11,757 research outputs found
MAT: A Multi-strength Adversarial Training Method to Mitigate Adversarial Attacks
Some recent works revealed that deep neural networks (DNNs) are vulnerable to
so-called adversarial attacks where input examples are intentionally perturbed
to fool DNNs. In this work, we revisit the DNN training process that includes
adversarial examples into the training dataset so as to improve DNN's
resilience to adversarial attacks, namely, adversarial training. Our
experiments show that different adversarial strengths, i.e., perturbation
levels of adversarial examples, have different working zones to resist the
attack. Based on the observation, we propose a multi-strength adversarial
training method (MAT) that combines the adversarial training examples with
different adversarial strengths to defend adversarial attacks. Two training
structures - mixed MAT and parallel MAT - are developed to facilitate the
tradeoffs between training time and memory occupation. Our results show that
MAT can substantially minimize the accuracy degradation of deep learning
systems to adversarial attacks on MNIST, CIFAR-10, CIFAR-100, and SVHN.Comment: 6 pages, 4 figures, 2 table
Hyp3rArmor: reducing web application exposure to automated attacks
Web applications (webapps) are subjected constantly to automated, opportunistic attacks from autonomous robots (bots) engaged in reconnaissance to discover victims that may be vulnerable to specific exploits. This is a typical behavior found in botnet recruitment, worm propagation, largescale fingerprinting and vulnerability scanners. Most anti-bot techniques are deployed at the application layer, thus leaving the network stack of the webapp’s server exposed. In this paper we present a mechanism called Hyp3rArmor, that addresses this vulnerability by minimizing the webapp’s attack surface exposed to automated opportunistic attackers, for JavaScriptenabled web browser clients. Our solution uses port knocking to eliminate the webapp’s visible network footprint. Clients of the webapp are directed to a visible static web server to obtain JavaScript that authenticates the client to the webapp server (using port knocking) before making any requests to the webapp. Our implementation of Hyp3rArmor, which is compatible with all webapp architectures, has been deployed and used to defend single and multi-page websites on the Internet for 114 days. During this time period the static web server observed 964 attempted attacks that were deflected from the webapp, which was only accessed by authenticated clients. Our evaluation shows that in most cases client-side overheads were negligible and that server-side overheads were minimal. Hyp3rArmor is ideal for critical systems and legacy applications that must be accessible on the Internet. Additionally Hyp3rArmor is composable with other security tools, adding an additional layer to a defense in depth approach.This work has been supported by the National Science Foundation (NSF) awards #1430145, #1414119, and #1012798
DR.SGX: Hardening SGX Enclaves against Cache Attacks with Data Location Randomization
Recent research has demonstrated that Intel's SGX is vulnerable to various
software-based side-channel attacks. In particular, attacks that monitor CPU
caches shared between the victim enclave and untrusted software enable accurate
leakage of secret enclave data. Known defenses assume developer assistance,
require hardware changes, impose high overhead, or prevent only some of the
known attacks. In this paper we propose data location randomization as a novel
defensive approach to address the threat of side-channel attacks. Our main goal
is to break the link between the cache observations by the privileged adversary
and the actual data accesses by the victim. We design and implement a
compiler-based tool called DR.SGX that instruments enclave code such that data
locations are permuted at the granularity of cache lines. We realize the
permutation with the CPU's cryptographic hardware-acceleration units providing
secure randomization. To prevent correlation of repeated memory accesses we
continuously re-randomize all enclave data during execution. Our solution
effectively protects many (but not all) enclaves from cache attacks and
provides a complementary enclave hardening technique that is especially useful
against unpredictable information leakage
TRIDEnT: Building Decentralized Incentives for Collaborative Security
Sophisticated mass attacks, especially when exploiting zero-day
vulnerabilities, have the potential to cause destructive damage to
organizations and critical infrastructure. To timely detect and contain such
attacks, collaboration among the defenders is critical. By correlating
real-time detection information (alerts) from multiple sources (collaborative
intrusion detection), defenders can detect attacks and take the appropriate
defensive measures in time. However, although the technical tools to facilitate
collaboration exist, real-world adoption of such collaborative security
mechanisms is still underwhelming. This is largely due to a lack of trust and
participation incentives for companies and organizations. This paper proposes
TRIDEnT, a novel collaborative platform that aims to enable and incentivize
parties to exchange network alert data, thus increasing their overall detection
capabilities. TRIDEnT allows parties that may be in a competitive relationship,
to selectively advertise, sell and acquire security alerts in the form of
(near) real-time peer-to-peer streams. To validate the basic principles behind
TRIDEnT, we present an intuitive game-theoretic model of alert sharing, that is
of independent interest, and show that collaboration is bound to take place
infinitely often. Furthermore, to demonstrate the feasibility of our approach,
we instantiate our design in a decentralized manner using Ethereum smart
contracts and provide a fully functional prototype.Comment: 28 page
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