Generating Steganographic Text with LSTMs

Motivated by concerns for user privacy, we design a steganographic system ("stegosystem") that enables two users to exchange encrypted messages without an adversary detecting that such an exchange is taking place. We propose a new linguistic stegosystem based on a Long Short-Term Memory (LSTM) neural network. We demonstrate our approach on the Twitter and Enron email datasets and show that it yields high-quality steganographic text while significantly improving capacity (encrypted bits per word) relative to the state-of-the-art.Comment: ACL 2017 Student Research Worksho

A Covert Data Transport Protocol

Both enterprise and national firewalls filter network connections. For data forensics and botnet removal applications, it is important to establish the information source. In this paper, we describe a data transport layer which allows a client to transfer encrypted data that provides no discernible information regarding the data source. We use a domain generation algorithm (DGA) to encode AES encrypted data into domain names that current tools are unable to reliably differentiate from valid domain names. The domain names are registered using (free) dynamic DNS services. The data transmission format is not vulnerable to Deep Packet Inspection (DPI).Comment: 8 pages, 10 figures, conferenc

Smart techniques and tools to detect Steganography - a viable practice to Security Office Department

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementInternet is today a commodity and a way for being connect to the world. It is through Internet is where most of the information is shared and where people run their businesses. However, there are some people that make a malicious use of it. Cyberattacks have been increasing all over the recent years, targeting people and organizations, looking to perform illegal actions. Cyber criminals are always looking for new ways to deliver malware to victims to launch an attack. Millions of users share images and photos on their social networks and generally users find them safe to use. Contrary to what most people think, images can contain a malicious payload and perform harmful actions. Steganography is the technique of hiding data, which, combined with media files, can be used to place malicious code. This problem, leveraged by the continuous media file sharing through massive use of digital platforms, may become a worldwide threat in malicious content sharing. Like phishing, people and organizations must be trained to suspect about inappropriate content and implement the proper set of actions to reduce probability of infections when accessing files supposed to be inoffensive. The aim of this study will try to help people and organizations by trying to set a toolbox where it can be possible to get some tools and techniques to assist in dealing with this kind of situations. A theoretical overview will be performed over other concepts such as Steganalysis, touching also Deep Learning and in Machine Learning to assess which is the range of its applicability in find solutions in detection and facing these situations. In addition, understanding the current main technologies, architectures and users’ hurdles will play an important role in designing and developing the proposed toolbox artifact