Efficient vertical handover in heterogeneous low-power wide-area networks

As the Internet of Things (IoT) continues to expand, the need to combine communication technologies to cope with the limitations of one another and to support more diverse requirements will proceed to increase. Consequently, we started to see IoT devices being equipped with multiple radio technologies to connect to different networks over time. However, the detection of the available radio technologies in an energy-efficient way for devices with limited battery capacity and processing power has not yet been investigated. As this is not a straightforward task, a novel approach in such heterogeneous networks is required. This article analyzes different low-power wide-area network technologies and how they can be integrated in such a heterogeneous system. Our contributions are threefold. First, an optimal protocol stack for a constrained device with access to multiple communication technologies is put forward to hide the underlying complexity for the application layer. Next, the architecture to hide the complexity of a heterogeneous network is presented. Finally, it is demonstrated how devices with limited processing power and battery capacity can have access to higher bandwidth networks combined with longer range networks and on top are able to save energy compared to their homogeneous counterparts, by measuring the impact of the novel vertical handover algorithm

The Missing Data Encoder: Cross-Channel Image Completion\\with Hide-And-Seek Adversarial Network

Image completion is the problem of generating whole images from fragments only. It encompasses inpainting (generating a patch given its surrounding), reverse inpainting/extrapolation (generating the periphery given the central patch) as well as colorization (generating one or several channels given other ones). In this paper, we employ a deep network to perform image completion, with adversarial training as well as perceptual and completion losses, and call it the ``missing data encoder'' (MDE). We consider several configurations based on how the seed fragments are chosen. We show that training MDE for ``random extrapolation and colorization'' (MDE-REC), i.e. using random channel-independent fragments, allows a better capture of the image semantics and geometry. MDE training makes use of a novel ``hide-and-seek'' adversarial loss, where the discriminator seeks the original non-masked regions, while the generator tries to hide them. We validate our models both qualitatively and quantitatively on several datasets, showing their interest for image completion, unsupervised representation learning as well as face occlusion handling

Imperfect camouflage: how to hide in a variable world?

Camouflage is an important anti-predator strategy for many animals and is traditionally thought of as being tightly linked to a specific visual background. While much work focuses on optimizing camouflage against one background, this may not be relevant for many species and contexts, as animals may encounter many different habitats throughout their lives due to temporal and spatial variation in their environment. How should camouflage be optimized when an animal or object is seen against multiple visual backgrounds? Various solutions may exist, including colour change to match new environments or use of behaviour to maintain crypsis by choosing appropriate substrates. Here, we focus on a selection of approaches under a third alternative strategy: animals may adopt (over evolution) camouflage appearances that represent an optimal solution against multiple visual scenes. One approach may include a generalist or compromise strategy, where coloration matches several backgrounds to some extent, but none closely. A range of other camouflage types, including disruptive camouflage, may also provide protection in multiple environments. Despite detailed theoretical work determining the plausibility of compromise camouflage and elucidating the conditions under which it might evolve, there is currently mixed experimental evidence supporting its value and little evidence of it in natural systems. In addition, there remain many questions including how camouflage strategies should be defined and optimized, and how they might interact with other types of crypsis and defensive markings. Overall, we provide a critical overview of our current knowledge about how camouflage can enable matching to multiple backgrounds, discuss important challenges of working on this question and make recommendations for future research

Covert Communication in Fading Channels under Channel Uncertainty

A covert communication system under block fading channels is considered where users experience uncertainty about their channel knowledge. The transmitter seeks to hide the covert communication to a private user by exploiting a legitimate public communication link while the warden tries to detect this covert communication by using a radiometer. We derive the exact expression for the radiometers optimal threshold which determines the performance limit of the wardens detector. Furthermore for given transmission outage constraints the achievable rates for legitimate and covert users are analyzed while maintaining a specific level of covertness. Our numerical results illustrate how the achievable performance is affected by the channel uncertainty and required level of covertness.Comment: to appear in IEEE VTC2017-Sprin

Predicting User-Interactions on Reddit

In order to keep up with the demand of curating the deluge of crowd-sourced content, social media platforms leverage user interaction feedback to make decisions about which content to display, highlight, and hide. User interactions such as likes, votes, clicks, and views are assumed to be a proxy of a content's quality, popularity, or news-worthiness. In this paper we ask: how predictable are the interactions of a user on social media? To answer this question we recorded the clicking, browsing, and voting behavior of 186 Reddit users over a year. We present interesting descriptive statistics about their combined 339,270 interactions, and we find that relatively simple models are able to predict users' individual browse- or vote-interactions with reasonable accuracy.Comment: Presented at ASONAM 201

Diversity As A Trade Secret

When we think of trade secrets, we often think of famous examples such as the Coca-Cola formula, Google’s algorithm, or McDonald’s special sauce used on the Big Mac. However, companies have increasingly made the novel argument that diversity data and strategies are protected trade secrets. This may sound like an unusual, even suspicious, legal argument. Many of the industries that dominate the economy in wealth, status, and power continue to struggle with a lack of diversity. Various stakeholders have mobilized to improve access and equity, but there is an information asymmetry that makes this pursuit daunting. When potential plaintiffs and other diversity advocates request workforce statistics and related employment information, many companies have responded with virulent attempts to maintain secrecy, including the use of trade secret protection. In this Article, I use the technology industry as an example to examine the trending legal argument of treating diversity as a trade secret. I discuss how companies can use this tactic to hide gender and race disparities and interfere with the advancement of civil rights law and workplace equity. I argue that instead of permitting companies to hide information, we should treat diversity data and strategies as public resources. This type of open model will advance the goals of equal opportunity law by raising awareness of inequalities and opportunities, motivating employers to invest in effective practices, facilitating collaboration on diversity goals, fostering innovation, and increasing accountability for action and progress

Introduction on intrusion detection systems : focus on hierarchical analysis

In today\u27s fast paced computing world security is a main concern. Intrusion detection systems are an important component of defensive measures protecting computer systems and networks from abuse. This paper will examine various intrusion detection systems. The task of intrusion detection is to monitor usage of a system and detect and malicious activity, therefore, the architecture is a key component when studying intrusion detection systems. This thesis will also analyze various neural networks for statistical anomaly intrusion detection systems. The thesis will focus on the Hierarchical Intrusion Detection system (HIDE) architecture. The HIDE system detects network based attack as anomalies using statistical preprocessing and neural network classification. The thesis will conclude with studies conducted on the HIDE architecture. The studies conducted on the HIDE architecture indicate how the hierarchical multi-tier anomaly intrusion detection system is an effective one

Command & Control: Understanding, Denying and Detecting - A review of malware C2 techniques, detection and defences

In this survey, we first briefly review the current state of cyber attacks, highlighting significant recent changes in how and why such attacks are performed. We then investigate the mechanics of malware command and control (C2) establishment: we provide a comprehensive review of the techniques used by attackers to set up such a channel and to hide its presence from the attacked parties and the security tools they use. We then switch to the defensive side of the problem, and review approaches that have been proposed for the detection and disruption of C2 channels. We also map such techniques to widely-adopted security controls, emphasizing gaps or limitations (and success stories) in current best practices.Comment: Work commissioned by CPNI, available at c2report.org. 38 pages. Listing abstract compressed from version appearing in repor