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PE-Miner: Mining Structural Information to Detect Malicious Executables in Realtime
TLDR
In this paper, we present an accurate and realtime PE-Miner framework that automatically extracts distinguishing features from portable executables (PE) to detect zero-day (i.e. previously unknown) malware. Expand
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PE-Probe: Leveraging Packer Detection and Structural Information to Detect Malicious Portable Executables
TLDR
We present a novel scheme ‐ PE-Probe ‐ which has the ability to detect packed files and uses structural information of portable executables to detect zero-day (i.e. previously unseen) malicious executables. Expand
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Malware detection using statistical analysis of byte-level file content
TLDR
We propose a novel malware detection technique which is based on the analysis of byte-level file content. Expand
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A Sense of 'Danger' for Windows Processes
TLDR
We investigate the suitability of recently proposed Dendritic Cell Algorithms (DCA), both classical DCA (cDCA) and deterministic DCA, for malware detection at run-time. Expand
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A Framework for Efficient Mining of Structural Information to Detect Zero-Day Malicious Portable Executables
TLDR
An exercising device for simulating hula-hoop rotations includes: a housing having at least a track annularly formed in the housing, a ball rolling or rotably moving in the track upon a hulahoop rotation by a user who wears the housing of the exercising device, and an audio and visual device provided on the housing to indicate or display the whirling movements audiovisually. Expand
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On the appropriateness of evolutionary rule learning algorithms for malware detection
TLDR
We compare the performance of ten well-known evolutionary and non-evolutionary rule learning algorithms with respect to four metrics: (1) classification accuracy, (2) number of rules in the developed rule set, (3) the comprehensibility of the generated rules, and (4) the processing overhead of the rule learning process. Expand
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Are evolutionary rule learning algorithms appropriate for malware detection?
TLDR
In this paper, we evaluate the performance of ten well-known evolutionary and non-evolutionary rule learning algorithms with respect to four metrics: classification accuracy, number of rules in the developed rule set, comprehensibility of the generated rules, and processing overhead of the rule learning process. Expand
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PE-Miner: Realtime Mining of 'Structural Information' to Detect Zero-Day Malicious Portable Executables
TLDR
In this paper, we present an accurate and realtime PE-Miner framework that automatically extracts distinguishing features from portable executables (PE) to detect zero-day malware without any a priori knowledge about them. Expand
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Embedded Malware Detection using Markovian Statistical Model of Benign Files
TLDR
A method for promoting bone growth in a patient (e.g., a mammal such as a human) said method including the step of administering a therapeutically effective amount of adrenomedullin or an adrenomedullain agonist to said patient. Expand
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Throughput Quantification of MIMO based Correlated Rician fading Channel for a LTE downlink system
A MIMO system can offer two types of gains i.e. spatial multiplexing (increase data rate) and diversity gain. However, these benefits of MIMO systems depend crucially on the kind of fading theExpand
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