13,360 research outputs found
A Cognitive Framework to Secure Smart Cities
The advancement in technology has transformed Cyber Physical Systems and their interface with IoT into a more sophisticated and challenging paradigm. As a result, vulnerabilities and potential attacks manifest themselves considerably more than before, forcing researchers to rethink the conventional strategies that are currently in place to secure such physical systems. This manuscript studies the complex interweaving of sensor networks and physical systems and suggests a foundational innovation in the field. In sharp contrast with the existing IDS and IPS solutions, in this paper, a preventive and proactive method is employed to stay ahead of attacks by constantly monitoring network data patterns and identifying threats that are imminent. Here, by capitalizing on the significant progress in processing power (e.g. petascale computing) and storage capacity of computer systems, we propose a deep learning approach to predict and identify various security breaches that are about to occur. The learning process takes place by collecting a large number of files of different types and running tests on them to classify them as benign or malicious. The prediction model obtained as such can then be used to identify attacks. Our project articulates a new framework for interactions between physical systems and sensor networks, where malicious packets are repeatedly learned over time while the system continually operates with respect to imperfect security mechanisms
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
An Empirical Study on Android-related Vulnerabilities
Mobile devices are used more and more in everyday life. They are our cameras,
wallets, and keys. Basically, they embed most of our private information in our
pocket. For this and other reasons, mobile devices, and in particular the
software that runs on them, are considered first-class citizens in the
software-vulnerabilities landscape. Several studies investigated the
software-vulnerabilities phenomenon in the context of mobile apps and, more in
general, mobile devices. Most of these studies focused on vulnerabilities that
could affect mobile apps, while just few investigated vulnerabilities affecting
the underlying platform on which mobile apps run: the Operating System (OS).
Also, these studies have been run on a very limited set of vulnerabilities.
In this paper we present the largest study at date investigating
Android-related vulnerabilities, with a specific focus on the ones affecting
the Android OS. In particular, we (i) define a detailed taxonomy of the types
of Android-related vulnerability; (ii) investigate the layers and subsystems
from the Android OS affected by vulnerabilities; and (iii) study the
survivability of vulnerabilities (i.e., the number of days between the
vulnerability introduction and its fixing). Our findings could help OS and apps
developers in focusing their verification & validation activities, and
researchers in building vulnerability detection tools tailored for the mobile
world
Towards a relation extraction framework for cyber-security concepts
In order to assist security analysts in obtaining information pertaining to
their network, such as novel vulnerabilities, exploits, or patches, information
retrieval methods tailored to the security domain are needed. As labeled text
data is scarce and expensive, we follow developments in semi-supervised Natural
Language Processing and implement a bootstrapping algorithm for extracting
security entities and their relationships from text. The algorithm requires
little input data, specifically, a few relations or patterns (heuristics for
identifying relations), and incorporates an active learning component which
queries the user on the most important decisions to prevent drifting from the
desired relations. Preliminary testing on a small corpus shows promising
results, obtaining precision of .82.Comment: 4 pages in Cyber & Information Security Research Conference 2015, AC
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