arXiv subjects

Bogdan Carbunar

Publications and source records attributed to Bogdan Carbunar.

24 records · Page 2Linked to original sources

arXiv2017

Secure Management of Low Power Fitness Trackers

The increasing popular interest in personal telemetry, also called the Quantified Self or "lifelogging", has induced a popularity surge for wearable personal fitness trackers. Fitness trackers automatically collect sensor data about the user throughout the day, and integrate it into social network accounts. Solution providers have to strike a balance between many constraints, leading to a design process that often puts security in the back seat. Case in point, we reverse engineered and identified security vulnerabilities in Fitbit Ultra and Gammon Forerunner 610, two popular and representative fitness tracker products. We introduce FitBite and GarMax, tools to launch efficient attacks against Fitbit and Garmin. We devise SensCrypt, a protocol for secure data storage and communication, for use by makers of affordable and lightweight personal trackers. SensCrypt thwarts not only the attacks we introduced, but also defends against powerful JTAG Read attacks. We have built Sens.io, an Arduino Uno based tracker platform, of similar capabilities but at a fraction of the cost of current solutions. On Sens.io, SensCrypt imposes a negligible write overhead and significantly reduces the end-to-end sync overhead of Fitbit and Garmin.

cs.CR↗

arXiv2017

Hardening Stratum, the Bitcoin Pool Mining Protocol

Stratum, the de-facto mining communication protocol used by blockchain based cryptocurrency systems, enables miners to reliably and efficiently fetch jobs from mining pool servers. In this paper we exploit Stratum's lack of encryption to develop passive and active attacks on Bitcoin's mining protocol, with important implications on the privacy, security and even safety of mining equipment owners. We introduce StraTap and ISP Log attacks, that infer miner earnings if given access to miner communications, or even their logs. We develop BiteCoin, an active attack that hijacks shares submitted by miners, and their associated payouts. We build BiteCoin on WireGhost, a tool we developed to hijack and surreptitiously maintain Stratum connections. Our attacks reveal that securing Stratum through pervasive encryption is not only undesirable (due to large overheads), but also ineffective: an adversary can predict miner earnings even when given access to only packet timestamps. Instead, we devise Bedrock, a minimalistic Stratum extension that protects the privacy and security of mining participants. We introduce and leverage the mining cookie concept, a secret that each miner shares with the pool and includes in its puzzle computations, and that prevents attackers from reconstructing or hijacking the puzzles. We have implemented our attacks and collected 138MB of Stratum protocol traffic from mining equipment in the US and Venezuela. We show that Bedrock is resilient to active attacks even when an adversary breaks the crypto constructs it uses. Bedrock imposes a daily overhead of 12.03s on a single pool server that handles mining traffic from 16,000 miners.

cs.CR↗

arXiv2017

FairPlay: Fraud and Malware Detection in Google Play

Fraudulent behaviors in Google Android app market fuel search rank abuse and malware proliferation. We present FairPlay, a novel system that uncovers both malware and search rank fraud apps, by picking out trails that fraudsters leave behind. To identify suspicious apps, FairPlay PCF algorithm correlates review activities and uniquely combines detected review relations with linguistic and behavioral signals gleaned from longitudinal Google Play app data. We contribute a new longitudinal app dataset to the community, which consists of over 87K apps, 2.9M reviews, and 2.4M reviewers, collected over half a year. FairPlay achieves over 95% accuracy in classifying gold standard datasets of malware, fraudulent and legitimate apps. We show that 75% of the identified malware apps engage in search rank fraud. FairPlay discovers hundreds of fraudulent apps that currently evade Google Bouncer detection technology, and reveals a new type of attack campaign, where users are harassed into writing positive reviews, and install and review other apps.

cs.SI↗

arXiv2015

Spotting Suspicious Reviews via (Quasi-)clique Extraction

How to tell if a review is real or fake? What does the underworld of fraudulent reviewing look like? Detecting suspicious reviews has become a major issue for many online services. We propose the use of a clique-finding approach to discover well-organized suspicious reviewers. From a Yelp dataset with over one million reviews, we construct multiple Reviewer Similarity graphs to link users that have unusually similar behavior: two reviewers are connected in the graph if they have reviewed the same set of venues within a few days. From these graphs, our algorithms extracted many large cliques and quasi-cliques, the largest one containing a striking 11 users who coordinated their review activities in identical ways. Among the detected cliques, a large portion contain Yelp Scouts who are paid by Yelp to review venues in new areas. Our work sheds light on their little-known operation.

cs.SI↗

arXiv2013

Fit and Vulnerable: Attacks and Defenses for a Health Monitoring Device

The fusion of social networks and wearable sensors is becoming increasingly popular, with systems like Fitbit automating the process of reporting and sharing user fitness data. In this paper we show that while compelling, the integration of health data into social networks is fraught with privacy and security vulnerabilities. Case in point, by reverse engineering the communication protocol, storage details and operation codes, we identified several vulnerabilities in Fitbit. We have built FitBite, a suite of tools that exploit these vulnerabilities to launch a wide range of attacks against Fitbit. Besides eavesdropping, injection and denial of service, several attacks can lead to rewards and financial gains. We have built FitLock, a lightweight defense system that protects Fitbit while imposing only a small overhead. Our experiments on BeagleBoard and Xperia devices show that FitLock's end-to-end overhead over Fitbit is only 2.4%.

cs.CR↗

arXiv2013

Eat the Cake and Have It Too: Privacy Preserving Location Aggregates in Geosocial Networks

Geosocial networks are online social networks centered on the locations of subscribers and businesses. Providing input to targeted advertising, profiling social network users becomes an important source of revenue. Its natural reliance on personal information introduces a trade-off between user privacy and incentives of participation for businesses and geosocial network providers. In this paper we introduce location centric profiles (LCPs), aggregates built over the profiles of users present at a given location. We introduce PROFILR, a suite of mechanisms that construct LCPs in a private and correct manner. We introduce iSafe, a novel, context aware public safety application built on PROFILR . Our Android and browser plugin implementations show that PROFILR is efficient: the end-to-end overhead is small even under strong correctness assurances.

cs.CR↗