Android Application for Password less Login to Web Application

Passwordless Login for Web Application? is an android application, will be used to access online internet accounts of distinct web applications and web services. The user would register with pre-requisite credentials primarily like, an email id, primary mobile number, and a unique username and a secondary mobile number. After successful registration, the user would be required to enter its registered username only. After submitting the registered username, a unique QR code will be popped up on the website. The user would scan the QR code using his/her android mobile phone. After successful scanning of QR code, the authentication and authorization procedure will be performed thereby granting secure access

Control What You Include! Server-Side Protection against Third Party Web Tracking

Third party tracking is the practice by which third parties recognize users accross different websites as they browse the web. Recent studies show that 90% of websites contain third party content that is tracking its users across the web. Website developers often need to include third party content in order to provide basic functionality. However, when a developer includes a third party content, she cannot know whether the third party contains tracking mechanisms. If a website developer wants to protect her users from being tracked, the only solution is to exclude any third-party content, thus trading functionality for privacy. We describe and implement a privacy-preserving web architecture that gives website developers a control over third party tracking: developers are able to include functionally useful third party content, the same time ensuring that the end users are not tracked by the third parties

PALPAS - PAsswordLess PAssword Synchronization

Tools that synchronize passwords over several user devices typically store the encrypted passwords in a central online database. For encryption, a low-entropy, password-based key is used. Such a database may be subject to unauthorized access which can lead to the disclosure of all passwords by an offline brute-force attack. In this paper, we present PALPAS, a secure and user-friendly tool that synchronizes passwords between user devices without storing information about them centrally. The idea of PALPAS is to generate a password from a high entropy secret shared by all devices and a random salt value for each service. Only the salt values are stored on a server but not the secret. The salt enables the user devices to generate the same password but is statistically independent of the password. In order for PALPAS to generate passwords according to different password policies, we also present a mechanism that automatically retrieves and processes the password requirements of services. PALPAS users need to only memorize a single password and the setup of PALPAS on a further device demands only a one-time transfer of few static data.Comment: An extended abstract of this work appears in the proceedings of ARES 201

Analysing the Security of Google's implementation of OpenID Connect

Many millions of users routinely use their Google accounts to log in to relying party (RP) websites supporting the Google OpenID Connect service. OpenID Connect, a newly standardised single-sign-on protocol, builds an identity layer on top of the OAuth 2.0 protocol, which has itself been widely adopted to support identity management services. It adds identity management functionality to the OAuth 2.0 system and allows an RP to obtain assurances regarding the authenticity of an end user. A number of authors have analysed the security of the OAuth 2.0 protocol, but whether OpenID Connect is secure in practice remains an open question. We report on a large-scale practical study of Google's implementation of OpenID Connect, involving forensic examination of 103 RP websites which support its use for sign-in. Our study reveals serious vulnerabilities of a number of types, all of which allow an attacker to log in to an RP website as a victim user. Further examination suggests that these vulnerabilities are caused by a combination of Google's design of its OpenID Connect service and RP developers making design decisions which sacrifice security for simplicity of implementation. We also give practical recommendations for both RPs and OPs to help improve the security of real world OpenID Connect systems

Your Smart Home Can't Keep a Secret: Towards Automated Fingerprinting of IoT Traffic with Neural Networks

The IoT (Internet of Things) technology has been widely adopted in recent years and has profoundly changed the people's daily lives. However, in the meantime, such a fast-growing technology has also introduced new privacy issues, which need to be better understood and measured. In this work, we look into how private information can be leaked from network traffic generated in the smart home network. Although researchers have proposed techniques to infer IoT device types or user behaviors under clean experiment setup, the effectiveness of such approaches become questionable in the complex but realistic network environment, where common techniques like Network Address and Port Translation (NAPT) and Virtual Private Network (VPN) are enabled. Traffic analysis using traditional methods (e.g., through classical machine-learning models) is much less effective under those settings, as the features picked manually are not distinctive any more. In this work, we propose a traffic analysis framework based on sequence-learning techniques like LSTM and leveraged the temporal relations between packets for the attack of device identification. We evaluated it under different environment settings (e.g., pure-IoT and noisy environment with multiple non-IoT devices). The results showed our framework was able to differentiate device types with a high accuracy. This result suggests IoT network communications pose prominent challenges to users' privacy, even when they are protected by encryption and morphed by the network gateway. As such, new privacy protection methods on IoT traffic need to be developed towards mitigating this new issue

Undermining User Privacy on Mobile Devices Using AI

Over the past years, literature has shown that attacks exploiting the microarchitecture of modern processors pose a serious threat to the privacy of mobile phone users. This is because applications leave distinct footprints in the processor, which can be used by malware to infer user activities. In this work, we show that these inference attacks are considerably more practical when combined with advanced AI techniques. In particular, we focus on profiling the activity in the last-level cache (LLC) of ARM processors. We employ a simple Prime+Probe based monitoring technique to obtain cache traces, which we classify with Deep Learning methods including Convolutional Neural Networks. We demonstrate our approach on an off-the-shelf Android phone by launching a successful attack from an unprivileged, zeropermission App in well under a minute. The App thereby detects running applications with an accuracy of 98% and reveals opened websites and streaming videos by monitoring the LLC for at most 6 seconds. This is possible, since Deep Learning compensates measurement disturbances stemming from the inherently noisy LLC monitoring and unfavorable cache characteristics such as random line replacement policies. In summary, our results show that thanks to advanced AI techniques, inference attacks are becoming alarmingly easy to implement and execute in practice. This once more calls for countermeasures that confine microarchitectural leakage and protect mobile phone applications, especially those valuing the privacy of their users