Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8651

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  • Article
    Citation - WoS: 6
    Citation - Scopus: 14
    A Novel Approach for Continuous Authentication of Mobile Users Using Reduce Feature Elimination (Rfe): A Machine Learning Approach
    (Springer, 2023) Singh, Karan; Khan, Tayyab; Ariffin, Mazeyanti Mohd; Mohan, Senthil Kumar; Baleanu, Dumitru; Ahmadian, Ali; Kumari, Sonal
    Mobile phones are a valuable object in our daily life. With the acquisition of the latest technologies, their capabilities and demands increase day by day. However, acquiring the latest technologies makes mobile phones vulnerable to various security threats. Generally, people use passwords, pins, fingerprint locks, etc., to secure their mobile phones. Passwords and pins create so much burden for people always to remember their credentials. These traditional approaches are susceptible to brute force attacks, smudge attacks, and shoulder surfing attacks. Due to the difficulties mentioned above, researchers are leaning more towards continuous authentication. Therefore, this paper introduces an adaptive continuous authentication approach, a behavioral-based mobile authentication mechanism. In (Ehatisham-ul-Haq et al. J Netw Comput Appl 109:24-35, 2018), the authors achieved a good average accuracy of 97.95% with a Support vector machine classifier (SVM). We used LGB and RF and got 95.8% and 98.8% accuracy in user recognition. RF and LGB were trained for all five body positions separately to recognize each User among five users. This model also promises to reduce the system's cost and complexity by introducing the reduce feature elimination (RFE) technique during feature selection. RFE eliminates the less critical feature and reduces the dimension of the feature set. Hence, it demonstrates the benefits of our model for mobile authentication.
  • 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%.