steganography, such as data leakage or the exchange of pedophile data. This paper provides an introduction to network steganography and highlights its potential application for harmful purposes. We discuss the issues related to countering network steganography in practice and provide an outlook on further research directions and problems.Comment: 11 page

Document flow tracking within corporate networks

Tese de mestrado, Segurança Informática, Universidade de Lisboa, Faculdade de Ciências, 2009Notícias sobre documentos sensíveis publicados na Internet são cada vez mais frequentes nos cabeçalhos da imprensa de hoje em dia. Em Outubro de 2009, o Manual de Segurança do Ministério da Defesa do Reino Unido, com 2389 páginas, que descreve a totalidade do protocolo militar do Reino Unido relativamente a operações e informações de segurança, foi tornado público por acidente. Este é apenas um caso, mas existem exemplos de fugas de informação em praticamente qualquer área, desde a médica à financeira. Estas fugas de informação podem ter consequências sérias para quem seja afectado por elas, como a exposição de segredos de negócio, danos da imagem de marca ou a aplicação de multas elevadas por parte de entidades reguladoras. Uma fuga de informação pode ter várias causas, sendo uma delas devido a empregados que expõem documentos sensíveis para o exterior da empresa, de forma não intencional. Neste trabalho propomos uma solução capaz de rastrear ficheiros numa rede empresarial e detectar situações que podem levar a que um documento sensível se torne público. Fazemos uso de um agente que é instalado nas máquinas que pretendemos monitorizar, que detecta e regista a utilização de ficheiros em operações potencialmente perigosas, como a cópia para um dispositivo amovível ou o envio por correio electrónico como anexo. Essas operações são registadas e recolhidas para uma localização central, onde podemos fazer uso de um motor de correlação para encontrar relações entre diferentes cópias de um mesmo ficheiro. Para finalizar, desenvolvemos e avaliámos um protótipo que implementa a solução proposta, provando que pode efectivamente ser usado para detectar fugas de informação.News about sensitive documents being leaked to the Internet are becoming a commonplace in today’s headlines. In October of 2009, the United Kingdom Ministry of Defense Manual of Security, with 2389 pages, which fully describes the United Kingdom military protocol for all security and counter-intelligence operations, was inadvertently made public. This is only one, but there are examples of information leaks from almost any area, from medical to financial. These information leaks can have serious consequences to those affected by them, such as exposing business secrets, brand damaging or large fines from regulation entities. An information leak can have multiple causes, being one the employee that inadvertently exposes sensitive documents to the exterior of the company. In this work, we propose a solution capable of tracking files within a corporate network and detecting situations that can lead to a sensitive document being leaked to the exterior. We resort to an agent installed on the hosts to be monitored that detects and logs the usage of files by potentially dangerous operations, such as copying it to a removable drive or sending it by e-mail as an attachment. Those operations are logged and collected to a central repository, where we make use of a correlation engine to find relationships between different copies of a same file. Finally, we have developed and evaluated a prototype that implements the proposed solution, proving that it can indeed be used to detect information leaks

Is Blockchain for Internet of Medical Things a Panacea for COVID-19 Pandemic?

The outbreak of the COVID-19 pandemic has deeply influenced the lifestyle of the general public and the healthcare system of the society. As a promising approach to address the emerging challenges caused by the epidemic of infectious diseases like COVID-19, Internet of Medical Things (IoMT) deployed in hospitals, clinics, and healthcare centers can save the diagnosis time and improve the efficiency of medical resources though privacy and security concerns of IoMT stall the wide adoption. In order to tackle the privacy, security, and interoperability issues of IoMT, we propose a framework of blockchain-enabled IoMT by introducing blockchain to incumbent IoMT systems. In this paper, we review the benefits of this architecture and illustrate the opportunities brought by blockchain-enabled IoMT. We also provide use cases of blockchain-enabled IoMT on fighting against the COVID-19 pandemic, including the prevention of infectious diseases, location sharing and contact tracing, and the supply chain of injectable medicines. We also outline future work in this area.Comment: 15 pages, 8 figure

A Survey on Block Chain and Bitcoin – Challenges & Applications

Block chain is as of late presented and changing the advanced world conveying another point of view to security, flexibility and productivity of framework. While at first promoted by Bit Coin, Block chain is significantly more than an establishment for digital money. It offers a safe method to trade any sort of good administration or exchange. This paper exhibits an exhaustive review on Block chain Technology and Bit coin. Bitcoin has emerged as the most successful crypto currency since its appearance back in 2009. Besides its security robustness, two main properties have probably been its key to success: anonymity and decentralization. In this paper, we provide a comprehensive description on the details that make such crypto currency an interesting research topic in the privacy community. We perform an exhaustive review of the bitcoin anonymity research papers that have been published so far and we outline some research challenges on that topic

Deep Graph Embedding for IoT Botnet Traffic Detection

Botnet attacks have mainly targeted computers in the past, which is a fundamental cybersecurity problem. Due to the booming of Internet of things (IoT) devices, an increasing number of botnet attacks are now targeting IoT devices. Researchers have proposed several mechanisms to avoid botnet attacks, such as identification by communication patterns or network topology and defence by DNS blacklisting. A popular direction for botnet detection currently relies on the specific topological characteristics of botnets and uses machine learning models. However, it relies on network experts’ domain knowledge for feature engineering. Recently, neural networks have shown the capability of representation learning. This paper proposes a new approach to extracting graph features via graph neural networks. To capture the particular topology of the botnet, we transform the network traffic into graphs and train a graph neural network to extract features. In our evaluations, we use graph embedding features to train six machine learning models and compare them with the performance of traditional graph features in identifying botnet nodes. The experimental results show that botnet traffic detection is still challenging even with neural networks. We should consider the impact of data, features, and algorithms for an accurate and robust solution