AN EVALUATION OF PARALLEL STRATEGIES FOR FEATURE VECTOR CONSTRUCTION IN AUTOMATIC SIGNATURE VERIFICATION SYSTEMS

1994 ◽  
Vol 08 (03) ◽  
pp. 661-678 ◽  
Author(s):  
M.C. FAIRHURST ◽  
P. BRITTAN
Keyword(s):  
Feature Selection ◽  
Feature Vector ◽  
General Purpose ◽  
Signature Verification ◽  
Generic Model ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Vector Construction ◽  
Verification Systems ◽  
Handwritten Signature

This paper describes possible strategies for the implementation of a feature selection algorithm particularly suited to the realisation of an efficient automatic handwritten signature verification system in which an active feature vector, optimised with respect to an individual signer, is constructed during an enrollment period. A range of configurations based on transputer arrays are considered and the possible implementational approaches evaluated. The paper demonstrates how the inherent parallelism which exists within a generic model for verification can be exploited to provide an optimised general-purpose framework for verification processing.

Byte2vec: Malware Representation and Feature Selection for Android

2019 ◽  
Vol 63 (8) ◽  
pp. 1125-1138
Author(s):  
Mahmood Yousefi-Azar ◽  
Len Hamey ◽  
Vijay Varadharajan ◽  
Shiping Chen
Keyword(s):  
Feature Selection ◽  
Feature Vector ◽  
False Negative ◽  
Malware Detection ◽  
False Negative Rate ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Context Vector ◽  
Wide Range ◽  
Selection For

Abstract Malware detection based on static features and without code disassembling is a challenging path of research. Obfuscation makes the static analysis of malware even more challenging. This paper extends static malware detection beyond byte level $n$-grams and detecting important strings. We propose a model (Byte2vec) with the capabilities of both binary file feature representation and feature selection for malware detection. Byte2vec embeds the semantic similarity of byte level codes into a feature vector (byte vector) and also into a context vector. The learned feature vectors of Byte2vec, using skip-gram with negative-sampling topology, are combined with byte-level term-frequency (tf) for malware detection. We also show that the distance between a feature vector and its corresponding context vector provides a useful measure to rank features. The top ranked features are successfully used for malware detection. We show that this feature selection algorithm is an unsupervised version of mutual information (MI). We test the proposed scheme on four freely available Android malware datasets including one obfuscated malware dataset. The model is trained only on clean APKs. The results show that the model outperforms MI in a low-dimensional feature space and is competitive with MI and other state-of-the-art models in higher dimensions. In particular, our tests show very promising results on a wide range of obfuscated malware with a false negative rate of only 0.3% and a false positive rate of 2.0%. The detection results on obfuscated malware show the advantage of the unsupervised feature selection algorithm compared with the MI-based method.

scholarly journals High-Accuracy Power Quality Disturbance Classification Using the Adaptive ABC-PSO as Optimal Feature Selection Algorithm

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1238
Author(s):  
Supanat Chamchuen ◽  
Apirat Siritaratiwat ◽  
Pradit Fuangfoo ◽  
Puripong Suthisopapan ◽  
Pirat Khunkitti
Keyword(s):  
Feature Selection ◽  
Power Quality ◽  
Distribution System ◽  
Classification Accuracy ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Electrical Distribution ◽  
Power Quality Disturbance ◽  
Optimal Feature Selection ◽  
Optimal Feature

Power quality disturbance (PQD) is an important issue in electrical distribution systems that needs to be detected promptly and identified to prevent the degradation of system reliability. This work proposes a PQD classification using a novel algorithm, comprised of the artificial bee colony (ABC) and the particle swarm optimization (PSO) algorithms, called “adaptive ABC-PSO” as the feature selection algorithm. The proposed adaptive technique is applied to a combination of ABC and PSO algorithms, and then used as the feature selection algorithm. A discrete wavelet transform is used as the feature extraction method, and a probabilistic neural network is used as the classifier. We found that the highest classification accuracy (99.31%) could be achieved through nine optimally selected features out of all 72 extracted features. Moreover, the proposed PQD classification system demonstrated high performance in a noisy environment, as well as the real distribution system. When comparing the presented PQD classification system’s performance to previous studies, PQD classification accuracy using adaptive ABC-PSO as the optimal feature selection algorithm is considered to be at a high-range scale; therefore, the adaptive ABC-PSO algorithm can be used to classify the PQD in a practical electrical distribution system.

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