18,392 research outputs found
ACMiner: Extraction and Analysis of Authorization Checks in Android's Middleware
Billions of users rely on the security of the Android platform to protect
phones, tablets, and many different types of consumer electronics. While
Android's permission model is well studied, the enforcement of the protection
policy has received relatively little attention. Much of this enforcement is
spread across system services, taking the form of hard-coded checks within
their implementations. In this paper, we propose Authorization Check Miner
(ACMiner), a framework for evaluating the correctness of Android's access
control enforcement through consistency analysis of authorization checks.
ACMiner combines program and text analysis techniques to generate a rich set of
authorization checks, mines the corresponding protection policy for each
service entry point, and uses association rule mining at a service granularity
to identify inconsistencies that may correspond to vulnerabilities. We used
ACMiner to study the AOSP version of Android 7.1.1 to identify 28
vulnerabilities relating to missing authorization checks. In doing so, we
demonstrate ACMiner's ability to help domain experts process thousands of
authorization checks scattered across millions of lines of code
AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments
This report considers the application of Articial Intelligence (AI) techniques to
the problem of misuse detection and misuse localisation within telecommunications
environments. A broad survey of techniques is provided, that covers inter alia
rule based systems, model-based systems, case based reasoning, pattern matching,
clustering and feature extraction, articial neural networks, genetic algorithms, arti
cial immune systems, agent based systems, data mining and a variety of hybrid
approaches. The report then considers the central issue of event correlation, that
is at the heart of many misuse detection and localisation systems. The notion of
being able to infer misuse by the correlation of individual temporally distributed
events within a multiple data stream environment is explored, and a range of techniques,
covering model based approaches, `programmed' AI and machine learning
paradigms. It is found that, in general, correlation is best achieved via rule based approaches,
but that these suffer from a number of drawbacks, such as the difculty of
developing and maintaining an appropriate knowledge base, and the lack of ability
to generalise from known misuses to new unseen misuses. Two distinct approaches
are evident. One attempts to encode knowledge of known misuses, typically within
rules, and use this to screen events. This approach cannot generally detect misuses
for which it has not been programmed, i.e. it is prone to issuing false negatives.
The other attempts to `learn' the features of event patterns that constitute normal
behaviour, and, by observing patterns that do not match expected behaviour, detect
when a misuse has occurred. This approach is prone to issuing false positives,
i.e. inferring misuse from innocent patterns of behaviour that the system was not
trained to recognise. Contemporary approaches are seen to favour hybridisation,
often combining detection or localisation mechanisms for both abnormal and normal
behaviour, the former to capture known cases of misuse, the latter to capture
unknown cases. In some systems, these mechanisms even work together to update
each other to increase detection rates and lower false positive rates. It is concluded
that hybridisation offers the most promising future direction, but that a rule or state
based component is likely to remain, being the most natural approach to the correlation
of complex events. The challenge, then, is to mitigate the weaknesses of
canonical programmed systems such that learning, generalisation and adaptation
are more readily facilitated
RefDiff: Detecting Refactorings in Version Histories
Refactoring is a well-known technique that is widely adopted by software
engineers to improve the design and enable the evolution of a system. Knowing
which refactoring operations were applied in a code change is a valuable
information to understand software evolution, adapt software components, merge
code changes, and other applications. In this paper, we present RefDiff, an
automated approach that identifies refactorings performed between two code
revisions in a git repository. RefDiff employs a combination of heuristics
based on static analysis and code similarity to detect 13 well-known
refactoring types. In an evaluation using an oracle of 448 known refactoring
operations, distributed across seven Java projects, our approach achieved
precision of 100% and recall of 88%. Moreover, our evaluation suggests that
RefDiff has superior precision and recall than existing state-of-the-art
approaches.Comment: Paper accepted at 14th International Conference on Mining Software
Repositories (MSR), pages 1-11, 201
kLog: A Language for Logical and Relational Learning with Kernels
We introduce kLog, a novel approach to statistical relational learning.
Unlike standard approaches, kLog does not represent a probability distribution
directly. It is rather a language to perform kernel-based learning on
expressive logical and relational representations. kLog allows users to specify
learning problems declaratively. It builds on simple but powerful concepts:
learning from interpretations, entity/relationship data modeling, logic
programming, and deductive databases. Access by the kernel to the rich
representation is mediated by a technique we call graphicalization: the
relational representation is first transformed into a graph --- in particular,
a grounded entity/relationship diagram. Subsequently, a choice of graph kernel
defines the feature space. kLog supports mixed numerical and symbolic data, as
well as background knowledge in the form of Prolog or Datalog programs as in
inductive logic programming systems. The kLog framework can be applied to
tackle the same range of tasks that has made statistical relational learning so
popular, including classification, regression, multitask learning, and
collective classification. We also report about empirical comparisons, showing
that kLog can be either more accurate, or much faster at the same level of
accuracy, than Tilde and Alchemy. kLog is GPLv3 licensed and is available at
http://klog.dinfo.unifi.it along with tutorials
Detecting Malicious Software By Dynamicexecution
Traditional way to detect malicious software is based on signature matching. However, signature matching only detects known malicious software. In order to detect unknown malicious software, it is necessary to analyze the software for its impact on the system when the software is executed. In one approach, the software code can be statically analyzed for any malicious patterns. Another approach is to execute the program and determine the nature of the program dynamically. Since the execution of malicious code may have negative impact on the system, the code must be executed in a controlled environment. For that purpose, we have developed a sandbox to protect the system. Potential malicious behavior is intercepted by hooking Win32 system calls. Using the developed sandbox, we detect unknown virus using dynamic instruction sequences mining techniques. By collecting runtime instruction sequences in basic blocks, we extract instruction sequence patterns based on instruction associations. We build classification models with these patterns. By applying this classification model, we predict the nature of an unknown program. We compare our approach with several other approaches such as simple heuristics, NGram and static instruction sequences. We have also developed a method to identify a family of malicious software utilizing the system call trace. We construct a structural system call diagram from captured dynamic system call traces. We generate smart system call signature using profile hidden Markov model (PHMM) based on modularized system call block. Smart system call signature weakly identifies a family of malicious software
Data Mining in Electronic Commerce
Modern business is rushing toward e-commerce. If the transition is done
properly, it enables better management, new services, lower transaction costs
and better customer relations. Success depends on skilled information
technologists, among whom are statisticians. This paper focuses on some of the
contributions that statisticians are making to help change the business world,
especially through the development and application of data mining methods. This
is a very large area, and the topics we cover are chosen to avoid overlap with
other papers in this special issue, as well as to respect the limitations of
our expertise. Inevitably, electronic commerce has raised and is raising fresh
research problems in a very wide range of statistical areas, and we try to
emphasize those challenges.Comment: Published at http://dx.doi.org/10.1214/088342306000000204 in the
Statistical Science (http://www.imstat.org/sts/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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