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Found 8 papers, 1 papers with code
Multi-Agent Context Learning Strategy for Interference-Aware Beam Allocation in mmWave Vehicular Communications
The use of mmWave for various applications including vehicular communications has been extensively discussed.
SETTI: A Self-supervised Adversarial Malware Detection Architecture in an IoT Environment
In the SETTI architecture, we design three self-supervised attack techniques, namely Self-MDS, GSelf-MDS and ASelf-MDS.
Deep Image: A precious image based deep learning method for online malware detection in IoT Environment
In this way, a criterion is introduced that is used together with accuracy and FPR criteria for malware analysis in IoT environment.
Similarity-based Android Malware Detection Using Hamming Distance of Static Binary Features
We test our experiments in a different type of features: API, intent, and permission features on these three datasets.
On Defending Against Label Flipping Attacks on Malware Detection Systems
Our evaluation shows that using random forest feature selection and varying ratios of features can result in an improvement of up to 19\% accuracy when compared with the state-of-the-art method in the literature.
Can Machine Learning Model with Static Features be Fooled: an Adversarial Machine Learning Approach
We also test our methods using various classifier algorithms and compare them with the state-of-the-art data poisoning method using the Jacobian matrix.
FeatureAnalytics: An approach to derive relevant attributes for analyzing Android Malware
Ever increasing number of Android malware, has always been a concern for cybersecurity professionals.
Hybrid Genetic Algorithm for Cloud Computing Applications
In this paper with the aid of genetic algorithm and fuzzy theory, we present a hybrid job scheduling approach, which considers the load balancing of the system and reduces total execution time and execution cost.
