A hybrid feature-selection approach for finding the digital evidence of webapplication attacks

Abstract

The most critical challenge of web attack forensic investigations is the sheer amount of data and level of complexity. Machine learning technology might be an efficient solution for web attack analysis and investigation.Consequently, machine learning applications have been applied in various areas of information security and digital forensics, and have improved over time. Moreover, feature selection is a crucial step in machine learning; in fact, selecting an optimal feature subset could enhance the accuracy and performance of the predictive model. To date, there has not been an adequate approach to select optimal features for the evidence of web attack. In this study, a hybrid approach that selects the relevant web attack features by combining the filter and wrapper methods is proposed.This approach has been validated by experimental measurements on 3 web attack datasets. The results show that our proposed approach can find the evidence with high recall, high accuracy, and low error rates. We believe that the results presented herein may help us to improve accuracy and recall of machine learning techniques; particularly, in the field of web attack investigation. The tools that use this approach may help digital forensic professionals and law enforcement in finding the evidence much more efficiently and faster.

An Improved DeepNN with Feature Ranking for Covid-19 Detection

“…Some additional notations are used to distinguish between the variables of the hidden layers as follows: superscripts to define the number of the layer, and subscripts to define the number of the neurons in the current layer (e.g., w 2 1 , means the weight value for neuron number 1 in layer number 2) [34]. The pseudo-code of the original MLP algorithm is displayed in Algorithm 1 [36].…”

“…In general, the traditional feature selection approaches are classified into three types; filter approach, wrapper approach, and embedded approach, as shown in Fig. 1 [2]. Filter methods are classifier-independent, where they select the features' subsets based on specific given criteria [3].…”

“…The second covid-19 dataset includes clinical features for symptomatic and asymptomatic patients (e.g., comorbidities, vitals, epidemiologic factors, clinicianassessed symptoms, and patient-reported symptoms). This dataset consists of 34475 records with 41 features and one class label and can be found at2 . The third dataset focuses on predicting intensive care unit (ICU) admission for positive COVID-19 cases based on clinical data.…”

Adli Bilişim İncelenme Süreçlerinde Yapay Zeka Kullanımı: VGG16 ile Görüntü Sınıflandırma

“…Araştırma neticesinde adli bilişim alanı kapsamında derin öğrenme algoritmalarının yoğun olarak metin verisi [5], ses verisi [6], video [7][8][9] ve resim [3], [10][11][12][13][14][15][16][17][18][19] verileri analizinde kullanıldığı tespit edilmiştir. Söz konusu çalışma alanlarına ek olarak yöntem iyileştirme [20,21], süreç iyileştirme ve otomasyon [22,23], saldırı tespit ve güvenlik log analizi [2], [24][25], derin öğrenme algoritmalarının yaygın olarak kullanıldığı diğer alanlardır.…”

“…Yöntem iyileştirme çalışmaları kapsamında ise makine öğrenmesinin, internet sitesi saldırılarının analizi ve incelenmesi süreçlerinde etkili bir yöntem olarak kullanılabileceği önerilmiştir [20]. Çalışmada, makine öğrenmesi aşamalarında özellik seçiminin kritik olduğu ve doğru parametrelerin seçilmesi durumunda modelin performansının artacağı vurgulanmıştır.…”