70 research outputs found
Mitigating Branch-Shadowing Attacks on Intel SGX using Control Flow Randomization
Intel Software Guard Extensions (SGX) is a promising hardware-based
technology for protecting sensitive computations from potentially compromised
system software. However, recent research has shown that SGX is vulnerable to
branch-shadowing -- a side channel attack that leaks the fine-grained (branch
granularity) control flow of an enclave (SGX protected code), potentially
revealing sensitive data to the attacker. The previously-proposed defense
mechanism, called Zigzagger, attempted to hide the control flow, but has been
shown to be ineffective if the attacker can single-step through the enclave
using the recent SGX-Step framework.
Taking into account these stronger attacker capabilities, we propose a new
defense against branch-shadowing, based on control flow randomization. Our
scheme is inspired by Zigzagger, but provides quantifiable security guarantees
with respect to a tunable security parameter. Specifically, we eliminate
conditional branches and hide the targets of unconditional branches using a
combination of compile-time modifications and run-time code randomization.
We evaluated the performance of our approach by measuring the run-time
overhead of ten benchmark programs of SGX-Nbench in SGX environment
Software Grand Exposure: SGX Cache Attacks Are Practical
Side-channel information leakage is a known limitation of SGX. Researchers
have demonstrated that secret-dependent information can be extracted from
enclave execution through page-fault access patterns. Consequently, various
recent research efforts are actively seeking countermeasures to SGX
side-channel attacks. It is widely assumed that SGX may be vulnerable to other
side channels, such as cache access pattern monitoring, as well. However, prior
to our work, the practicality and the extent of such information leakage was
not studied.
In this paper we demonstrate that cache-based attacks are indeed a serious
threat to the confidentiality of SGX-protected programs. Our goal was to design
an attack that is hard to mitigate using known defenses, and therefore we mount
our attack without interrupting enclave execution. This approach has major
technical challenges, since the existing cache monitoring techniques experience
significant noise if the victim process is not interrupted. We designed and
implemented novel attack techniques to reduce this noise by leveraging the
capabilities of the privileged adversary. Our attacks are able to recover
confidential information from SGX enclaves, which we illustrate in two example
cases: extraction of an entire RSA-2048 key during RSA decryption, and
detection of specific human genome sequences during genomic indexing. We show
that our attacks are more effective than previous cache attacks and harder to
mitigate than previous SGX side-channel attacks
SoK: A Systematic Review of TEE Usage for Developing Trusted Applications
Trusted Execution Environments (TEEs) are a feature of modern central
processing units (CPUs) that aim to provide a high assurance, isolated
environment in which to run workloads that demand both confidentiality and
integrity. Hardware and software components in the CPU isolate workloads,
commonly referred to as Trusted Applications (TAs), from the main operating
system (OS). This article aims to analyse the TEE ecosystem, determine its
usability, and suggest improvements where necessary to make adoption easier. To
better understand TEE usage, we gathered academic and practical examples from a
total of 223 references. We summarise the literature and provide a publication
timeline, along with insights into the evolution of TEE research and
deployment. We categorise TAs into major groups and analyse the tools available
to developers. Lastly, we evaluate trusted container projects, test
performance, and identify the requirements for migrating applications inside
them.Comment: In The 18th International Conference on Availability, Reliability and
Security (ARES 2023), August 29 -- September 01, 2023, Benevento, Italy. 15
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Security and trust in cloud computing and IoT through applying obfuscation, diversification, and trusted computing technologies
Cloud computing and Internet of Things (IoT) are very widely spread and commonly used technologies nowadays. The advanced services offered by cloud computing have made it a highly demanded technology.
Enterprises and businesses are more and more relying on the cloud to deliver services to their customers. The prevalent use of cloud means that more data is stored outside the organization’s premises, which raises concerns about the security and privacy of the stored and processed data. This highlights the significance of effective security practices to secure the cloud infrastructure.
