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    The Android malware detection systems between hope and reality
    (Springer International Publishing Ag, 2019) Bakour, Khaled; Unver, Halil Murat; Ghanem, Razan
    The widespread use of Android-based smartphones made it an important target for malicious applications' developers. So, a large number of frameworks have been proposed to tackle the huge number of daily published malwares. Despite there are many review papers that have been conducted in order to shed light on the works that achieved in Android malware analysing domain, the number of conducted review papers do not fit with the importance of this research field and with the volume of achieved works. Also, there is no comprehensive taxonomy for all research trends in the field of analysing malicious applications targeting the Android system. Furthermore, none of the existing review papers contains a schematic model that makes it easy for the reader to know the methods and methodologies used in a particular field of research without much effort. This paper aims at proposing a comprehensive taxonomy and suggesting a new schematic review approach.To this end, a review of a large number of works that achieved between 2009 and 2019 has been conducted. The achieved study includes more than 200 papers that have different goals such as apps' behaviour analysis, automatic user interface triggers or packer/unpacker frameworks development. Also, a comprehensive taxonomy has been proposed so that most of the previous works can be classified under it. To the best of our knowledge, the suggested taxonomy is the widest and the most comprehensive in terms of the covered research trends. Moreover, we have proposed a detailed schematic model (called Schematic Review Model) illustrates the process of detecting the malignant applications of an Android in the light of the studied works and the proposed taxonomy. To our knowledge, this is the first time that the Android malware detection methods have been explained in this way with this amount of detail. Furthermore, the studied researches have been analysed according to multiple criteria such as used analysing method, used features, used detection method, and used dataset. Also, the features used in the studied works were discussed in detail by dividing it into multiple classes. Moreover, the challenges facing Android's malware analysing methods were discussed in detail. Finally, it has been concluded that there are gaps between the size and the goal of the conducted works and the number of malicious apps published every day, so some future works areas have been proposed and discussed.
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    The Android Malware Static Analysis: Techniques, Limitations, and Open Challenges
    (Ieee, 2018) Bakour, Khaled; Unver, H. Murat; Ghanem, Razan
    This paper aims to explain static analysis techniques in detail, and to highlight the weaknesses and challenges which face it. To this end, more than 80 static analysis based framework have been studied, and in their light, the process of detecting malicious applications has been divided into four phases that were explained in a schematic manner. Also, the features that is used in static analysis were discussed in detail by dividing it into four categories namely, Manifest-based features, code-based features, semantic features and app's metadata-based features. Also, the challenges facing methods based on static analysis were discussed in detail. Finally, a case study was conducted to test the strength of some known commercial antivirus and one of the stat-of-art academic static analysis frameworks against obfuscation techniques used by developers of malicious applications. The results showed a significant impact on the performance of the most tested antiviruses and frameworks, which is reflecting the urgent need for more accurately tools.
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    Context-dependent model for spam detection on social networks
    (SPRINGER INTERNATIONAL PUBLISHING AG, 2020) Ghanem, Razan; Erbay, Hasan
    Social media platforms are getting an important communication medium in our daily life, and their increasing popularity makes them an ideal platform for spammers to spread spam messages, known as spam problems. Moreover, messages on social media are vague and messy, so a good representation of the text may be the first step to address spam problem. While traditional weighting methods suffer from both high dimensionality and high sparsity problems, traditional word embedding methods suffer from context independence and out of vocabulary problems. To overcome these problems, in this study, we propose a novel architecture based on a context-dependent representation of text using the BERT model. The model was tested using the Twitter dataset, and experimental results show that the proposed method outperforms traditional weighting methods, traditional word embedding based methods as well as the existing state of the art methods used to detect spam on the twitter platform.
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    Spam detection on social networks using deep contextualized word representation
    (Springer, 2023) Ghanem, Razan; Erbay, Hasan
    Spam detection on social networks, considered a short text classification problem, is a challenging task in natural language processing due to the sparsity and ambiguity of the text. One of the key tasks to address this problem is a powerful text representation. Traditional word embedding models solve the data sparsity problem by representing words with dense vectors, but these models have some limitations that prevent them from handling some problems effectively. The most common limitation is the out of vocabulary problem, in which the models fail to provide any vector representation for the words that are not present in the model's dictionary. Another problem these models face is the independence from the context, in which the models output just one vector for each word regardless of the position of the word in the sentence. To overcome these problems, we propose to build a new model based on deep contextualized word representation, consequently, in this study, we develop CBLSTM (Contextualized Bi-directional Long Short Term Memory neural network), a novel deep learning architecture based on bidirectional long short term neural network with embedding from language models, to address the spam texts problem on social networks. The experimental results on three benchmark datasets show that our proposed method achieves high accuracy and outperforms the existing state-of-the-art methods to detect spam on social networks.