65,142 research outputs found
Security Guidelines for the Development of Accessible Web Applications through the implementation of intelligent systems
Due to the significant increase in threats, attacks
and vulnerabilities that affect the Web in recent years has
resulted the development and implementation of tools and
methods to ensure security measures in the privacy,
confidentiality and data integrity of users and businesses. Under
certain circumstances, despite the implementation of these tools
do not always get the flow of information which is passed in a
secure manner. Many of these security tools and methods cannot
be accessed by people who have disabilities or assistive
technologies which enable people to access the Web efficiently.
Among these security tools that are not accessible are the virtual
keyboard, the CAPTCHA and other technologies that help to
some extent to ensure safety on the Internet and are used in
certain measures to combat malicious code and attacks that have
been increased in recent times on the Web. Through the
implementation of intelligent systems can detect, recover and
receive information on the characteristics and properties of the
different tools and hardware devices or software with which the
user is accessing a web application and through analysis and
interpretation of these intelligent systems can infer and
automatically adjust the characteristics necessary to have these
tools to be accessible by anyone regardless of disability or
navigation context. This paper defines a set of guidelines and
specific features that should have the security tools and methods
to ensure the Web accessibility through the implementation of
intelligent systems
Detection of Early-Stage Enterprise Infection by Mining Large-Scale Log Data
Recent years have seen the rise of more sophisticated attacks including
advanced persistent threats (APTs) which pose severe risks to organizations and
governments by targeting confidential proprietary information. Additionally,
new malware strains are appearing at a higher rate than ever before. Since many
of these malware are designed to evade existing security products, traditional
defenses deployed by most enterprises today, e.g., anti-virus, firewalls,
intrusion detection systems, often fail at detecting infections at an early
stage.
We address the problem of detecting early-stage infection in an enterprise
setting by proposing a new framework based on belief propagation inspired from
graph theory. Belief propagation can be used either with "seeds" of compromised
hosts or malicious domains (provided by the enterprise security operation
center -- SOC) or without any seeds. In the latter case we develop a detector
of C&C communication particularly tailored to enterprises which can detect a
stealthy compromise of only a single host communicating with the C&C server.
We demonstrate that our techniques perform well on detecting enterprise
infections. We achieve high accuracy with low false detection and false
negative rates on two months of anonymized DNS logs released by Los Alamos
National Lab (LANL), which include APT infection attacks simulated by LANL
domain experts. We also apply our algorithms to 38TB of real-world web proxy
logs collected at the border of a large enterprise. Through careful manual
investigation in collaboration with the enterprise SOC, we show that our
techniques identified hundreds of malicious domains overlooked by
state-of-the-art security products
Tree-based Intelligent Intrusion Detection System in Internet of Vehicles
The use of autonomous vehicles (AVs) is a promising technology in Intelligent
Transportation Systems (ITSs) to improve safety and driving efficiency.
Vehicle-to-everything (V2X) technology enables communication among vehicles and
other infrastructures. However, AVs and Internet of Vehicles (IoV) are
vulnerable to different types of cyber-attacks such as denial of service,
spoofing, and sniffing attacks. In this paper, an intelligent intrusion
detection system (IDS) is proposed based on tree-structure machine learning
models. The results from the implementation of the proposed intrusion detection
system on standard data sets indicate that the system has the ability to
identify various cyber-attacks in the AV networks. Furthermore, the proposed
ensemble learning and feature selection approaches enable the proposed system
to achieve high detection rate and low computational cost simultaneously.Comment: Accepted in IEEE Global Communications Conference (GLOBECOM) 201
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