The number of IoT devices is growing rapidly and the technology is being employed in a wide range of sectors including smart healthcare, industry automation, and smart environments. These devices collect and exchange a great deal of information, some of which may contain critical and personal data of the users of the device. Hence, it is highly significant to protect the collected and shared data over the network; notwithstanding, the studies signify that attacks on these devices are increasing, while a high percentage of IoT devices lack proper security measures to protect the devices, the data, and the privacy of the users.
In this dissertation, we study the security of cloud computing and IoT and propose software-based security approaches supported by the hardware-based technologies to provide robust measures for enhancing the security of these environments. To achieve this goal, we use obfuscation and diversification as the potential software security techniques. Code obfuscation protects the software from malicious reverse engineering and diversification mitigates the risk of large-scale exploits. We study trusted computing and Trusted Execution Environments (TEE) as the hardware-based security solutions. Trusted Platform Module (TPM) provides security and trust through a hardware root of trust, and assures the integrity of a platform. We also study Intel SGX which is a TEE solution that guarantees the integrity and confidentiality of the code and data loaded onto its protected container, enclave.
More precisely, through obfuscation and diversification of the operating systems and APIs of the IoT devices, we secure them at the application level, and by obfuscation and diversification of the communication protocols, we protect the communication of data between them at the network level. For securing the cloud computing, we employ obfuscation and diversification techniques for securing the cloud computing software at the client-side. For an enhanced level of security, we employ hardware-based security solutions, TPM and SGX. These solutions, in addition to security, ensure layered trust in various layers from hardware to the application.
As the result of this PhD research, this dissertation addresses a number of security risks targeting IoT and cloud computing through the delivered publications and presents a brief outlook on the future research directions.Pilvilaskenta ja esineiden internet ovat nykyään hyvin tavallisia ja laajasti sovellettuja tekniikkoja. Pilvilaskennan pitkälle kehittyneet palvelut ovat tehneet siitä hyvin kysytyn teknologian. Yritykset enenevässä määrin nojaavat pilviteknologiaan toteuttaessaan palveluita asiakkailleen. Vallitsevassa pilviteknologian soveltamistilanteessa yritykset ulkoistavat tietojensa käsittelyä yrityksen ulkopuolelle, minkä voidaan nähdä nostavan esiin huolia taltioitavan ja käsiteltävän tiedon turvallisuudesta ja yksityisyydestä. Tämä korostaa tehokkaiden turvallisuusratkaisujen merkitystä osana pilvi-infrastruktuurin turvaamista.
Esineiden internet -laitteiden lukumäärä on nopeasti kasvanut. Teknologiana sitä sovelletaan laajasti monilla sektoreilla, kuten älykkäässä terveydenhuollossa, teollisuusautomaatiossa ja älytiloissa. Sellaiset laitteet keräävät ja välittävät suuria määriä informaatiota, joka voi sisältää laitteiden käyttäjien kannalta kriittistä ja yksityistä tietoa. Tästä syystä johtuen on erittäin merkityksellistä suojata verkon yli kerättävää ja jaettavaa tietoa. Monet tutkimukset osoittavat esineiden internet -laitteisiin kohdistuvien tietoturvahyökkäysten määrän olevan nousussa, ja samaan aikaan suuri osuus näistä laitteista ei omaa kunnollisia teknisiä ominaisuuksia itse laitteiden tai niiden käyttäjien yksityisen tiedon suojaamiseksi.
