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Öğe Deep Learning Approaches for Predictive Masquerade Detection(Hindawi Limited, 2018) Elmasry, Wisam; Akbulut, Akhan; Zaim, Abdül HalimIn computer security, masquerade detection is a special type of intrusion detection problem. Effective and early intrusion detection is a crucial factor for computer security. Although considerable work has been focused on masquerade detection for more than a decade, achieving a high level of accuracy and a comparatively low false alarm rate is still a big challenge. In this paper, we present a comprehensive empirical study in the area of anomaly-based masquerade detection using three deep learning models, namely, Deep Neural Networks (DNN), Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN), and Convolutional Neural Networks (CNN). In order to surpass previous studies on this subject, we used three UNIX command line-based datasets, with six variant data configurations implemented from them. Furthermore, static and dynamic masquerade detection approaches were utilized in this study. In a static approach, DNN and LSTM-RNN models are used along with a Particle Swarm Optimization-based algorithm for their hyperparameters selection. On the other hand, a CNN model is employed in a dynamic approach. Moreover, twelve well-known evaluation metrics are used to assess model performance in each of the data configurations. Finally, intensive quantitative and ROC curves analyses of results are provided at the end of this paper. The results not only show that deep learning models outperform all traditional machine learning methods in the literature but also prove their ability to enhance masquerade detection on the used datasets significantly. © 2018 Wisam Elmasry et al.Öğe Derin öğrenme yaklaşımı kullanarak bulut ortamları için saldırı tespit hizmet tasarımı(İstanbul Ticaret Üniversitesi, 2019) Elmasry, Wisam; Zai?m, Abdül Hali?m; Akbulut, AkhanSaldırı tespiti, siber güvenliğin temel taşı olarak kabul edilir. Erken ve etkili saldırı tespiti, son on yılda araştırmacılardan büyük ilgi görmüştür. Bununla birlikte, siber güvenliğe saldırı tespiti için derin öğrenme modellerinin kullanımı konusunda derin ve yeterli bir çalışmanın varlığı nadiren mümkündür. Bu tez çalışmasında kişisel bilgisayar, ağ ve bulut bilişim olmak üzere üç farklı ortamda saldırı saptama problemini araştırdık. Kişisel bilgisayar ve ağ ortamları ile ilgili olarak, sırasıyla maskeli ve ağ saldırı tespiti için bir dizi derin öğrenme modeli geliştirdik. Ayrıca, hem özellik hem de hiperparametre seçimi için yeni ve etkili bir çift Parçacık Sürü Optimizasyonu (PSO) tabanlı bir algoritma önerdik. Eski algoritmayı, derin öğrenme modelinin antrenman öncesi aşamasında, verilen antrenman setinin optimum özellik alt kümesi ve azaltılmış antrenman setinin doğruluğunu en üst düzeye çıkartan modelin optimum hipermetreleri elde edileceği şekilde kullandık. eğitim aşamasına. Ayrıca, geliştirilen derin öğrenme modellerinin saldırı tespitinde iyi bilinen bir dizi veri seti ve çeşitli analizler kullanarak etkinliğini doğruladık. Deneysel sonuçlar, çift PSO tabanlı algoritmayı kullanarak ön eğitimli derin öğrenme modellerinin performans açısından geleneksel makine öğrenme yöntemlerinden daha iyi performans gösterdiğini, tespit oranını 1% ile 10% arasında artırdığını ve yanlış alarm oranını 1% ıle 5% arasında azalttığını çoğu durumda göstermiştir. Kişisel bilgisayar ve ağ ortamları için saldırı tespitindeki bulgularımız, dinamik, karma ve çok iş parçacıklı bir bulut tabanlı saldırı algılama sistemi tasarlamak için kullanılır. Buna ek olarak, üçüncü taraf bir bulut hizmeti, önerilen bir bulut tabanlı saldırı algılama sistemini izlemek ve yönetmek, ayrıca bir saldırı alarmı verildiğinde bulut kullanıcıları ve bulut hizmeti sağlayıcısıyla iletişim kurmak için de tasarlanmıştır.Öğe A design of an integrated cloud-based intrusion detection system with third party cloud service(De Gruyter, 2021) Elmasry, Wisam; Akbulut, Akhan; Zaim, Abdül HalimAlthough cloud computing is considered the most widespread technology nowadays, it still suffers from many challenges, especially related to its security. Due to the open and distributed nature of the cloud environment, this makes the cloud itself vulnerable to various attacks. In this paper, the design of a novel integrated Cloud-based Intrusion Detection System (CIDS) is proposed to immunise the cloud against any possible attacks. The proposed CIDS consists of five main modules to do the following actions: monitoring the network, capturing the traffic flows, extracting features, analyzing the flows, detecting intrusions, taking a reaction, and logging all activities. Furthermore an enhanced bagging ensemble system of three deep learning models is utilized to predict intrusions effectively. Moreover, a third-party Cloud-based Intrusion Detection System Service (CIDSS) is also exploited to control the proposed CIDS and provide the reporting service. Finally, it has been shown that the proposed approach overcomes all problems associated with attacks on the cloud raised in the literature.