1,644 research outputs found
Arguing satisfaction of security requirements
This chapter presents a process for security requirements elicitation and analysis,
based around the construction of a satisfaction argument for the security of a
system. The process starts with the enumeration of security goals based on assets
in the system, then uses these goals to derive security requirements in the form of
constraints. Next, a satisfaction argument for the system is constructed, using a
problem-centered representation, a formal proof to analyze properties that can be
demonstrated, and structured informal argumentation of the assumptions exposed
during construction of the argument. Constructing the satisfaction argument can
expose missing and inconsistent assumptions about system context and behavior
that effect security, and a completed argument provides assurances that a system
can respect its security requirements
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
Using Event Calculus to Formalise Policy Specification and Analysis
As the interest in using policy-based approaches for systems management grows, it is becoming increasingly important to develop methods for performing analysis and refinement of policy specifications. Although this is an area that researchers have devoted some attention to, none of the proposed solutions address the issues of analysing specifications that combine authorisation and management policies; analysing policy specifications that contain constraints on the applicability of the policies; and performing a priori analysis of the specification that will both detect the presence of inconsistencies and explain the situations in which the conflict will occur. We present a method for transforming both policy and system behaviour specifications into a formal notation that is based on event calculus. Additionally it describes how this formalism can be used in conjunction with abductive reasoning techniques to perform a priori analysis of policy specifications for the various conflict types identified in the literature. Finally, it presents some initial thoughts on how this notation and analysis technique could be used to perform policy refinement
Spatiotemporal patterns and predictability of cyberattacks
A relatively unexplored issue in cybersecurity science and engineering is
whether there exist intrinsic patterns of cyberattacks. Conventional wisdom
favors absence of such patterns due to the overwhelming complexity of the
modern cyberspace. Surprisingly, through a detailed analysis of an extensive
data set that records the time-dependent frequencies of attacks over a
relatively wide range of consecutive IP addresses, we successfully uncover
intrinsic spatiotemporal patterns underlying cyberattacks, where the term
"spatio" refers to the IP address space. In particular, we focus on analyzing
{\em macroscopic} properties of the attack traffic flows and identify two main
patterns with distinct spatiotemporal characteristics: deterministic and
stochastic. Strikingly, there are very few sets of major attackers committing
almost all the attacks, since their attack "fingerprints" and target selection
scheme can be unequivocally identified according to the very limited number of
unique spatiotemporal characteristics, each of which only exists on a
consecutive IP region and differs significantly from the others. We utilize a
number of quantitative measures, including the flux-fluctuation law, the Markov
state transition probability matrix, and predictability measures, to
characterize the attack patterns in a comprehensive manner. A general finding
is that the attack patterns possess high degrees of predictability, potentially
paving the way to anticipating and, consequently, mitigating or even preventing
large-scale cyberattacks using macroscopic approaches
Spatiotemporal Patterns and Predictability of Cyberattacks
Y.C.L. was supported by Air Force Office of Scientific Research (AFOSR) under grant no. FA9550-10-1-0083 and Army Research Office (ARO) under grant no. W911NF-14-1-0504. S.X. was supported by Army Research Office (ARO) under grant no. W911NF-13-1-0141. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD
CryptoKnight:generating and modelling compiled cryptographic primitives
Cryptovirological augmentations present an immediate, incomparable threat. Over the last decade, the substantial proliferation of crypto-ransomware has had widespread consequences for consumers and organisations alike. Established preventive measures perform well, however, the problem has not ceased. Reverse engineering potentially malicious software is a cumbersome task due to platform eccentricities and obfuscated transmutation mechanisms, hence requiring smarter, more efficient detection strategies. The following manuscript presents a novel approach for the classification of cryptographic primitives in compiled binary executables using deep learning. The model blueprint, a Dynamic Convolutional Neural Network (DCNN), is fittingly configured to learn from variable-length control flow diagnostics output from a dynamic trace. To rival the size and variability of equivalent datasets, and to adequately train our model without risking adverse exposure, a methodology for the procedural generation of synthetic cryptographic binaries is defined, using core primitives from OpenSSL with multivariate obfuscation, to draw a vastly scalable distribution. The library, CryptoKnight, rendered an algorithmic pool of AES, RC4, Blowfish, MD5 and RSA to synthesise combinable variants which automatically fed into its core model. Converging at 96% accuracy, CryptoKnight was successfully able to classify the sample pool with minimal loss and correctly identified the algorithm in a real-world crypto-ransomware applicatio
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