878 research outputs found
A Touch of Evil: High-Assurance Cryptographic Hardware from Untrusted Components
The semiconductor industry is fully globalized and integrated circuits (ICs)
are commonly defined, designed and fabricated in different premises across the
world. This reduces production costs, but also exposes ICs to supply chain
attacks, where insiders introduce malicious circuitry into the final products.
Additionally, despite extensive post-fabrication testing, it is not uncommon
for ICs with subtle fabrication errors to make it into production systems.
While many systems may be able to tolerate a few byzantine components, this is
not the case for cryptographic hardware, storing and computing on confidential
data. For this reason, many error and backdoor detection techniques have been
proposed over the years. So far all attempts have been either quickly
circumvented, or come with unrealistically high manufacturing costs and
complexity.
This paper proposes Myst, a practical high-assurance architecture, that uses
commercial off-the-shelf (COTS) hardware, and provides strong security
guarantees, even in the presence of multiple malicious or faulty components.
The key idea is to combine protective-redundancy with modern threshold
cryptographic techniques to build a system tolerant to hardware trojans and
errors. To evaluate our design, we build a Hardware Security Module that
provides the highest level of assurance possible with COTS components.
Specifically, we employ more than a hundred COTS secure crypto-coprocessors,
verified to FIPS140-2 Level 4 tamper-resistance standards, and use them to
realize high-confidentiality random number generation, key derivation, public
key decryption and signing. Our experiments show a reasonable computational
overhead (less than 1% for both Decryption and Signing) and an exponential
increase in backdoor-tolerance as more ICs are added
TrojDRL: Trojan Attacks on Deep Reinforcement Learning Agents
Recent work has identified that classification models implemented as
neural networks are vulnerable to
data-poisoning and Trojan attacks at training time.
In this work, we show that these
training-time vulnerabilities extend to
deep reinforcement learning (DRL) agents
and can be exploited by an adversary with access
to the training process.
In particular, we focus on
Trojan attacks that augment the function of
reinforcement learning policies
with hidden behaviors.
We demonstrate that such attacks can be implemented
through minuscule data poisoning (as little as 0.025% of the training data) and
in-band
reward modification that does not affect
the reward on normal inputs.
The policies learned with our proposed attack approach perform imperceptibly similar to benign policies but deteriorate drastically when the Trojan is triggered
in both targeted and untargeted settings.
Furthermore, we show that existing Trojan defense mechanisms for classification tasks are not effective in the reinforcement learning setting
Trojan Taxonomy in Quantum Computing
Quantum computing introduces unfamiliar security vulnerabilities demanding
customized threat models. Hardware and software Trojans pose serious concerns
needing rethinking from classical paradigms. This paper develops the first
structured taxonomy of Trojans tailored to quantum information systems. We
enumerate potential attack vectors across the quantum stack from hardware to
software layers. A categorization of quantum Trojan types and payloads is
outlined ranging from reliability degradation, functionality corruption,
backdoors, and denial-of-service. Adversarial motivations behind quantum
Trojans are analyzed. By consolidating diverse threats into a unified
perspective, this quantum Trojan taxonomy provides insights guiding threat
modeling, risk analysis, detection mechanisms, and security best practices
customized for this novel computing paradigm.Comment: 6 pages, 2 figure
Studying a Virtual Testbed for Unverified Data
It is difficult to fully know the effects a piece of software will have on your computer, particularly when the software is distributed by an unknown source. The research in this paper focuses on malware detection, virtualization, and sandbox/honeypot techniques with the goal of improving the security of installing useful, but unverifiable, software. With a combination of these techniques, it should be possible to install software in an environment where it cannot harm a machine, but can be tested to determine its safety. Testing for malware, performance, network connectivity, memory usage, and interoperability can be accomplished without allowing the program to access the base operating system of a machine. After the full effects of the software are understood and it is determined to be safe, it could then be run from, and given access to, the base operating system. This thesis investigates the feasibility of creating a system to verify the security of unknown software while ensuring it will have no negative impact on the host machine
Rapid mapping of digital integrated circuit logic gates via multi-spectral backside imaging
Modern semiconductor integrated circuits are increasingly fabricated at
untrusted third party foundries. There now exist myriad security threats of
malicious tampering at the hardware level and hence a clear and pressing need
for new tools that enable rapid, robust and low-cost validation of circuit
layouts. Optical backside imaging offers an attractive platform, but its
limited resolution and throughput cannot cope with the nanoscale sizes of
modern circuitry and the need to image over a large area. We propose and
demonstrate a multi-spectral imaging approach to overcome these obstacles by
identifying key circuit elements on the basis of their spectral response. This
obviates the need to directly image the nanoscale components that define them,
thereby relaxing resolution and spatial sampling requirements by 1 and 2 - 4
orders of magnitude respectively. Our results directly address critical
security needs in the integrated circuit supply chain and highlight the
potential of spectroscopic techniques to address fundamental resolution
obstacles caused by the need to image ever shrinking feature sizes in
semiconductor integrated circuits
A New Role for Human Resource Managers: Social Engineering Defense
[Excerpt] The general risk of social engineering attacks to organizations has increased with the rise of digital computing and communications, while for an attacker the risk has decreased. In order to counter the increased risk, organizations should recognize that human resources (HR) professionals have just as much responsibility and capability in preventing this risk as information technology (IT) professionals.
Part I of this paper begins by defining social engineering in context and with a brief history pre-digital age attacks. It concludes by showing the intersection of HR and IT through examples of operational attack vectors. In part II, the discussion moves to a series of measures that can be taken to help prevent social engineering attacks
Built-In Return-Oriented Programs in Embedded Systems and Deep Learning for Hardware Trojan Detection
Microcontrollers and integrated circuits in general have become ubiquitous in the world today. All aspects of our lives depend on them from driving to work, to calling our friends, to checking our bank account balance. People who would do harm to individuals, corporations and nation states are aware of this and for that reason they seek to find or create and exploit vulnerabilities in integrated circuits. This dissertation contains three papers dealing with these types of vulnerabilities. The first paper talks about a vulnerability that was found on a microcontroller, which is a type of integrated circuit. The final two papers deal with hardware trojans. Hardware trojans are purposely added to the design of an integrated circuit in secret so that the manufacturer doesn’t know about it. They are used to damage the integrated circuit, leak confidential information, or in other ways alter the circuit. Hardware trojans are a major concern for anyone using integrated circuits because an attacker can alter a circuit in almost any way if they are successful in inserting one. A known method to prevent hardware trojan insertion is discussed and a type of circuit for which this method does not work is revealed. The discussion of hardware trojans is concluded with a new way to detect them before the integrated circuit is manufactured. Modern deep learning models are used to detect the portions of the hardware trojan called triggers that activate them
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