Öğe Empirical study on multiclass classification-based network intrusion detection(Blackwell Publishing Inc., 2019) Elmasry, Wisam; Akbulut, Akhan; Zaim, Abdül HalimEarly and effective network intrusion detection is deemed to be a critical basis for cybersecurity domain. In the past decade, although a significant amount of work has focused on network intrusion detection, it is still a challenge to establish an intrusion detection system with a high detection rate and a relatively low false alarm rate. In this paper, we have performed a comprehensive empirical study on network intrusion detection as a multiclass classification task, not just to detect a suspicious connection but also to assign the correct type as well. To surpass the previous studies, we have utilized four deep learning models, namely, deep neural networks, long short-term memory recurrent neural networks, gated recurrent unit recurrent neural networks, and deep belief networks. Our approach relies on the pretraining of the models by exploiting a particle swarm optimization–based algorithm for their hyperparameters selection. In order to investigate the performance differences, we also included two well-known shallow learning methods, namely, decision forest and decision jungle. Furthermore, we used in our experiments four datasets, which are dedicated to intrusion detection systems to explore various environments. These datasets are KDD CUP 99, NSL-KDD, CIDDS, and CICIDS2017. Moreover, 22 evaluation metrics are used to assess the model's performance in each of the datasets. Finally, intensive quantitative, Friedman test, and ranking methods analyses of our results are provided at the end of this paper. The results show a significant improvement in the detection of network attacks with our recommended approach. © 2019 Wiley Periodicals, Inc.Öğe Evolving deep learning architectures for network intrusion detection using a double PSO metaheuristic(Elsevier B.V., 2020) Elmasry, Wisam; Akbulut, Akhan; Zaim, Abdül HalimThe prevention of intrusion is deemed to be a cornerstone of network security. Although excessive work has been introduced on network intrusion detection in the last decade, finding an Intrusion Detection Systems (IDS) with potent intrusion detection mechanism is still highly desirable. One of the leading causes of the high number of false alarms and a low detection rate is the existence of redundant and irrelevant features of the datasets, which are used to train the IDSs. To cope with this problem, we proposed a double Particle Swarm Optimization (PSO)-based algorithm to select both feature subset and hyperparameters in one process. The aforementioned algorithm is exploited in the pre-training phase for selecting the optimized features and model's hyperparameters automatically. In order to investigate the performance differences, we utilized three deep learning models, namely, Deep Neural Networks (DNN), Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN), and Deep Belief Networks (DBN). Furthermore, we used two common IDS datasets in our experiments to validate our approach and show the effectiveness of the developed models. Moreover, many evaluation metrics are used for both binary and multiclass classifications to assess the model's performance in each of the datasets. Finally, intensive quantitative, Friedman test, and ranking methods analyses of our results are provided at the end of this paper. Experimental results show a significant improvement in network intrusion detection when using our approach by increasing Detection Rate (DR) by 4% to 6% and reducing False Alarm Rate (FAR) by 1% to 5% from the corresponding values of same models without pre-training on the same dataset. © 2019Öğe New LSB-based colour image steganography method to enhance the efficiency in payload capacity, security and integrity check(Springer India, 2018) Kasapbaşı, Mustafa Cem; Elmasry, WisamSteganography is the technique for hiding information within a carrier file so that it is imperceptible for unauthorized parties. In this study, it is intended to combine many techniques to gather a new method for colour image steganography to obtain enhanced efficiency, attain increased payload capacity, posses integrity check and security with cryptography at the same time. Proposed work supports many different formats as payload. In the proposed method, the codeword is firstly formed with secret data and its CRC-32 checksum, then the codeword is compressed by Gzip just before encrypting it by AES, and it is finally added to encrypted header information for further process and then embedded into the cover image. Embedding the encrypted data and header information process utilizes Fisher-Yates Shuffle algorithm for selecting next pixel location. To hide one byte, different LSB (least significant bits) of all colour channels of the selected pixel is exploited. In order to evaluate the proposed method, comparative performance tests are carried out against different spatial image steganographic techniques using some of the well-known image quality metrics. For security analysis, histogram, enhanced LSB and Chi-square analyses are carried out. The results indicate that with the proposed method has an improved payload capacity, security and integrity check for common problems of simple LSB method. Moreover, it has been shown that the proposed method increases the visual quality of the stego image when compared to other studied methods, and makes the secret message difficult to be discovered. © 2018, Indian Academy of Sciences.
