Results 51 to 60 of about 165,511 (160)
Multi-type low-rate DDoS attack detection method based on hybrid deep learning
Low-Rate distributed denial of service (DDoS) attack attacks the vulnerabilities in the adaptive mechanism of network protocols, posing a huge threat to the quality of network services.Low-Rate DDoS attack was characterized by high secrecy, low attack ...
Lijuan LI +3 more
doaj
RedHerring Attack: Testing the Reliability of Attack Detection
In response to adversarial text attacks, attack detection models have been proposed and shown to successfully identify text modified by adversaries. Attack detection models can be leveraged to provide an additional check for NLP models and give signals for human input. However, the reliability of these models has not yet been thoroughly explored. Thus,
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Attack Detection in Wireless Localization
Accurately positioning nodes in wireless and sensor networks is important because the location of sensors is a critical input to many higher-level networking tasks. However, the localization infrastructure can be subjected to non-cryptographic attacks, such as signal attenuation and amplification, that cannot be addressed by traditional security ...
Yingying Chen 0001 +2 more
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Attack detection model for BCoT based on contrastive variational autoencoder and metric learning
With development of blockchain technology, clouding computing and Internet of Things (IoT), blockchain and cloud of things (BCoT) has become development tendency. But the security has become the most development hinder of BCoT.
Chunwang Wu +6 more
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Research on low-rate DDoS attack of SDN network in cloud environment
Aiming at the problems of low-rate DDoS attack detection accuracy in cloud SDN network and the lack of unified framework for data plane and control plane low-rate DDoS attack detection and defense,a unified framework for low-rate DDoS attack detection ...
Xingshu CHEN +4 more
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Image preprocessing models are usually employed as the preceding operations of high‐level vision tasks to improve the performance. The adversarial attack technology makes both these models face severe challenges.
Xueshuai Gao +6 more
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Detection and Prevention of Smishing Attacks
Phishing is an online identity theft technique where attackers steal users personal information, leading to financial losses for individuals and organizations. With the increasing adoption of smartphones, which provide functionalities similar to desktop computers, attackers are targeting mobile users.
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Adversarial Attacks for Drift Detection
Concept drift refers to the change of data distributions over time. While drift poses a challenge for learning models, requiring their continual adaption, it is also relevant in system monitoring to detect malfunctions, system failures, and unexpected behavior. In the latter case, the robust and reliable detection of drifts is imperative.
Hinder, Fabian +2 more
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The expansion of operation technology (OT) use and its tight integration with classical information and communication technologies have led not only to additional and improved possibilities in monitoring physical/manufacturing processes and the emergency
Nikolaj Goranin +2 more
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Phasor measurement unit (PMU) plays a crucial role in smart grids, enabling precise synchronized acquisition of electric power data. Due to the use of the global positioning system (GPS) for time synchronization, the PMU is vulnerable to GPS spoofing ...
Hui WU +5 more
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