28,541 research outputs found
ATTACK2VEC: Leveraging Temporal Word Embeddings to Understand the Evolution of Cyberattacks
Despite the fact that cyberattacks are constantly growing in complexity, the
research community still lacks effective tools to easily monitor and understand
them. In particular, there is a need for techniques that are able to not only
track how prominently certain malicious actions, such as the exploitation of
specific vulnerabilities, are exploited in the wild, but also (and more
importantly) how these malicious actions factor in as attack steps in more
complex cyberattacks. In this paper we present ATTACK2VEC, a system that uses
temporal word embeddings to model how attack steps are exploited in the wild,
and track how they evolve. We test ATTACK2VEC on a dataset of billions of
security events collected from the customers of a commercial Intrusion
Prevention System over a period of two years, and show that our approach is
effective in monitoring the emergence of new attack strategies in the wild and
in flagging which attack steps are often used together by attackers (e.g.,
vulnerabilities that are frequently exploited together). ATTACK2VEC provides a
useful tool for researchers and practitioners to better understand cyberattacks
and their evolution, and use this knowledge to improve situational awareness
and develop proactive defenses
Support Vector Machines in High Energy Physics
This lecture will introduce the Support Vector algorithms for classification
and regression. They are an application of the so called kernel trick, which
allows the extension of a certain class of linear algorithms to the non linear
case. The kernel trick will be introduced and in the context of structural risk
minimization, large margin algorithms for classification and regression will be
presented. Current applications in high energy physics will be discussed.Comment: 11 pages, 12 figures. Part of the proceedings of the Track
'Computational Intelligence for HEP Data Analysis' at iCSC 200
Information Extraction, Data Integration, and Uncertain Data Management: The State of The Art
Information Extraction, data Integration, and uncertain data management are different areas of research that got vast focus in the last two decades. Many researches tackled those areas of research individually. However, information extraction systems should have integrated with data integration methods to make use of the extracted information. Handling uncertainty in extraction and integration process is an important issue to enhance the quality of the data in such integrated systems. This article presents the state of the art of the mentioned areas of research and shows the common grounds and how to integrate information extraction and data integration under uncertainty management cover
Outlier detection techniques for wireless sensor networks: A survey
In the field of wireless sensor networks, those measurements that significantly deviate from the normal pattern of sensed data are considered as outliers. The potential sources of outliers include noise and errors, events, and malicious attacks on the network. Traditional outlier detection techniques are not directly applicable to wireless sensor networks due to the nature of sensor data and specific requirements and limitations of the wireless sensor networks. This survey provides a comprehensive overview of existing outlier detection techniques specifically developed for the wireless sensor networks. Additionally, it presents a technique-based taxonomy and a comparative table to be used as a guideline to select a technique suitable for the application at hand based on characteristics such as data type, outlier type, outlier identity, and outlier degree
Attack2vec: Leveraging temporal word embeddings to understand the evolution of cyberattacks
Despite the fact that cyberattacks are constantly growing in complexity, the research community still lacks effective tools to easily monitor and understand them. In particular, there is a need for techniques that are able to not only track how prominently certain malicious actions, such as the exploitation of specific vulnerabilities, are exploited in the wild, but also (and more importantly) how these malicious actions factor in as attack steps in more complex cyberattacks. In this paper we present ATTACK2VEC, a system that uses temporal word embeddings to model how attack steps are exploited in the wild, and track how they evolve. We test ATTACK2VEC on a dataset
of billions of security events collected from the customers of a commercial Intrusion Prevention System over a period of two years, and show that our approach is effective in monitoring the emergence of new attack strategies in the wild and in flagging which attack steps are often used together by attackers (e.g., vulnerabilities that are frequently exploited together). ATTACK2VEC provides a useful tool for researchers and practitioners to better
understand cyberattacks and their evolution, and use this knowledge to improve situational awareness and develop proactive defenses.Accepted manuscrip
Construction of embedded fMRI resting state functional connectivity networks using manifold learning
We construct embedded functional connectivity networks (FCN) from benchmark
resting-state functional magnetic resonance imaging (rsfMRI) data acquired from
patients with schizophrenia and healthy controls based on linear and nonlinear
manifold learning algorithms, namely, Multidimensional Scaling (MDS), Isometric
Feature Mapping (ISOMAP) and Diffusion Maps. Furthermore, based on key global
graph-theoretical properties of the embedded FCN, we compare their
classification potential using machine learning techniques. We also assess the
performance of two metrics that are widely used for the construction of FCN
from fMRI, namely the Euclidean distance and the lagged cross-correlation
metric. We show that the FCN constructed with Diffusion Maps and the lagged
cross-correlation metric outperform the other combinations
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