PapersCut A shortcut to recent security papers

Human Cognition through the Lens of Social Engineering Cyberattacks

Authors: Rosana Montanez Rodriguez, Edward Golog, Shouhuai Xu

Abstract: Social engineering cyberattacks are a major threat because they often prelude sophisticated and devastating cyberattacks. Social engineering cyberattacks are a kind of psychological attack that exploits weaknesses in human cognitive functions. Adequate defense against social engineering cyberattacks requires a deeper understanding of what aspects of human cognition are exploited by these cyberattacks, why humans are susceptible to these cyberattacks, and how we can minimize or at least mitigate their damage. These questions have received some amount of attention but the state-of-the-art understanding is superficial and scattered in the literature. In this paper, we review human cognition through the lens of social engineering cyberattacks. Then, we propose an extended framework of human cognitive functions to accommodate social engineering cyberattacks. We cast existing studies on various aspects of social engineering cyberattacks into the extended framework, while drawing a number of insights that represent the current understanding and shed light on future research directions. The extended framework might inspire future research endeavors towards a new sub-field that can be called Cybersecurity Cognitive Psychology, which tailors or adapts principles of Cognitive Psychology to the cybersecurity domain while embracing new notions and concepts that are unique to the cybersecurity domain.

Date: 9 Jul 2020

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Human-Computer Interaction Considerations When Developing Cyber Ranges

Authors: Lynsay A. Shepherd, Stefano De Paoli, Jim Conacher

Abstract: The number of cyber-attacks are continuing to rise globally. It is therefore vital for organisations to develop the necessary skills to secure their assets and to protect critical national infrastructure. In this short paper, we outline upon human-computer interaction elements which should be considered when developing a cybersecurity training platform, in an effort to maintain levels of user engagement. We provide an overview of existing training platforms before covering specialist cyber ranges. Aspects of human-computer interaction are noted with regards to their relevance in the context of cyber ranges. We conclude with design suggestions when developing a cyber range platform.

Comment: 5 pages, short discussion paper

Date: 9 Jul 2020

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The Road Not Taken: Re-thinking the Feasibility of Voice Calling Over Tor

Authors: Piyush Kumar Sharma, Shashwat Chaudhary, Nikhil Hassija, Mukulika Maity, Sambuddho Chakravarty

Abstract: Anonymous VoIP calls over the Internet holds great significance for privacy-conscious users, whistle-blowers and political activists alike. Prior research deems popular anonymization systems like Tor unsuitable for providing requisite performance guarantees that real-time applications like VoIP need. Their claims are backed by studies that may no longer be valid due to constant advancements in Tor. Moreover, we believe that these studies lacked the requisite diversity and comprehensiveness. Thus, conclusions from these studies led them to propose novel and tailored solutions. However, no such system is available for immediate use. Additionally, operating such new systems would incur significant costs for recruiting users and volunteered relays, to provide the necessary anonymity guarantees. It thus becomes imperative that the exact performance of VoIP over Tor be quantified and analyzed so that the potential performance bottlenecks can be amended. We thus conducted an extensive empirical study across various in-lab and real-world scenarios to shed light on VoIP performance over Tor. In over 0.5 million measurements spanning 12 months, across seven countries and covering about 6650 Tor relays, we observed that Tor supports good voice quality (Perceptual Evaluation of Speech Quality (PESQ) >3 and oneway delay <400ms) in more than 85% of cases. Further analysis indicates that in general for most Tor relays, the contentions due to cross-traffic were low enough to support VoIP calls, that are anyways transmitted at low rates (<120 Kbps). Our findings are supported by concordant measurements using iperf that show more than the adequate available bandwidth for most cases. Data published by the Tor Metrics also corroborates the same. Hence, unlike prior efforts, our research reveals that Tor is suitable for supporting anonymous VoIP calls.

