Browsing by Author "Worku, Tewodros"

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    Anomaly Based Peer-to-Peer Botnet Detectionusing Fuzzy-Neuronetwork
    (Addis Ababa University, 2020-10-10) Worku, Tewodros; Gizaw, Solomon (PhD)
    Peer-to-Peer (P2P) botnets are considered as one of the most significant contributors to various malicious activities on the Internet. The denial of service attacks, spamming, keylogging, click fraud, traffic sniffing, stealing personal user information, for example credit card numbers, and social security numbers, are some of the illegal activities based on botnets. P2P botnets are networks of infected computing devices, called zombies or bots. These bots are remotely controlled and instructed by malicious entities commonly referred to as Botmasters or hackers. In recent years, lots of researchers have proposed a number of P2P botnet detection models, but due to the evolving nature of botnets, there is still a need for new techniques to identify recent botnets. Due to that, we propose a model that is able to distinguish genuine network traffic from malicious one by analyzing the network flow data using Fuzzy-Neuro Network (FNN). The proposed model has the following components: Feature Extractor, Feature Selector, Dataset Constructor, Preprocessor, Classifier and P2P Botnet Detector. The feature extraction component extracts the network traffic-based feature vectors from the network traffic whereas the feature selection component selects vital features based on their information gain value. The next component which is the dataset constructor is used to convert the comma separated value (CSV) file into sets and help us to split the dataset as training (70%) and testing (30%) sets. Then, the major activities in the preprocessing component are data cleaning, data transformation and data reduction. Finally, the FNN classifier is utilized to classify the network traffic into P2P botnet and normal using the botnet detection module. The feasibility of our proposed model has been validated through experiments using network traffic records acquired from two publicly available P2P botnet datasets Bot-IoT and UNSW-NB15. The datasets include both genuine and malicious network traffic. The evaluation result shows the proposed model is effective in detecting P2P botnets. Based on the evaluation results of our classifier, using Bot-IoT dataset, the model scored 100% for all evaluation metrics. Whereas, using the UNSW-NB15 dataset, the model scored highest classification accuracy of 99.9%, precision of 99.9% and recall of 100% with F-measure rate of 99.9%.
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    Anomaly Based Peer-to-Peer Botnet Detectionusing Fuzzy-Neuronetwork
    (Addis Ababa University, 10/10/2020) Worku, Tewodros; Gizaw, Solomon (PhD)
    Peer-to-Peer (P2P) botnets are considered as one of the most significant contributors to various malicious activities on the Internet. The denial of service attacks, spamming, keylogging, click fraud, traffic sniffing, stealing personal user information, for example credit card numbers, and social security numbers, are some of the illegal activities based on botnets. P2P botnets are networks of infected computing devices, called zombies or bots. These bots are remotely controlled and instructed by malicious entities commonly referred to as Botmasters or hackers. In recent years, lots of researchers have proposed a number of P2P botnet detection models, but due to the evolving nature of botnets, there is still a need for new techniques to identify recent botnets. Due to that, we propose a model that is able to distinguish genuine network traffic from malicious one by analyzing the network flow data using Fuzzy-Neuro Network (FNN). The proposed model has the following components: Feature Extractor, Feature Selector, Dataset Constructor, Preprocessor, Classifier and P2P Botnet Detector. The feature extraction component extracts the network traffic-based feature vectors from the network traffic whereas the feature selection component selects vital features based on their information gain value. The next component which is the dataset constructor is used to convert the comma separated value (CSV) file into sets and help us to split the dataset as training (70%) and testing (30%) sets. Then, the major activities in the preprocessing component are data cleaning, data transformation and data reduction. Finally, the FNN classifier is utilized to classify the network traffic into P2P botnet and normal using the botnet detection module. The feasibility of our proposed model has been validated through experiments using network traffic records acquired from two publicly available P2P botnet datasets Bot-IoT and UNSW-NB15. The datasets include both genuine and malicious network traffic. The evaluation result shows the proposed model is effective in detecting P2P botnets. Based on the evaluation results of our classifier, using Bot-IoT dataset, the model scored 100% for all evaluation metrics. Whereas, using the UNSW-NB15 dataset, the model scored highest classification accuracy of 99.9%, precision of 99.9% and recall of 100% with F-measure rate of 99.9%.
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    Context Aware Semantic Search Engine for Smart Phones Tewodros Worku Kerie
    (Addis Ababa University, 2015-03-31) Worku, Tewodros; Getahun, Fekade(PhD)
    The World Wide Web is an information system of interlinked hypertext documents that are accessed via the Internet. A search engine is a document retrieval system design to find information stored in a computer system, such as on the WWW. With the exponential growth in web content, the answers provided by traditional search engines by query specific keywords to content has resulted in high recall and low precision. Many queries processed on the World Wide Web do not return the desired results because they fail to take into account the context of the query and information about user’s situation and preferences. Some search engine now has semantic functionality, can understand data context, but it is limited to only the context of the query. Context-awareness refers to the capability of a software application to provide services to their users based on the user’s current context. As such, the objective of this thesis is to present context aware semantic search engines for smart phones. We present the architecture of context aware semantic search engines on the top of semantic web. Ontology based context modeling is used to represent user contexts. The user’s query history and submitted query is also analyzed to identify the concept of the search. Prototype of the system is also developed to demonstrate and test applicability and effectiveness of the proposed approach. To evaluate the effectiveness of our approach precision measures were conducted on top 15 retrieved documents. The experimental results showed 71.7 % precision which is higher than keyword base search results on the same dataset which is 41.18 % precision. Keywords Semantic Search; Context aware Search; Search engine; Semantic web.
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    Context Aware Semantic Search Engine for Smart Phones Tewodros Worku Kerie
    (Addis Ababa University, 3/31/2015) Worku, Tewodros; Getahun, Fekade(PhD)
    The World Wide Web is an information system of interlinked hypertext documents that are accessed via the Internet. A search engine is a document retrieval system design to find information stored in a computer system, such as on the WWW. With the exponential growth in web content, the answers provided by traditional search engines by query specific keywords to content has resulted in high recall and low precision. Many queries processed on the World Wide Web do not return the desired results because they fail to take into account the context of the query and information about user’s situation and preferences. Some search engine now has semantic functionality, can understand data context, but it is limited to only the context of the query. Context-awareness refers to the capability of a software application to provide services to their users based on the user’s current context. As such, the objective of this thesis is to present context aware semantic search engines for smart phones. We present the architecture of context aware semantic search engines on the top of semantic web. Ontology based context modeling is used to represent user contexts. The user’s query history and submitted query is also analyzed to identify the concept of the search. Prototype of the system is also developed to demonstrate and test applicability and effectiveness of the proposed approach. To evaluate the effectiveness of our approach precision measures were conducted on top 15 retrieved documents. The experimental results showed 71.7 % precision which is higher than keyword base search results on the same dataset which is 41.18 % precision. Keywords Semantic Search; Context aware Search; Search engine; Semantic web.