Bilgisayar Mühendisliği Bölümü Yayın Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/253

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Publisher: search.filters.publisher.Assoc Computing MachinerySubject: search.filters.subject.Deep Learning

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Now showing 1 - 3 of 3
  • Conference Object
    Citation - WoS: 40
    Citation - Scopus: 77
    Malware Classification Using Deep Learning Methods
    (Assoc Computing Machinery, 2018) Dogdu, Erdogan; Cakir, Bugra
    Malware, short for Malicious Software, is growing continuously in numbers and sophistication as our digital world continuous to grow. It is a very serious problem and many efforts are devoted to malware detection in today's cybersecurity world. Many machine learning algorithms are used for the automatic detection of malware in recent years. Most recently, deep learning is being used with better performance. Deep learning models are shown to work much better in the analysis of long sequences of system calls. In this paper a shallow deep learning-based feature extraction method (word2vec) is used for representing any given malware based on its opcodes. Gradient Boosting algorithm is used for the classification task. Then, k-fold cross-validation is used to validate the model performance without sacrificing a validation split. Evaluation results show up to 96% accuracy with limited sample data.
  • Conference Object
    Citation - WoS: 7
    Phishing E-Mail Detection by Using Deep Learning Algorithms
    (Assoc Computing Machinery, 2018) Hassanpour, Reza; Dogdu, Erdogan; Choupani, Roya; Goker, Onur; Nazli, Nazli
  • Conference Object
    Citation - WoS: 140
    Citation - Scopus: 214
    Intrusion Detection Using Big Data and Deep Learning Techniques
    (Assoc Computing Machinery, 2019) Dogdu, Erdogan; Faker, Osama
    In this paper, Big Data and Deep Learning Techniques are integrated to improve the performance of intrusion detection systems. Three classifiers are used to classify network traffic datasets, and these are Deep Feed-Forward Neural Network (DNN) and two ensemble techniques, Random Forest and Gradient Boosting Tree (GBT). To select the most relevant attributes from the datasets, we use a homogeneity metric to evaluate features. Two recently published datasets UNSW NB15 and CICIDS2017 are used to evaluate the proposed method. 5-fold cross validation is used in this work to evaluate the machine learning models. We implemented the method using the distributed computing environment Apache Spark, integrated with Keras Deep Learning Library to implement the deep learning technique while the ensemble techniques are implemented using Apache Spark Machine Learning Library. The results show a high accuracy with DNN for binary and multiclass classification on UNSW NB15 dataset with accuracies at 99.16% for binary classification and 97.01% for multiclass classification. While GBT classifier achieved the best accuracy for binary classification with the CICIDS2017 dataset at 99.99%, for multiclass classification DNN has the highest accuracy with 99.56%.