Date: 9 Jul 2020

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Serverless Electronic Mail

Authors: Geoffrey Goodell

Abstract: We describe a simple approach to peer-to-peer electronic mail that would allow users of ordinary workstations and mobile devices to exchange messages without relying upon third-party mail server operators. Crucially, the system allows participants to establish and use multiple unlinked identities for communication with each other. The architecture leverages ordinary SMTP \cite{smtp} for message delivery and Tor \cite{tor} for peer-to-peer communication. The design offers a robust, unintrusive method to use self-certifying Tor onion service names to bootstrap a web of trust based on public keys for end-to-end authentication and encryption, which in turn can be used to facilitate message delivery when the sender and recipient are not online simultaneously. We show how the system can interoperate with existing email systems and paradigms, allowing users to hold messages that others can retrieve via IMAP \cite{imap} or to operate as a relay between system participants and external email users. Finally, we show how it is possible to use a broadcast protocol to implement mailing lists and how distributed ledger technology might be used to bootstrap consensus about shared knowledge among list members.

Comment: 9 pages, 8 figures

Date: 9 Jul 2020

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A Secure Back-up and Restore for Resource-Constrained IoT based on Nanotechnology

Authors: Mesbah Uddin, Md. Badruddoja Majumder, Md. Sakib Hasan, Garrett S. Rose

Abstract: With the emergence of IoT (Internet of things), huge amounts of sensitive data are being processed and transmitted everyday in edge devices with little to no security. Due to their aggressive power management schemes, it is a common and necessary technique to make a back-up of their program states and other necessary data in a non-volatile memory (NVM) before going to sleep or low power mode. However, this memory is often left unprotected as adding robust security measures tends to be expensive for these resource constrained systems. In this paper, we propose a lightweight security system for NVM during low power mode. This security architecture uses the memristor, an emerging nanoscale device which is used to build hardware security primitives like PUF (physical unclonable function) based encryption-decryption, true random number generators (TRNG), and memory integrity checking. A reliability enhancement technique for this PUF is also proposed which shows how this system would work even with less-than-100\% reliable PUF responses. Together, with all these techniques, we have established a dual layer security protocol (data encryption+integrity check) which provides reasonable security to an embedded processor while being very lightweight in terms of area, power, and computation time. A complete system design is demonstrated with 65$n$m CMOS and emerging memristive technology. With this, we have provided a detailed and accurate estimation of resource overhead. Analysis of the security of the whole system is also provided.

Comment: Content: 17 pages with 15 figures and 7 tables Submitted to IEEE IoT Journal

Date: 9 Jul 2020

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Artificial Intelligence and Machine Learning in 5G Network Security: Opportunities, advantages, and future research trends

Authors: Noman Haider, Muhammad Zeeshan Baig, Muhammad Imran

Abstract: Recent technological and architectural advancements in 5G networks have proven their worth as the deployment has started over the world. Key performance elevating factor from access to core network are softwareization, cloudification and virtualization of key enabling network functions. Along with the rapid evolution comes the risks, threats and vulnerabilities in the system for those who plan to exploit it. Therefore, ensuring fool proof end-to-end (E2E) security becomes a vital concern. Artificial intelligence (AI) and machine learning (ML) can play vital role in design, modelling and automation of efficient security protocols against diverse and wide range of threats. AI and ML has already proven their effectiveness in different fields for classification, identification and automation with higher accuracy. As 5G networks' primary selling point has been higher data rates and speed, it will be difficult to tackle wide range of threats from different points using typical/traditional protective measures. Therefore, AI and ML can play central role in protecting highly data-driven softwareized and virtualized network components. This article presents AI and ML driven applications for 5G network security, their implications and possible research directions. Also, an overview of key data collection points in 5G architecture for threat classification and anomaly detection are discussed.

Comment: 7 Pages, 3 figures, 1 table, (Magazine type article)

Date: 9 Jul 2020

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Are PETs (Privacy Enhancing Technologies) Giving Protection for Smartphones? -- A Case Study

Authors: Tanusree Sharma, Masooda Bashir

Abstract: With smartphone technologies enhanced way of interacting with the world around us, it has also been paving the way for easier access to our private and personal information. This has been amplified by the existence of numerous embedded sensors utilized by millions of apps to users. While mobile apps have positively transformed many aspects of our lives with new functionalities, many of these applications are taking advantage of vast amounts of data, privacy apps, a form of Privacy Enhancing Technology can be an effective privacy management tool for smartphones. To protect against vulnerabilities related to the collection, storage, and sharing of sensitive data, developers are building numerous privacy apps. However, there has been a lack of discretion in this particular area which calls for a proper assessment to understand the far-reaching utilization of these apps among users. During this process we have conducted an evaluation of the most popular privacy apps from our total collection of five hundred and twelve to demonstrate their functionality specific data protections they are claiming to offer, both technologically and conventionally, measuring up to standards. Taking their offered security functionalities as a scale, we conducted forensic experiments to indicate where they are failing to be consistent in maintaining protection. For legitimate validation of security gaps in assessed privacy apps, we have also utilized NIST and OWASP guidelines. We believe this study will be efficacious for continuous improvement and can be considered as a foundation towards a common standard for privacy and security measures for an app's development stage.

