Poisoning Attacks on Learning-Based Keystroke Authentication and a Residue Feature Based Defense

Behavioral biometrics, such as keystroke dynamics, are characterized by relatively large variation in the input samples as compared to physiological biometrics such as fingerprints and iris. Recent advances in machine learning have resulted in behaviorbased pattern learning methods that obviate the effects of variation by mapping the variable behavior patterns to a unique identity with high accuracy. However, it has also exposed the learning systems to attacks that use updating mechanisms in learning by injecting imposter samples to deliberately drift the data to impostors’ patterns. Using the principles of adversarial drift, we develop a class of poisoning attacks, named Frog-Boiling attacks. The update samples are crafted with slow changes and random perturbations so that they can bypass the classifiers detection. Taking the case of keystroke dynamics which includes motoric and neurological learning, we demonstrate the success of our attack mechanism. We also present a detection mechanism for the frog-boiling attack that uses correlation between successive training samples to detect spurious input patterns. To measure the effect of adversarial drift in frog-boiling attack and the effectiveness of the proposed defense mechanism, we use traditional error rates such as FAR, FRR, and EER and the metric in terms of shifts in biometric menagerie

Efficient Anonymous Biometric Matching in Privacy-Aware Environments

Video surveillance is an important tool used in security and environmental monitoring, however, the widespread deployment of surveillance cameras has raised serious privacy concerns. Many privacy-enhancing schemes have been recently proposed to automatically redact images of selected individuals in the surveillance video for protection. To identify these individuals for protection, the most reliable approach is to use biometric signals as they are immutable and highly discriminative. If misused, these characteristics of biometrics can seriously defeat the goal of privacy protection. In this dissertation, an Anonymous Biometric Access Control (ABAC) procedure is proposed based on biometric signals for privacy-aware video surveillance. The ABAC procedure uses Secure Multi-party Computational (SMC) based protocols to verify membership of an incoming individual without knowing his/her true identity. To make SMC-based protocols scalable to large biometric databases, I introduce the k-Anonymous Quantization (kAQ) framework to provide an effective and secure tradeoff of privacy and complexity. kAQ limits systems knowledge of the incoming individual to k maximally dissimilar candidates in the database, where k is a design parameter that controls the amount of complexity-privacy tradeoff. The relationship between biometric similarity and privacy is experimentally validated using a twin iris database. The effectiveness of the entire system is demonstrated based on a public iris biometric database. To provide the protected subjects with full access to their privacy information in video surveillance system, I develop a novel privacy information management system that allows subjects to access their information via the same biometric signals used for ABAC. The system is composed of two encrypted-domain protocols: the privacy information encryption protocol encrypts the original video records using the iris pattern acquired during ABAC procedure; the privacy information retrieval protocol allows the video records to be anonymously retrieved through a GC-based iris pattern matching process. Experimental results on a public iris biometric database demonstrate the validity of my framework

Reconceptualizing Privacy: An Examination Of The Developing Regulatory Regime For Facial Recognition Technology

ABSTRACT The National Telecommunications and Information Administration have convened a series of meetings to create a voluntary code of conduct for the commercial use of facial recognition technology. This research asks and answers three questions related to the creation of the voluntary code of conduct: 1) How is the regulatory regime of FRT emerging in the U.S.? 2) What are the roles of the various stakeholders in shaping the commercial regulation of FRT? 3) How does FRT challenge our current conceptions of privacy? Data has been gathered to answer these questions using participant observation and semi-structured interviews. The data was analyzed via mediated discourse analysis. Findings of the research include: the highly sensitive nature of the biometric data that facial recognition technology collects, the data’s ability to be linked across multiple databases, the surreptitious way the data can be collected, the potential chilling effect the technology can have on the First Amendment, and the various threats the technology poses to privacy. Keywords: Privacy, Facial Recognition Technology, Multistakeholder, and Biometric Dat

HMOG: New Behavioral Biometric Features for Continuous Authentication of Smartphone Users

We introduce hand movement, orientation, and grasp (HMOG), a set of behavioral features to continuously authenticate smartphone users. HMOG features unobtrusively capture subtle micro-movement and orientation dynamics resulting from how a user grasps, holds, and taps on the smartphone. We evaluated authentication and biometric key generation (BKG) performance of HMOG features on data collected from 100 subjects typing on a virtual keyboard. Data were collected under two conditions: 1) sitting and 2) walking. We achieved authentication equal error rates (EERs) as low as 7.16% (walking) and 10.05% (sitting) when we combined HMOG, tap, and keystroke features. We performed experiments to investigate why HMOG features perform well during walking. Our results suggest that this is due to the ability of HMOG features to capture distinctive body movements caused by walking, in addition to the hand-movement dynamics from taps. With BKG, we achieved the EERs of 15.1% using HMOG combined with taps. In comparison, BKG using tap, key hold, and swipe features had EERs between 25.7% and 34.2%. We also analyzed the energy consumption of HMOG feature extraction and computation. Our analysis shows that HMOG features extracted at a 16-Hz sensor sampling rate incurred a minor overhead of 7.9% without sacrificing authentication accuracy. Two points distinguish our work from current literature: 1) we present the results of a comprehensive evaluation of three types of features (HMOG, keystroke, and tap) and their combinations under the same experimental conditions and 2) we analyze the features from three perspectives (authentication, BKG, and energy consumption on smartphones)

