113,355 research outputs found
Machine-assisted Cyber Threat Analysis using Conceptual Knowledge Discovery
Over the last years, computer networks have evolved into highly dynamic and interconnected environments, involving multiple heterogeneous devices and providing a myriad of services on top of them. This complex landscape has made it extremely difficult for security administrators to keep accurate and be effective in protecting their systems against cyber threats. In this paper, we describe our vision and scientific posture on how artificial intelligence techniques and a smart use of security knowledge may assist system administrators in better defending their networks. To that end, we put forward a research roadmap involving three complimentary axes, namely, (I) the use of FCA-based mechanisms for managing configuration vulnerabilities, (II) the exploitation of knowledge representation techniques for automated security reasoning, and (III) the design of a cyber threat intelligence mechanism as a CKDD process. Then, we describe a machine-assisted process for cyber threat analysis which provides a holistic perspective of how these three research axes are integrated together
Efficient Passive ICS Device Discovery and Identification by MAC Address Correlation
Owing to a growing number of attacks, the assessment of Industrial Control
Systems (ICSs) has gained in importance. An integral part of an assessment is
the creation of a detailed inventory of all connected devices, enabling
vulnerability evaluations. For this purpose, scans of networks are crucial.
Active scanning, which generates irregular traffic, is a method to get an
overview of connected and active devices. Since such additional traffic may
lead to an unexpected behavior of devices, active scanning methods should be
avoided in critical infrastructure networks. In such cases, passive network
monitoring offers an alternative, which is often used in conjunction with
complex deep-packet inspection techniques. There are very few publications on
lightweight passive scanning methodologies for industrial networks. In this
paper, we propose a lightweight passive network monitoring technique using an
efficient Media Access Control (MAC) address-based identification of industrial
devices. Based on an incomplete set of known MAC address to device
associations, the presented method can guess correct device and vendor
information. Proving the feasibility of the method, an implementation is also
introduced and evaluated regarding its efficiency. The feasibility of
predicting a specific device/vendor combination is demonstrated by having
similar devices in the database. In our ICS testbed, we reached a host
discovery rate of 100% at an identification rate of more than 66%,
outperforming the results of existing tools.Comment: http://dx.doi.org/10.14236/ewic/ICS2018.
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
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