Date: 8 Jul 2020

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Epidemic Exposure Notification with Smartwatch: A Proximity-Based Privacy-Preserving Approach

Authors: Pai Chet Ng, Petros Spachos, Stefano Gregori, Konstantinos Plataniotis

Abstract: Businesses planning for the post-pandemic world are looking for innovative ways to protect the health and welfare of their employees and customers. Wireless technologies can play a key role in assisting contact tracing to quickly halt a local infection outbreak and prevent further spread. In this work, we present a wearable proximity and exposure notification solution based on a smartwatch that also promotes safe physical distancing in business, hospitality, or recreational facilities. Our proximity-based privacy-preserving contact tracing (P$^3$CT) leverages the Bluetooth Low Energy (BLE) technology for reliable proximity sensing, and an ambient signature protocol for preserving identity. Proximity sensing exploits the received signal strength (RSS) to detect the user's interaction and thus classifying them into low- or high-risk with respect to a patient diagnosed with an infectious disease. More precisely, a user is notified of their exposure based on their interactions, in terms of distance and time, with a patient. Our privacy-preserving protocol uses the ambient signatures to ensure that users' identities be anonymized. We demonstrate the feasibility of our proposed solution through extensive experimentation.

Date: 8 Jul 2020

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A Critical Evaluation of Open-World Machine Learning

Authors: Liwei Song, Vikash Sehwag, Arjun Nitin Bhagoji, Prateek Mittal

Abstract: Open-world machine learning (ML) combines closed-world models trained on in-distribution data with out-of-distribution (OOD) detectors, which aim to detect and reject OOD inputs. Previous works on open-world ML systems usually fail to test their reliability under diverse, and possibly adversarial conditions. Therefore, in this paper, we seek to understand how resilient are state-of-the-art open-world ML systems to changes in system components? With our evaluation across 6 OOD detectors, we find that the choice of in-distribution data, model architecture and OOD data have a strong impact on OOD detection performance, inducing false positive rates in excess of $70\%$. We further show that OOD inputs with 22 unintentional corruptions or adversarial perturbations render open-world ML systems unusable with false positive rates of up to $100\%$. To increase the resilience of open-world ML, we combine robust classifiers with OOD detection techniques and uncover a new trade-off between OOD detection and robustness.

Comment: Presented at the ICML 2020 Workshop on Uncertainty and Robustness in Deep Learning

Date: 8 Jul 2020

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SmartBugs: A Framework to Analyze Solidity Smart Contracts

Authors: João F. Ferreira, Pedro Cruz, Thomas Durieux, Rui Abreu

Abstract: Over the last few years, there has been substantial research on automated analysis, testing, and debugging of Ethereum smart contracts. However, it is not trivial to compare and reproduce that research. To address this, we present SmartBugs, an extensible and easy-to-use execution framework that simplifies the execution of analysis tools on smart contracts written in Solidity, the primary language used in Ethereum. SmartBugs is currently distributed with support for 10 tools and two datasets of Solidity contracts. The first dataset can be used to evaluate the precision of analysis tools, as it contains 143 annotated vulnerable contracts with 208 tagged vulnerabilities. The second dataset contains 47,518 unique contracts collected through Etherscan. We discuss how SmartBugs supported the largest experimental setup to date both in the number of tools and in execution time. Moreover, we show how it enables easy integration and comparison of analysis tools by presenting a new extension to the tool SmartCheck that improves substantially the detection of vulnerabilities related to the DASP10 categories Bad Randomness, Time Manipulation, and Access Control (identified vulnerabilities increased from 11% to 24%).

Comment: arXiv admin note: text overlap with arXiv:1910.10601

Date: 8 Jul 2020

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