Implementing Information Security and Its Technology: A LineManagement Perspective

Assuring the security and privacy of institutionalinformation assets is a complex task for the line manager responsible forinternational and multi-national transactions. In the face of an unsureand often conflicting international legal framework, the line managermust employ all available tools in an Integrated Security and PrivacyManagement framework that ranges from legal obligations, to policy, toprocedure, to cutting edge technology to counter the rapidly evolvingcyber threat to information assets and the physical systems thatinformation systems control

On Security and Privacy for Networked Information Society : Observations and Solutions for Security Engineering and Trust Building in Advanced Societal Processes

Our society has developed into a networked information society, in which all aspects of human life are interconnected via the Internet — the backbone through which a significant part of communications traffic is routed. This makes the Internet arguably the most important piece of critical infrastructure in the world. Securing Internet communications for everyone using it is extremely important, as the continuing growth of the networked information society relies upon fast, reliable and secure communications. A prominent threat to the security and privacy of Internet users is mass surveillance of Internet communications. The methods and tools used to implement mass surveillance capabilities on the Internet pose a danger to the security of all communications, not just the intended targets. When we continue to further build the networked information upon the unreliable foundation of the Internet we encounter increasingly complex problems,which are the main focus of this dissertation. As the reliance on communication technology grows in a society, so does the importance of information security. At this stage, information security issues become separated from the purely technological domain and begin to affect everyone in society. The approach taken in this thesis is therefore both technical and socio-technical. The research presented in this PhD thesis builds security in to the networked information society and provides parameters for further development of a safe and secure networked information society. This is achieved by proposing improvements on a multitude of layers. In the technical domain we present an efficient design flow for secure embedded devices that use cryptographic primitives in a resource-constrained environment, examine and analyze threats to biometric passport and electronic voting systems, observe techniques used to conduct mass Internet surveillance, and analyze the security of Finnish web user passwords. In the socio-technical domain we examine surveillance and how it affects the citizens of a networked information society, study methods for delivering efficient security education, examine what is essential security knowledge for citizens, advocate mastery over surveillance data by the targeted citizens in the networked information society, and examine the concept of forced trust that permeates all topics examined in this work.Yhteiskunta, jossa elämme, on muovautunut teknologian kehityksen myötä todelliseksi tietoyhteiskunnaksi. Monet verkottuneen tietoyhteiskunnan osa-alueet ovat kokeneet muutoksen tämän kehityksen seurauksena. Tämän muutoksen keskiössä on Internet: maailmanlaajuinen tietoverkko, joka mahdollistaa verkottuneiden laitteiden keskenäisen viestinnän ennennäkemättömässä mittakaavassa. Internet on muovautunut ehkä keskeisimmäksi osaksi globaalia viestintäinfrastruktuuria, ja siksi myös globaalin viestinnän turvaaminen korostuu tulevaisuudessa yhä enemmän. Verkottuneen tietoyhteiskunnan kasvu ja kehitys edellyttävät vakaan, turvallisen ja nopean viestintäjärjestelmän olemassaoloa. Laajamittainen tietoverkkojen joukkovalvonta muodostaa merkittävän uhan tämän järjestelmän vakaudelle ja turvallisuudelle. Verkkovalvonnan toteuttamiseen käytetyt menetelmät ja työkalut eivät vain anna mahdollisuutta tarkastella valvonnan kohteena olevaa viestiliikennettä, vaan myös vaarantavat kaiken Internet-liikenteen ja siitä riippuvaisen toiminnan turvallisuuden. Kun verkottunutta tietoyhteiskuntaa rakennetaan tämän kaltaisia valuvikoja ja haavoittuvuuksia sisältävän järjestelmän varaan, keskeinen uhkatekijä on, että yhteiskunnan ydintoiminnot ovat alttiina ulkopuoliselle vaikuttamiselle. Näiden uhkatekijöiden ja niiden taustalla vaikuttavien mekanismien tarkastelu on tämän väitöskirjatyön keskiössä. Koska työssä on teknisen sisällön lisäksi vahva yhteiskunnallinen elementti, tarkastellaan tiukan teknisen tarkastelun sijaan aihepiirä laajemmin myös yhteiskunnallisesta näkökulmasta. Tässä väitöskirjassa pyritään rakentamaan kokonaiskuvaa verkottuneen tietoyhteiskunnan turvallisuuteen, toimintaan ja vakauteen vaikuttavista tekijöistä, sekä tuomaan esiin uusia ratkaisuja ja avauksia eri näkökulmista. Työn tavoitteena on osaltaan mahdollistaa entistä turvallisemman verkottuneen tietoyhteiskunnan rakentaminen tulevaisuudessa. Teknisestä näkökulmasta työssä esitetään suunnitteluvuo kryptografisia primitiivejä tehokkaasti hyödyntäville rajallisen laskentatehon sulautetuviiille järjestelmille, analysoidaan biometrisiin passeihin, kansainväliseen passijärjestelmään, sekä sähköiseen äänestykseen kohdistuvia uhkia, tarkastellaan joukkovalvontaan käytettyjen tekniikoiden toimintaperiaatteita ja niiden aiheuttamia uhkia, sekä tutkitaan suomalaisten Internet-käyttäjien salasanatottumuksia verkkosovelluksissa. Teknis-yhteiskunnallisesta näkökulmasta työssä tarkastellaan valvonnan teoriaa ja perehdytään siihen, miten valvonta vaikuttaa verkottuneen tietoyhteiskunnan kansalaisiin. Lisäksi kehitetään menetelmiä parempaan tietoturvaopetukseen kaikilla koulutusasteilla, määritellään keskeiset tietoturvatietouden käsitteet, tarkastellaan mahdollisuutta soveltaa tiedon herruuden periaatetta verkottuneen tietoyhteiskunnan kansalaisistaan keräämän tiedon hallintaan ja käyttöön, sekä tutkitaan luottamuksen merkitystä yhteiskunnan ydintoimintojen turvallisuudelle ja toiminnalle, keskittyen erityisesti pakotetun luottamuksen vaikutuksiin

