704 research outputs found
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
Cyber-Physical Security Strategies
Cyber-physical security describes the protection of systems with close relationships between computational functions and physical ones and addresses the issue of vulnerability to attack through both cyber and physical avenues. This describes systems in a wide variety of functions, many crucial to the function of modern society, making their security of paramount importance. The development of secure system design and attack detection strategies for each potential avenue of attack is needed to combat malicious attacks. This thesis will provide an overview of the approaches to securing different aspect of cyber-physical systems. The cyber element can be designed to better prevent unauthorized entry and to be more robust to attack while its use is evaluated for signs of ongoing intrusion. Nodes in sensor networks can be evaluated by their claims to determine the likelihood of their honesty. Control systems can be designed to be robust in cases of the failure of one component and to detect signal insertion or replay attack. Through the application of these strategies, the safety and continued function of cyber-physical systems can be improved
Design, Implementation and Experiments for Moving Target Defense Framework
The traditional defensive security strategy for distributed systems employs well-established defensive techniques such as; redundancy/replications, firewalls, and encryption to prevent attackers from taking control of the system. However, given sufficient time and resources, all these methods can be defeated, especially when dealing with sophisticated attacks from advanced adversaries that leverage zero-day exploits
A consensus based network intrusion detection system
Network intrusion detection is the process of identifying malicious behaviors
that target a network and its resources. Current systems implementing intrusion
detection processes observe traffic at several data collecting points in the
network but analysis is often centralized or partly centralized. These systems
are not scalable and suffer from the single point of failure, i.e. attackers
only need to target the central node to compromise the whole system. This paper
proposes an anomaly-based fully distributed network intrusion detection system
where analysis is run at each data collecting point using a naive Bayes
classifier. Probability values computed by each classifier are shared among
nodes using an iterative average consensus protocol. The final analysis is
performed redundantly and in parallel at the level of each data collecting
point, thus avoiding the single point of failure issue. We run simulations
focusing on DDoS attacks with several network configurations, comparing the
accuracy of our fully distributed system with a hierarchical one. We also
analyze communication costs and convergence speed during consensus phases.Comment: Presented at THE 5TH INTERNATIONAL CONFERENCE ON IT CONVERGENCE AND
SECURITY 2015 IN KUALA LUMPUR, MALAYSI
Intrusion Tolerant Routing Protocols for Wireless Sensor Networks
This MSc thesis is focused in the study, solution proposal and experimental evaluation of security solutions for Wireless Sensor Networks (WSNs). The objectives are centered on intrusion tolerant routing services, adapted for the characteristics and requirements of WSN nodes and operation behavior.
The main contribution addresses the establishment of pro-active intrusion tolerance properties at the network level, as security mechanisms for the proposal of a reliable and secure routing protocol. Those properties and mechanisms will augment a secure communication base layer supported by light-weigh cryptography methods, to improve the global network resilience capabilities against possible intrusion-attacks on the WSN nodes. Adapting to WSN characteristics, the design of the intended security services also pushes complexity away from resource-poor sensor nodes towards resource-rich and trustable base stations.
The devised solution will construct, securely and efficiently, a secure tree-structured routing service for data-dissemination in large scale deployed WSNs. The purpose is to tolerate the damage caused by adversaries modeled according with the Dolev-Yao threat model and ISO X.800 attack typology and framework, or intruders that can compromise maliciously the deployed sensor nodes, injecting, modifying, or blocking packets, jeopardizing the correct behavior of internal network routing processing and topology management.
The proposed enhanced mechanisms, as well as the design and implementation of a new intrusiontolerant routing protocol for a large scale WSN are evaluated by simulation. For this purpose, the evaluation is based on a rich simulation environment, modeling networks from hundreds to tens of thousands of wireless sensors, analyzing different dimensions: connectivity conditions, degree-distribution patterns, latency and average short-paths, clustering, reliability metrics and energy cost
Edge Learning for 6G-enabled Internet of Things: A Comprehensive Survey of Vulnerabilities, Datasets, and Defenses
The ongoing deployment of the fifth generation (5G) wireless networks
constantly reveals limitations concerning its original concept as a key driver
of Internet of Everything (IoE) applications. These 5G challenges are behind
worldwide efforts to enable future networks, such as sixth generation (6G)
networks, to efficiently support sophisticated applications ranging from
autonomous driving capabilities to the Metaverse. Edge learning is a new and
powerful approach to training models across distributed clients while
protecting the privacy of their data. This approach is expected to be embedded
within future network infrastructures, including 6G, to solve challenging
problems such as resource management and behavior prediction. This survey
article provides a holistic review of the most recent research focused on edge
learning vulnerabilities and defenses for 6G-enabled IoT. We summarize the
existing surveys on machine learning for 6G IoT security and machine
learning-associated threats in three different learning modes: centralized,
federated, and distributed. Then, we provide an overview of enabling emerging
technologies for 6G IoT intelligence. Moreover, we provide a holistic survey of
existing research on attacks against machine learning and classify threat
models into eight categories, including backdoor attacks, adversarial examples,
combined attacks, poisoning attacks, Sybil attacks, byzantine attacks,
inference attacks, and dropping attacks. In addition, we provide a
comprehensive and detailed taxonomy and a side-by-side comparison of the
state-of-the-art defense methods against edge learning vulnerabilities.
Finally, as new attacks and defense technologies are realized, new research and
future overall prospects for 6G-enabled IoT are discussed
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