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Additionally, we could find no studies that incorporated distributed or streaming implementations for learning from Big Data into their spam detection frameworks.
The simulation performed using NS-3 simulator shows, AECFV exhibits a high detection rate, low false positive rate, faster attack detection, and lower communication overhead compared to current detection frameworks.
We argue that in order to explore combinatorial signal detection in its full potential, semantically-enriched detection frameworks are required to overcome existing barriers.
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In this paper, a novel jammer detection framework to detect the intrusion of jammer and the presence of jamming in a cluster-based wireless sensor network is proposed.
In this paper, a novel jammer detection framework (which detects the intrusion of jammer and various types of jamming attacks) is proposed and evaluated.
A novel jammer detection framework is proposed to detect the presence of jammer and jamming in the cluster-based wireless sensor networks.
The novel jammer detection framework is proposed to detect the intrusion of jammer and the presence of jamming in the cluster-based wireless sensor network.
Based on this ground truth, Cao and Kot [23] proposed an ensemble manipulation detection framework (i.e., FusionBoost) to detect a range of manipulations on local image patches.
Jammer detection framework (Section 3): The jammer detection framework (JDF) detects the intrusion of the jammer and various types of jamming attacks.
Second, Farooqi et al. [13] proposed the novel intrusion detection framework, which applied to the distributed environment for detecting intrusion.
Table 1 Types of jamming Types of jammer Average PDR (in %) PDR range (in %) RSSI (in db) Constant 9.76 0–10 −93.141 Deceptive 28.81 25.6 53.5 −93.141 Random 57.9 53.6–73.75 −93.141 Reactive 22.21 11 25.5 −93.141 Jammer detection framework (Section 3): The jammer detection framework (JDF) detects the intrusion of the jammer and various types of jamming attacks.
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