We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

This review examines the dichotomy between automatic and autonomous behaviors in surgical robots, maps the possible levels of autonomy of these robots, and describes the primary enabling technologi...

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Autonomy in Surgical Robotics

International traceability to the SI is increasingly demanded for measurements in chemistry. These concern measurements made not only for industrial chemical manufacturing but also for checking the safety of a wide range of food and agricultural products, for environmental protection, for medicine, as well as for a multitude of regulatory purposes. This article outlines the role that the Comite Consultatif pour la Quantite de Matiere (CCQM) can play in establishing traceability at the highest level for such measurements. A brief account is given of the actions taken so far by the Committee. These include the precise description of primary methods, the meaning of traceability for measurements in chemistry and the organization of some key international comparisons designed to test the procedures drawn up to guide them. Plans for future work are described.

The Comité Consultatif pour la Quantité de Matière: a brief review of its origin and present

Some applications of service robots within domestic and working environments are envisaged to be a significant part of our lives in the not too distant future. They are developed to autonomously accomplish different tasks either on behalf of or in collaboration with a human being. Robots can perceive and interpret data from the external environment, so they also collect personal information and habits; they can plan, navigate, and manipulate objects, eventually intruding in our personal space and disturbing us in the current activities. Indeed, such capabilities need to be socially enhanced to ensure their effective deployment and to favour a significant social impact. The modelling and evaluation of a service robot’s behaviour, while not interacting with a human, have only been marginally considered in the last few years. But these can be expected to play a key role in developing socially acceptable robotic applications that can be used widely. To explore this research direction, we present research objectives related to the effective development of socially-aware service robots that are not involved in tasks that require explicit interaction with a person. Such discussion aims at highlighting some of the future challenges that will be posed for the social robotics community in the next years.

The Secret Life of Robots: Perspectives and Challenges for Robot’s Behaviours During Non-interactive Tasks

Advertising income depends on the amount of clicks by users of websites and mobile applications. However, the emergence of click fraud greatly reduces the real benefits of the advertisement. Most existing researches focus on detecting click fraud by analyzing properties and patterns of click data streams, but attackers can construct data that looks legitimate by replaying former data streams. In this paper, we propose a novel system called ClickGuard to detect click fraud attacks. ClickGuard takes advantage of motion sensor signals from mobile devices, since the pattern of motion signals is completely different under real click events and fraud events. To prevent attackers from bypassing the system by faking the time-domain statistical characteristics of original signals, we introduce the MFCC algorithm in feature extraction phase. MFCC algorithm can extract frequency-domain features of original signals in specific frequency bands which are hardly constructed out of thin air. Classifiers are finally constructed using these features and several machine learning algorithms. Experiments show that ClickGuard can achieve the accuracy of 96.71% in general environment and 84.16% when attackers modify the time-domain statistical characteristics of raw data.

ClickGuard: Exposing Hidden Click Fraud via Mobile Sensor Side-channel Analysis

Advertising is a primary means for revenue generation for millions of websites and smartphone apps. Naturally, a fraction abuse ad networks to systematically defraud advertisers of their money. Modern defences have matured to overcome some forms of click fraud but measurement studies have reported that a third of clicks supplied by ad networks could be clickspam. Our work develops novel inference techniques which can isolate click fraud attacks using their fundamental properties. We propose two defences, mimicry and bait-click, which provide clickspam detection with substantially improved results over current approaches. Mimicry leverages the observation that organic clickfraud involves the reuse of legitimate click traffic, and thus isolates clickspam by detecting patterns of click reuse within ad network clickstreams. The bait-click defence leverages the vantage point of an ad network to inject a pattern of bait clicks into a user's device. Any organic clickspam generated involving the bait clicks will be subsequently recognisable by the ad network. Our experiments show that the mimicry defence detects around 81% of fake clicks in stealthy (low rate) attacks, with a false-positive rate of 110 per hundred thousand clicks. Similarly, the bait-click defence enables further improvements in detection, with rates of 95% and a reduction in false-positive rates of between 0 and 30 clicks per million - a substantial improvement over current approaches.

