74,496 research outputs found
Algorithm Selection Framework for Cyber Attack Detection
The number of cyber threats against both wired and wireless computer systems
and other components of the Internet of Things continues to increase annually.
In this work, an algorithm selection framework is employed on the NSL-KDD data
set and a novel paradigm of machine learning taxonomy is presented. The
framework uses a combination of user input and meta-features to select the best
algorithm to detect cyber attacks on a network. Performance is compared between
a rule-of-thumb strategy and a meta-learning strategy. The framework removes
the conjecture of the common trial-and-error algorithm selection method. The
framework recommends five algorithms from the taxonomy. Both strategies
recommend a high-performing algorithm, though not the best performing. The work
demonstrates the close connectedness between algorithm selection and the
taxonomy for which it is premised.Comment: 6 pages, 7 figures, 1 table, accepted to WiseML '2
Classification of logical vulnerability based on group attacking method
New advancement in the field of e-commerce software technology has also brought many benefits, at the same time developing process always face different sort of problems from design phase to implement phase. Software faults and defects increases the issues of reliability and security, that’s reason why a solution of this problem is required to fortify these issues. The paper addresses the problem associated with lack of clear component-based web application related classification of logical vulnerabilities through identifying Attack Group Method by categorizing two different types of vulnerabilities in component-based web applications. A new classification scheme of logical group attack method is proposed and developed by using a Posteriori Empirically methodology
FraudDroid: Automated Ad Fraud Detection for Android Apps
Although mobile ad frauds have been widespread, state-of-the-art approaches
in the literature have mainly focused on detecting the so-called static
placement frauds, where only a single UI state is involved and can be
identified based on static information such as the size or location of ad
views. Other types of fraud exist that involve multiple UI states and are
performed dynamically while users interact with the app. Such dynamic
interaction frauds, although now widely spread in apps, have not yet been
explored nor addressed in the literature. In this work, we investigate a wide
range of mobile ad frauds to provide a comprehensive taxonomy to the research
community. We then propose, FraudDroid, a novel hybrid approach to detect ad
frauds in mobile Android apps. FraudDroid analyses apps dynamically to build UI
state transition graphs and collects their associated runtime network traffics,
which are then leveraged to check against a set of heuristic-based rules for
identifying ad fraudulent behaviours. We show empirically that FraudDroid
detects ad frauds with a high precision (93%) and recall (92%). Experimental
results further show that FraudDroid is capable of detecting ad frauds across
the spectrum of fraud types. By analysing 12,000 ad-supported Android apps,
FraudDroid identified 335 cases of fraud associated with 20 ad networks that
are further confirmed to be true positive results and are shared with our
fellow researchers to promote advanced ad fraud detectionComment: 12 pages, 10 figure
Predicting Network Attacks Using Ontology-Driven Inference
Graph knowledge models and ontologies are very powerful modeling and re
asoning tools. We propose an effective approach to model network attacks and
attack prediction which plays important roles in security management. The goals
of this study are: First we model network attacks, their prerequisites and
consequences using knowledge representation methods in order to provide
description logic reasoning and inference over attack domain concepts. And
secondly, we propose an ontology-based system which predicts potential attacks
using inference and observing information which provided by sensory inputs. We
generate our ontology and evaluate corresponding methods using CAPEC, CWE, and
CVE hierarchical datasets. Results from experiments show significant capability
improvements comparing to traditional hierarchical and relational models.
Proposed method also reduces false alarms and improves intrusion detection
effectiveness.Comment: 9 page
Comprehensive Security Framework for Global Threats Analysis
Cyber criminality activities are changing and becoming more and more professional. With the growth of financial flows through the Internet and the Information System (IS), new kinds of thread arise involving complex scenarios spread within multiple IS components. The IS information modeling and Behavioral Analysis are becoming new solutions to normalize the IS information and counter these new threads. This paper presents a framework which details the principal and necessary steps for monitoring an IS. We present the architecture of the framework, i.e. an ontology of activities carried out within an IS to model security information and User Behavioral analysis. The results of the performed experiments on real data show that the modeling is effective to reduce the amount of events by 91%. The User Behavioral Analysis on uniform modeled data is also effective, detecting more than 80% of legitimate actions of attack scenarios
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