2,829 research outputs found
One-Class Classification: Taxonomy of Study and Review of Techniques
One-class classification (OCC) algorithms aim to build classification models
when the negative class is either absent, poorly sampled or not well defined.
This unique situation constrains the learning of efficient classifiers by
defining class boundary just with the knowledge of positive class. The OCC
problem has been considered and applied under many research themes, such as
outlier/novelty detection and concept learning. In this paper we present a
unified view of the general problem of OCC by presenting a taxonomy of study
for OCC problems, which is based on the availability of training data,
algorithms used and the application domains applied. We further delve into each
of the categories of the proposed taxonomy and present a comprehensive
literature review of the OCC algorithms, techniques and methodologies with a
focus on their significance, limitations and applications. We conclude our
paper by discussing some open research problems in the field of OCC and present
our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure
Game Theory Meets Network Security: A Tutorial at ACM CCS
The increasingly pervasive connectivity of today's information systems brings
up new challenges to security. Traditional security has accomplished a long way
toward protecting well-defined goals such as confidentiality, integrity,
availability, and authenticity. However, with the growing sophistication of the
attacks and the complexity of the system, the protection using traditional
methods could be cost-prohibitive. A new perspective and a new theoretical
foundation are needed to understand security from a strategic and
decision-making perspective. Game theory provides a natural framework to
capture the adversarial and defensive interactions between an attacker and a
defender. It provides a quantitative assessment of security, prediction of
security outcomes, and a mechanism design tool that can enable
security-by-design and reverse the attacker's advantage. This tutorial provides
an overview of diverse methodologies from game theory that includes games of
incomplete information, dynamic games, mechanism design theory to offer a
modern theoretic underpinning of a science of cybersecurity. The tutorial will
also discuss open problems and research challenges that the CCS community can
address and contribute with an objective to build a multidisciplinary bridge
between cybersecurity, economics, game and decision theory
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Byzantine Attack and Defense in Cognitive Radio Networks: A Survey
The Byzantine attack in cooperative spectrum sensing (CSS), also known as the
spectrum sensing data falsification (SSDF) attack in the literature, is one of
the key adversaries to the success of cognitive radio networks (CRNs). In the
past couple of years, the research on the Byzantine attack and defense
strategies has gained worldwide increasing attention. In this paper, we provide
a comprehensive survey and tutorial on the recent advances in the Byzantine
attack and defense for CSS in CRNs. Specifically, we first briefly present the
preliminaries of CSS for general readers, including signal detection
techniques, hypothesis testing, and data fusion. Second, we analyze the spear
and shield relation between Byzantine attack and defense from three aspects:
the vulnerability of CSS to attack, the obstacles in CSS to defense, and the
games between attack and defense. Then, we propose a taxonomy of the existing
Byzantine attack behaviors and elaborate on the corresponding attack
parameters, which determine where, who, how, and when to launch attacks. Next,
from the perspectives of homogeneous or heterogeneous scenarios, we classify
the existing defense algorithms, and provide an in-depth tutorial on the
state-of-the-art Byzantine defense schemes, commonly known as robust or secure
CSS in the literature. Furthermore, we highlight the unsolved research
challenges and depict the future research directions.Comment: Accepted by IEEE Communications Surveys and Tutoiral
A lightweight intrusion alert fusion system
In this paper, we present some practical experience on implementing an alert fusion mechanism from our project. After investigation on most of the existing alert fusion systems, we found the current body of work alternatively weighed down in the mire of insecure design or rarely deployed because of their complexity. As confirmed by our experimental analysis, unsuitable mechanisms could easily be submerged by an abundance of useless alerts. Even with the use of methods that achieve a high fusion rate and low false positives, attack is also possible. To find the solution, we carried out analysis on a series of alerts generated by well-known datasets as well as realistic alerts from the Australian Honey-Pot. One important finding is that one alert has more than an 85% chance of being fused in the following 5 alerts. Of particular importance is our design of a novel lightweight Cache-based Alert Fusion Scheme, called CAFS. CAFS has the capacity to not only reduce the quantity of useless alerts generated by IDS (Intrusion Detection System), but also enhance the accuracy of alerts, therefore greatly reducing the cost of fusion processing. We also present reasonable and practical specifications for the target-oriented fusion policy that provides a quality guarantee on alert fusion, and as a result seamlessly satisfies the process of successive correlation. Our experimental results showed that the CAFS easily attained the desired level of survivable, inescapable alert fusion design. Furthermore, as a lightweight scheme, CAFS can easily be deployed and excel in a large amount of alert fusions, which go towards improving the usability of system resources. To the best of our knowledge, our work is a novel exploration in addressing these problems from a survivable, inescapable and deployable point of view
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