Title |
Remark On the Security of Strong Proxy Signature Scheme with Proxy Signer Privacy
Protection |
Author |
Amit K Awasthi |
Abstract |
In 1996, Mambo et al. introduced the proxy signature
scheme to delegate the signing capability to a proxy signer. Various
constructions were made to device a strong non-designated proxy signa-
ture scheme. In 2002, Shum and Wei proposed an extended scheme to
hide the identity of the proxy signer. A trusted authority can reveal the
proxy signer's identity if required. In this paper we show some possible
attacks on this scheme.
|
Keywords |
Proxy Signature, Warrant, Message Recovery, Cryptog-raphy, Digital Signatures |
|
|
A Secure Partition Based Document Image Watermarking Scheme |
Author |
Shiyan Hu |
Abstract |
In this paper, a new document image watermarking method
based on secure partitioning scheme is proposed and tested. In the
method, a document image is securely divided into weight-invariant
partitions followed by selectively modifying characters to embed wa-
termarks. The high security of watermark results from applying a prob-
abilistic metaheuristic algorithm, namely the ant colony system, to ap-
proximate the involved Bottleneck Hamiltonian Path problem to gener-
ate key-dependent image partitions. For better e±ciency, the farthest
point heuristic and the multi-scale strategy are introduced into the ant
colony system. Our experimental results demonstrate that the proposed
watermarking scheme is secure, e±cient, and robust to common attacks.
The proposed secure partition scheme could serve as a general frame-
work to introduce high security to prevailing watermarking techniques. |
Keywords |
Digital watermarking, Document image, Secure partition,
Ant colony system, Robustness. |
|
|
Honeypot Detection in Advanced Botnet Attacks |
Authors |
Ping Wang, Lei Wu, Ryan Cunningham,
Cliff C. Zou |
Abstract |
Botnets have become one of the major attacks in current
Internet due to their illicit profitable financial gain. Meanwhile, honeypots
have been successfully deployed in many computer security defense
systems. Since honeypots set up by security defenders can attract bot-
net compromises and become spies in exposing botnet membership and
botnet attacker behaviors, they are widely used by security defenders
in botnet defense. Therefore, attackers constructing and maintaining
botnets will be forced to find ways to avoid honeypot traps. In this
paper, we present a hardware and software independent honeypot de-
tection methodology based on the following assumption: security pro-
fessionals deploying honeypots have liability constraint such that they
cannot allow their honeypots to participate in real attacks that could
cause damage to others, while attackers do not need to follow this con-
straint. Attackers could detect honeypots in their botnets by checking
whether compromised machines in a botnet can successfully send out
unmodified malicious traffic. Based on this basic detection principle,
we present honeypot detection techniques to be used in both central-
ized botnets and peer-to-peer structured botnets. Experiments show
that current standard honeypot and honeynet programs are vulnerable
to the proposed honeypot detection techniques. In the end, we discuss
some guidelines for defending against general honeypot-aware attacks. |
Keywords |
Liability; honeypot; botnet; peer-to-peer; modeling |
|
|
Speeding up Euclid’s GCD algorithm with no magnitude comparisons |
Authors |
Che Wun Chiou, Fu Hua Chou, Yun-Chi Yeh |
Abstract |
The Euclid’s greatest common divisor (GCD) algorithm is an efficient approach for calculating multiplicative inversions, and relies mainly on a fast modular arithmetic algorithm to run quickly. A traditional modular arithmetic algorithm based on non-restoring division needs a magnitude comparison for each iteration of shift-and-subtract operation. This process is time-consuming, since it requires magnitude comparisons for every computation iteration step. To eradicate this problem, this study develops a new fast Euclidean GCD algorithm without magnitude comparisons. The proposed modular algorithm has an execution time that is about 33% shorter than the conventional modular algorithm. |
Keywords |
GCD, modular arithmetic, public-key cryptosystem, multiplicative inversion, division |
|
|
Semantics-aware Security Policy Specification for the Semantic Web Data |
Authors |
Li Qin, Vijayalakshmi Atluri |
Abstract |
The Semantic Web has been envisioned as a machine-interpretable web, where data instances are described through concepts defined and related in ontologies. Though ontologies are publicly available as a crucial component of the semantic web infrastructure, many data instances are sensitive and should be kept confidential. Sensitive information can be illegally inferred from other seemingly unclassified information in combination with the underlying data semantics and inter-relationships revealed by ontologies. In other words, the visibility of ontologies can pose inference threats to the security of data instances, and this requires security policies be specified in a way that the semantic relationships among data instances are taken into account. To protect the semantic web data or other semantics-rich data, this paper presents semantics-aware security policy specification. We propose concept-level, association-level and propertylevel access control models for different security objects, and authorizations be propagated based on different inference patterns. These propagation policies can be used to generate safe and consistent access control authorizations. |
Keywords |
Information security; Inference problem; Access control; Semantics; Ontologies; Semantic Web |
|
|
An Integrated Approach to Network Intrusion Detection with Block Clustering, Generalized Logistic Regression and Linear Discriminant Analysis |
Authors |
Zhanshan (Sam) Ma |
Abstract |
The objective of this study is to develop an integrated
modeling approach to network intrusion detection with three
multivariate statistical methods: block clustering (BC), generalized
logistic regression (GLR), and linear discriminant analysis (LDA). A
pipeline processing strategy with BC followed by either GLR or LDA
is attempted in order to automate the intrusion detection process. The
preliminary testing results show that the integration of BC and LDA is
very promising, but that of BC and GLR is uncertain. Essentially, BC
offers a classification algorithm, and LDA or GLR further assesses the
results pipelined from BC and makes the judgment (e.g., intrusive,
suspicious, or normal). Although clustering techniques have been
widely utilized for intrusion detection from the very beginning of the
field, to the best of our knowledge, block clustering has not been
applied in intrusion detection or computer science previously. The twoway
joining strategy of BC in cluster detection is especially desirable
for intrusion detection since information from both data cases and
variables (features) are synthesized to form block clusters, while other
clustering methods often only consider information from either data
cases or variables. The paper also discusses the justification for our
choice of the three statistical methods. The choice is largely determined
by two of the most obvious properties of intrusion audit data: (i) most
variables in intrusion detection data are categorical, rather than
continuous and (ii) the probability distributions of these variables
usually are not normally distributed. We believe that recognizing these
two characteristics is of fundamental importance. First, the statistical
methods that work perfectly for continuous variables may not work for
the categorical variables or the reliability of conclusions may be
strongly compromised. Second, historically a large amount of statistical
methods were developed based on the assumption of the multivariate
normal distribution. In perspective, we suggest that the integration of
BC with the independent component analysis (ICA) (that has been
successfully utilized in speech recognition, brain imaging, and also
intrusion detection in combination with other statistical methods) is
likely to offer a mutually complementary approach. We further suggest
that the integration of the approach developed in this paper with
multidimensional scaling (MDS) may produce an effective technology
for building visualized real-time intrusion detection systems. |
Keywords |
Intrusion Detection, Block Clustering, Generalized Logistic
Regression, Linear Discriminant Analysis, Independent Component Analysis,
Multidimensional Scaling. |
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