Privacy-aware Security Applications in the Era of Internet of Things

In this dissertation, we introduce several novel privacy-aware security applications. We split these contributions into three main categories: First, to strengthen the current authentication mechanisms, we designed two novel privacy-aware alternative complementary authentication mechanisms, Continuous Authentication (CA) and Multi-factor Authentication (MFA). Our first system is Wearable-assisted Continuous Authentication (WACA), where we used the sensor data collected from a wrist-worn device to authenticate users continuously. Then, we improved WACA by integrating a noise-tolerant template matching technique called NTT-Sec to make it privacy-aware as the collected data can be sensitive. We also designed a novel, lightweight, Privacy-aware Continuous Authentication (PACA) protocol. PACA is easily applicable to other biometric authentication mechanisms when feature vectors are represented as fixed-length real-valued vectors. In addition to CA, we also introduced a privacy-aware multi-factor authentication method, called PINTA. In PINTA, we used fuzzy hashing and homomorphic encryption mechanisms to protect the users\u27 sensitive profiles while providing privacy-preserving authentication. For the second privacy-aware contribution, we designed a multi-stage privacy attack to smart home users using the wireless network traffic generated during the communication of the devices. The attack works even on the encrypted data as it is only using the metadata of the network traffic. Moreover, we also designed a novel solution based on the generation of spoofed traffic. Finally, we introduced two privacy-aware secure data exchange mechanisms, which allow sharing the data between multiple parties (e.g., companies, hospitals) while preserving the privacy of the individual in the dataset. These mechanisms were realized with the combination of Secure Multiparty Computation (SMC) and Differential Privacy (DP) techniques. In addition, we designed a policy language, called Curie Policy Language (CPL), to handle the conflicting relationships among parties. The novel methods, attacks, and countermeasures in this dissertation were verified with theoretical analysis and extensive experiments with real devices and users. We believe that the research in this dissertation has far-reaching implications on privacy-aware alternative complementary authentication methods, smart home user privacy research, as well as the privacy-aware and secure data exchange methods

Security Evaluation of Support Vector Machines in Adversarial Environments

Support Vector Machines (SVMs) are among the most popular classification techniques adopted in security applications like malware detection, intrusion detection, and spam filtering. However, if SVMs are to be incorporated in real-world security systems, they must be able to cope with attack patterns that can either mislead the learning algorithm (poisoning), evade detection (evasion), or gain information about their internal parameters (privacy breaches). The main contributions of this chapter are twofold. First, we introduce a formal general framework for the empirical evaluation of the security of machine-learning systems. Second, according to our framework, we demonstrate the feasibility of evasion, poisoning and privacy attacks against SVMs in real-world security problems. For each attack technique, we evaluate its impact and discuss whether (and how) it can be countered through an adversary-aware design of SVMs. Our experiments are easily reproducible thanks to open-source code that we have made available, together with all the employed datasets, on a public repository.Comment: 47 pages, 9 figures; chapter accepted into book 'Support Vector Machine Applications