Detection of Privacy Threat by Peculiar Feature Extraction in Malwares to Combat Targeted Cyber Attacks

2015 ◽  
pp. 1237-1247
Author(s):  
Farhan Habib Ahmad ◽  
Komal Batool ◽  
Azhar Javed
Keyword(s):  
Feature Extraction ◽  
Cyber Attacks ◽  
Peculiar Feature

scholarly journals Utilising Flow Aggregation to Classify Benign Imitating Attacks

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1761
Author(s):  
Hanan Hindy ◽  
Robert Atkinson ◽  
Christos Tachtatzis ◽  
Ethan Bayne ◽  
Miroslav Bures ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Network Traffic ◽  
Cyber Attacks ◽  
Detection Accuracy ◽  
Computational Power ◽  
Human Understanding ◽  
Flow Aggregation ◽  
Additional Level ◽  
Attack Surfaces

Cyber-attacks continue to grow, both in terms of volume and sophistication. This is aided by an increase in available computational power, expanding attack surfaces, and advancements in the human understanding of how to make attacks undetectable. Unsurprisingly, machine learning is utilised to defend against these attacks. In many applications, the choice of features is more important than the choice of model. A range of studies have, with varying degrees of success, attempted to discriminate between benign traffic and well-known cyber-attacks. The features used in these studies are broadly similar and have demonstrated their effectiveness in situations where cyber-attacks do not imitate benign behaviour. To overcome this barrier, in this manuscript, we introduce new features based on a higher level of abstraction of network traffic. Specifically, we perform flow aggregation by grouping flows with similarities. This additional level of feature abstraction benefits from cumulative information, thus qualifying the models to classify cyber-attacks that mimic benign traffic. The performance of the new features is evaluated using the benchmark CICIDS2017 dataset, and the results demonstrate their validity and effectiveness. This novel proposal will improve the detection accuracy of cyber-attacks and also build towards a new direction of feature extraction for complex ones.

scholarly journals A novel hybrid approach of SVM combined with NLP and probabilistic neural network for email phishing

2020 ◽  
Vol 10 (1) ◽  
pp. 486 ◽  
Author(s):  
Abhishek Kumar ◽  
Jyotir Moy Chatterjee ◽  
Vicente García Díaz
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Probabilistic Neural Network ◽  
Hybrid Approach ◽  
Cyber Attacks ◽  
Hybrid Analysis ◽  
Phishing Attacks ◽  
Phishing Detection ◽  
Hybrid Methodology

Phishing attacks are one of the slanting cyber-attacks that apply socially engineered messages that are imparted to individuals from expert hackers going for tricking clients to uncover their delicate data, the most mainstream correspondence channel to those messages is through clients' emails. Phishing has turned into a generous danger for web clients and a noteworthy reason for money related misfortunes. Therefore, different arrangements have been created to handle this issue. Deceitful emails, also called phishing emails, utilize a scope of impact strategies to convince people to react, for example, promising a fiscal reward or summoning a feeling of criticalness. Regardless of far reaching alerts and intends to instruct clients to distinguish phishing sends, these are as yet a pervasive practice and a worthwhile business. The creators accept that influence, as a style of human correspondence intended to impact others, has a focal job in fruitful advanced tricks. Cyber criminals have ceaselessly propelling their techniques for assault. The current strategies to recognize the presence of such malevolent projects and to keep them from executing are static, dynamic and hybrid analysis. In this work we are proposing a hybrid methodology for phishing detection incorporating feature extraction and classification of the mails using SVM. At last, alongside the chose features, the PNN characterizes the spam mails from the genuine mails with more exactness and accuracy.

Infill Defective Detection System Augmented by Semi-Supervised Learning

2020 ◽  
Author(s):  
Jinwoo Song ◽  
Young B. Moon
Keyword(s):  
Feature Extraction ◽  
Additive Manufacturing ◽  
Supervised Learning ◽  
Network Model ◽  
Detection System ◽  
Cyber Attacks ◽  
Training Data ◽  
Detection Accuracy ◽  
Data Sets ◽  
Detection Systems

Abstract In an effort to identify cyber-attacks on infill structures, detection systems based on supervised learning have been attempted in Additive Manufacturing (AM) security investigations. However, supervised learning requires a myriad of training data sets to achieve acceptable detection accuracy. Besides, since it is impossible to train for unprecedented defective types, the detection systems cannot guarantee robustness against unforeseen attacks. To overcome such disadvantages of supervised learning, This paper presents infill defective detection system (IDDS) augmented by semi-supervised learning. Semi-supervised learning allows classifying a sheer volume of unlabeled data sets by training a comparably small number of labeled data sets. Additionally, IDDS exploits self-training to increase the robustness against various defective types that are not pre-trained. IDDS consists of the feature extraction, pre-training, self-training. To validate the usefulness of IDDS, five defective types were designed and tested with IDDS, which was trained by only normal labeled data sets. The results are compared with the basis accuracy from the perceptron network model with supervised learning.

An Improved Quantitative Image Analyser

1977 ◽  
Vol 35 ◽  
pp. 226-227
Author(s):  
J.P. Fallon ◽  
P.J. Gregory ◽  
C.J. Taylor
Keyword(s):  
Feature Extraction ◽  
Quantitative Analysis ◽  
Frequency Content ◽  
General Sense ◽  
Physical Measurements ◽  
Quantitative Image ◽  
Image Analyser ◽  
Automatic Methods ◽  
Quantitative Results

Quantitative image analysis systems have been used for several years in research and quality control applications in various fields including metallurgy and medicine. The technique has been applied as an extension of subjective microscopy to problems requiring quantitative results and which are amenable to automatic methods of interpretation.Feature extraction. In the most general sense, a feature can be defined as a portion of the image which differs in some consistent way from the background. A feature may be characterized by the density difference between itself and the background, by an edge gradient, or by the spatial frequency content (texture) within its boundaries. The task of feature extraction includes recognition of features and encoding of the associated information for quantitative analysis.Quantitative Analysis. Quantitative analysis is the determination of one or more physical measurements of each feature. These measurements may be straightforward ones such as area, length, or perimeter, or more complex stereological measurements such as convex perimeter or Feret's diameter.

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