Tässä väitöskirjassa tutkitaan pilvilaskennan sekä esineiden internetin tietoturvaa ja esitetään ohjelmistopohjaisia tietoturvalähestymistapoja turvautumalla osittain laitteistopohjaisiin teknologioihin. Esitetyt lähestymistavat tarjoavat vankkoja keinoja tietoturvallisuuden kohentamiseksi näissä konteksteissa. Tämän saavuttamiseksi työssä sovelletaan obfuskaatiota ja diversifiointia potentiaalisiana ohjelmistopohjaisina tietoturvatekniikkoina. Suoritettavan koodin obfuskointi suojaa pahantahtoiselta ohjelmiston takaisinmallinnukselta ja diversifiointi torjuu tietoturva-aukkojen laaja-alaisen hyödyntämisen riskiä. Väitöskirjatyössä tutkitaan luotettua laskentaa ja luotettavan laskennan suoritusalustoja laitteistopohjaisina tietoturvaratkaisuina. TPM (Trusted Platform Module) tarjoaa turvallisuutta ja luottamuksellisuutta rakentuen laitteistopohjaiseen luottamukseen. Pyrkimyksenä on taata suoritusalustan eheys. Työssä tutkitaan myös Intel SGX:ää yhtenä luotettavan suorituksen suoritusalustana, joka takaa suoritettavan koodin ja datan eheyden sekä luottamuksellisuuden pohjautuen suojatun säiliön, saarekkeen, tekniseen toteutukseen.
Tarkemmin ilmaistuna työssä turvataan käyttöjärjestelmä- ja sovellusrajapintatasojen obfuskaation ja diversifioinnin kautta esineiden internet -laitteiden ohjelmistokerrosta. Soveltamalla samoja tekniikoita protokollakerrokseen, työssä suojataan laitteiden välistä tiedonvaihtoa verkkotasolla. Pilvilaskennan turvaamiseksi työssä sovelletaan obfuskaatio ja diversifiointitekniikoita asiakaspuolen ohjelmistoratkaisuihin. Vankemman tietoturvallisuuden saavuttamiseksi työssä hyödynnetään laitteistopohjaisia TPM- ja SGX-ratkaisuja. Tietoturvallisuuden lisäksi nämä ratkaisut tarjoavat monikerroksisen luottamuksen rakentuen laitteistotasolta ohjelmistokerrokseen asti.
Tämän väitöskirjatutkimustyön tuloksena, osajulkaisuiden kautta, vastataan moniin esineiden internet -laitteisiin ja pilvilaskentaan kohdistuviin tietoturvauhkiin. Työssä esitetään myös näkemyksiä jatkotutkimusaiheista
Offline Model Guard: Secure and Private ML on Mobile Devices
Performing machine learning tasks in mobile applications yields a challenging
conflict of interest: highly sensitive client information (e.g., speech data)
should remain private while also the intellectual property of service providers
(e.g., model parameters) must be protected. Cryptographic techniques offer
secure solutions for this, but have an unacceptable overhead and moreover
require frequent network interaction. In this work, we design a practically
efficient hardware-based solution. Specifically, we build Offline Model Guard
(OMG) to enable privacy-preserving machine learning on the predominant mobile
computing platform ARM - even in offline scenarios. By leveraging a trusted
execution environment for strict hardware-enforced isolation from other system
components, OMG guarantees privacy of client data, secrecy of provided models,
and integrity of processing algorithms. Our prototype implementation on an ARM
HiKey 960 development board performs privacy-preserving keyword recognition
using TensorFlow Lite for Microcontrollers in real time.Comment: Original Publication (in the same form): DATE 202
LogSafe: Secure and Scalable Data Logger for IoT Devices
As devices in the Internet of Things (IoT) increase in number and integrate with everyday lives, large amounts of personal information will be generated. With multiple discovered vulnerabilities in current IoT networks, a malicious attacker might be able to get access to and misuse this personal data. Thus, a logger that stores this information securely would make it possible to perform forensic analysis in case of such attacks that target valuable data. In this paper, we propose LogSafe, a scalable, fault-tolerant logger that leverages the use of Intel Software Guard Extensions (SGX) to store logs from IoT devices efficiently and securely. Using the security guarantees of SGX, LogSafe is designed to run on an untrusted cloud infrastructure and satisfies Confidentiality, Integrity, and Availability (CIA) security properties. Finally, we provide an exhaustive evaluation of LogSafe in order to demonstrate that it is capable of handling logs from a large number of IoT devices and at a very high data transmission rate
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