7,702 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
Transportation, Terrorism and Crime: Deterrence, Disruption and Resilience
Abstract: Terrorists likely have adopted vehicle ramming as a tactic because it can be carried out by an individual (or “lone wolf terrorist”), and because the skills required are minimal (e.g. the ability to drive a car and determine locations for creating maximum carnage). Studies of terrorist activities against transportation assets have been conducted to help law enforcement agencies prepare their communities, create mitigation measures, conduct effective surveillance and respond quickly to attacks.
This study reviews current research on terrorist tactics against transportation assets, with an emphasis on vehicle ramming attacks. It evaluates some of the current attack strategies, and the possible mitigation or response tactics that may be effective in deterring attacks or saving lives in the event of an attack. It includes case studies that can be used as educational tools for understanding terrorist methodologies, as well as ordinary emergencies that might become a terrorist’s blueprint
Protecting infrastructure networks from cost-based attacks
It has been known that heterogeneous networks are vulnerable to the
intentional removal of a small fraction of highly connected or loaded nodes,
which implies that, to protect a network effectively, a few important nodes
should be allocated with more defense resources than the others. However, if
too many resources are allocated to the few important nodes, the numerous
less-important nodes will be less protected, which, when attacked all together,
still capable of causing a devastating damage. A natural question therefore is
how to efficiently distribute the limited defense resources among the network
nodes such that the network damage is minimized whatever attack strategy the
attacker may take. In this paper, taking into account the factor of attack
cost, we will revisit the problem of network security and search for efficient
network defense against the cost-based attacks. The study shows that, for a
general complex network, there will exist an optimal distribution of the
defense resources, with which the network is well protected from cost-based
attacks. Furthermore, it is found that the configuration of the optimal defense
is dependent on the network parameters. Specifically, network that has a larger
size, sparser connection and more heterogeneous structure will be more
benefited from the defense optimization.Comment: 5 pages, 4 figure
Nuclear Weapons and the Militarization of AI
This contribution provides an overview of nuclear risks emerging from the militarization of AI technologies and systems. These include AI enhancements of cyber threats to nuclear command, control and communication infrastructures, proposed uses of AI systems affected by inherent vulnerabilities in nuclear early warning, AI-powered unmanned vessels trailing submarines armed with nuclear ballistic missiles. Taken together, nuclear risks emerging from the militarization of AI add new significant motives for nuclear non-proliferation and disarmament
Detecting Backdoors in Neural Networks Using Novel Feature-Based Anomaly Detection
This paper proposes a new defense against neural network backdooring attacks
that are maliciously trained to mispredict in the presence of attacker-chosen
triggers. Our defense is based on the intuition that the feature extraction
layers of a backdoored network embed new features to detect the presence of a
trigger and the subsequent classification layers learn to mispredict when
triggers are detected. Therefore, to detect backdoors, the proposed defense
uses two synergistic anomaly detectors trained on clean validation data: the
first is a novelty detector that checks for anomalous features, while the
second detects anomalous mappings from features to outputs by comparing with a
separate classifier trained on validation data. The approach is evaluated on a
wide range of backdoored networks (with multiple variations of triggers) that
successfully evade state-of-the-art defenses. Additionally, we evaluate the
robustness of our approach on imperceptible perturbations, scalability on
large-scale datasets, and effectiveness under domain shift. This paper also
shows that the defense can be further improved using data augmentation
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