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Clicktok: click fraud detection using traffic analysis

Advertising is a primary means for revenue generation for millions of websites and smartphone apps (publishers). Naturally, a fraction of publishers abuse the ad-network to systematically defraud advertisers of their money. Defenses have matured to overcome some forms of click fraud but are inadequate against the threat of organic click fraud attacks. Malware detection systems including honeypots fail to stop click fraud apps; ad-network filters are better but measurement studies have reported that a third of the clicks supplied by ad-networks are fake; collaborations between ad-networks and app stores that bad-lists malicious apps works better still, but fails to prevent criminals from writing fraudulent apps which they monetise until they get banned and start over again. This work develops novel inference techniques that can isolate click fraud attacks using their fundamental properties. In the {\em mimicry defence}, we leverage the observation that organic click fraud involves the re-use of legitimate clicks. Thus we can isolate fake-clicks by detecting patterns of click-reuse within ad-network clickstreams with historical behaviour serving as a baseline. Second, in {\em bait-click defence}. we leverage the vantage point of an ad-network to inject a pattern of bait clicks into the user's device, to trigger click fraud-apps that are gated on user-behaviour. Our experiments show that the mimicry defence detects around 81\% of fake-clicks in stealthy (low rate) attacks with a false-positive rate of 110110 per hundred thousand clicks. Bait-click defence enables further improvements in detection rates of 95\% and reduction in false-positive rates of between 0 and 30 clicks per million, a substantial improvement over current approaches.

Clicktok: Click Fraud Detection using Traffic Analysis

The use of connected surgical robotics to automate medical procedures presents new privacy challenges. We argue that conventional patient consent protocols no longer work. Indeed robots that replace human surgeons take on an extraordinary level of responsibility. Surgeons undergo years of training and peer review in a strongly regulated environment, and derive trust via a patient's faith in the hospital system. Robots on the other hand derive trust differently, via the integrity of the software that governs their operation. From a privacy perspective, there are two fundamental shifts. First, the threat model has shifted from one where the humans involved were untrusted to one where the robotic software is untrusted. Second, the basic unit of privacy control is no longer a medical record, but is replaced by four new basic units: the subject on which the robot is taking action; the tools used by the robot; the sensors (i.e data) the robot can access; and, finally access to monitoring and calibration services which afford correct operation of the robot. We suggest that contextual privacy provides useful theoretical tools to solve the privacy problems posed by surgical robots. However, it also poses some challenges: not least that the complexity of the contextual-privacy policies, if rigorously specified to achieve verification and enforceability, will be exceedingly high to directly expose to humans that review contextual privacy policies. A medical robot works with both information and physical material. While informational norms allow for judgements about contextual integrity and the transmission principle governs the constraints applied on information transfer, nothing is said about material property. Certainly, contextual privacy provides an anchor for useful notions of privacy in this scenario and thus should be considered to be extended to cover both information and material flows.

Privacy with Surgical Robotics: Challenges in Applying Contextual Privacy Theory

Traceable sensor calibration constitutes a foundational step that underpins operational safety in the Industrial Internet of Things. Traceability is the property that ensures reliability of sensed data by ensuring sensor accuracy is within a small error margin of a highly-accurate reference sensor. This is typically achieved via a calibration infrastructure involving a long chain of reference-calibration devices between the master reference and the IoT sensor. While much attention has been given to IoT security such as the use of TLS to secure sensed data, little thought has been given to securing the calibration infrastructure itself. Currently traceability is achieved via manual verification using paper-based datasheets which is both time consuming and insecure. For instance, when the calibration status of parent devices is revoked as mistakes or mischance is detected, calibrated devices are not updated until the next calibration cycle, leaving much of the calibration parameters invalid. Aside from error, any party within the calibration infrastructure can maliciously introduce errors since the current paper based system lacks authentication as well as non-repudiation. In this paper, we propose a novel resilient architecture for calibration infrastructure, where the calibration status of sensor elements can be verified on-the-fly to the root of trust preserving the properties of authentication and non-repudiation. We propose an implementation based on smart contracts on the Ethereum network. Our evaluation shows that Ethereum is likely to address the protection requirements of traceable measurements.

Secure calibration in high-assurance IoT: traceability for safety resilience

We develop a novel technique to determine call provenance in anonymous VoIP communications using acoustic side-channels. The technique exploits location-attributable information embedded within audio speech data. The victim’s speech is exploited as an excitation signal, which is modulated (acted upon) by the acoustic reflection characteristics of the victim’s location. We show that leading VoIP communication channels faithfully transfer this information between sender-receiver pairs, enabling passive receivers to extract a location fingerprint, to establish call provenance. To establish provenance, a fingerprint is compared against a database of labelled fingerprints to identify a match. The technique is fully passive and does not depend on any characteristic background sounds, is speaker independent, and is robust to lossy network conditions. Evaluation using a corpus of recordings of VoIP conversations, over the Tor network, confirms that recording locations can be fingerprinted and detected remotely with low false-positive rate.

Ryan Shah

Papers

Clicktok: click fraud detection using traffic analysis

Clicktok: Click Fraud Detection using Traffic Analysis

Privacy with Surgical Robotics: Challenges in Applying Contextual Privacy Theory

Secure calibration in high-assurance IoT: traceability for safety resilience

VoipLoc: VoIP call provenance using acoustic side-channels