WoS İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8653

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Author: search.filters.author.Dogdu, ErdoganPublisher: search.filters.publisher.Assoc Computing Machinery

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Now showing 1 - 6 of 6
  • Conference Object
    Citation - WoS: 8
    Citation - Scopus: 11
    Sentiment Analysis for the Social Media: a Case Study for Turkish General Elections
    (Assoc Computing Machinery, 2017) Yumusak, Semih; Oztoprak, Kasim; Dogdu, Erdogan; Uysal, Elif
    The ideas expressed in social media are not always compliant with natural language rules, and the mood and emotion indicators are mostly highlighted by emoticons and emotion specific keywords. There are language independent emotion keywords (e.g. love, hate, good, bad), besides every language has its own particular emotion specific keywords. These keywords can be used for polarity analysis for a particular sentence. In this study, we first created a Turkish dictionary containing emotion specific keywords. Then, we used this dictionary to detect the polarity of tweets that are collected by querying political keywords right before the Turkish general election in 2015. The tweets were collected based on their relatedness with three main categories: the political leaders, ideologies, and political parties. The polarity of these tweets are analyzed in comparison with the election results.
  • 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 - Scopus: 2
    A Discovery and Analysis Engine for Semantic Web
    (Assoc Computing Machinery, 2018) Kamilaris, Andreas; Dogdu, Erdogan; Kodaz, Halife; Uysal, Elif; Aras, Riza Emre; Yumusak, Semih
    The Semantic Web promotes common data formats and exchange protocols on the web towards better interoperability among systems and machines. Although Semantic Web technologies are being used to semantically annotate data and resources for easier reuse, the ad hoc discovery of these data sources remains an open issue. Popular Semantic Web endpoint repositories such as SPARQLES, Linking Open Data Project (LOD Cloud), and LODStats do not include recently published datasets and are not updated frequently by the publishers. Hence, there is a need for a web-based dynamic search engine that discovers these endpoints and datasets at frequent intervals. To address this need, a novel web meta-crawling method is proposed for discovering Linked Data sources on the Web. We implemented the method in a prototype system named SPARQL Endpoints Discovery (SpEnD). In this paper, we describe the design and implementation of SpEnD, together with an analysis and evaluation of its operation, in comparison to the aforementioned static endpoint repositories in terms of time performance, availability, and size. Findings indicate that SpEnD outperforms existing Linked Data resource discovery methods.
  • 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: 30
    Citation - Scopus: 51
    An Artificial Neural Network-Based Stock Trading System Using Technical Analysis and Big Data Framework
    (Assoc Computing Machinery, 2017) Ozbayoglu, A. Murat; Dogdu, Erdogan; Sezer, Omer Berat
    In this paper, a neural network-based stock price prediction and trading system using technical analysis indicators is presented. The model developed first converts the financial time series data into a series of buy-sell-hold trigger signals using the most commonly preferred technical analysis indicators. Then, a Multilayer Perceptron (MLP) artificial neural network (ANN) model is trained in the learning stage on the daily stock prices between 1997 and 2007 for all of the Dow30 stocks. Apache Spark big data framework is used in the training stage. The trained model is then tested with data from 2007 to 2017. The results indicate that by choosing the most appropriate technical indicators, the neural network model can achieve comparable results against the Buy and Hold strategy in most of the cases. Furthermore, fine tuning the technical indicators and/or optimization strategy can enhance the overall trading performance.
  • 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%.