In recent years, RNA secondary structure prediction is an important issue in structural bioinformatics, and RNA pseudoknotted secondary structure prediction represents an NP-hard problem. Current RNA secondary structure prediction methods are mainly based on the minimum free energy algorithm. However, due to the complexity of biotic environment, a true RNA structure always keeps the balance of biological potential energy status, rather than the optimal folding status that meets the minimum energy. For short sequence RNA its equilibrium energy status for the RNA folding organism is close to the minimum free energy status. Nevertheless, in a longer sequence RNA, constant folding causes its biopotential energy balance to deviate far from the minimum free energy status. In this paper, we propose a novel RNA secondary structure prediction algorithm using a convolutional neural network model combined with a genetic algorithm method to improve the accuracy with large-scale RNA sequence and structure data...
{"title":"Predicting RNA secondary structure based on machine learning and genetic algorithm","authors":"Duy Binh Doan, Minh Tuan Pham, Duc Long Dang","doi":"10.1145/3440749.3442659","DOIUrl":"https://doi.org/10.1145/3440749.3442659","url":null,"abstract":"In recent years, RNA secondary structure prediction is an important issue in structural bioinformatics, and RNA pseudoknotted secondary structure prediction represents an NP-hard problem. Current RNA secondary structure prediction methods are mainly based on the minimum free energy algorithm. However, due to the complexity of biotic environment, a true RNA structure always keeps the balance of biological potential energy status, rather than the optimal folding status that meets the minimum energy. For short sequence RNA its equilibrium energy status for the RNA folding organism is close to the minimum free energy status. Nevertheless, in a longer sequence RNA, constant folding causes its biopotential energy balance to deviate far from the minimum free energy status. In this paper, we propose a novel RNA secondary structure prediction algorithm using a convolutional neural network model combined with a genetic algorithm method to improve the accuracy with large-scale RNA sequence and structure data...","PeriodicalId":344578,"journal":{"name":"Proceedings of the 4th International Conference on Future Networks and Distributed Systems","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128204189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Atheer Alharthi, A. Eshmawi, Azzah Kabbas, L. Hsairi
Distributed Denial of Service attacks (DDoS) are one of the most prevalent attacks threatening systems and their security. In this paper, various models to categorize these attacks are presented, analyzed and compared on regards of their effectiveness for DDoS detection. Machine learning (ML) algorithms for classification are used after pre-processing DDoS dataset to classify network traffic. After analyzing the results of Naïve bayes, Decision Tree, Support Vector Machine, and Random Forest classifiers, we conclude that the most accurate results appeared when using the Random Forest classifier.
{"title":"Network Traffic Analysis for DDOS Attack Detection","authors":"Atheer Alharthi, A. Eshmawi, Azzah Kabbas, L. Hsairi","doi":"10.1145/3440749.3442637","DOIUrl":"https://doi.org/10.1145/3440749.3442637","url":null,"abstract":"Distributed Denial of Service attacks (DDoS) are one of the most prevalent attacks threatening systems and their security. In this paper, various models to categorize these attacks are presented, analyzed and compared on regards of their effectiveness for DDoS detection. Machine learning (ML) algorithms for classification are used after pre-processing DDoS dataset to classify network traffic. After analyzing the results of Naïve bayes, Decision Tree, Support Vector Machine, and Random Forest classifiers, we conclude that the most accurate results appeared when using the Random Forest classifier.","PeriodicalId":344578,"journal":{"name":"Proceedings of the 4th International Conference on Future Networks and Distributed Systems","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128777410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
V. Pham, Anton Ovchinnikov, A. Zadorozhnaya, R. Kirichek, L. Myrova
In developing Smart Sustainable Cities (SSC), we consider providing communication networks to communicate with sensors and actuators. Internet of Things devices are expected to send data over a long distance and save energy consumption. Understanding the challenges of data transmission in the urban area, the authors propose a hybrid wireless mesh network as a network infrastructure for managing sensors and actuators deployed in the SSC. Studying wireless technologies proposed for IoT networks, the authors describe the hybrid network architecture within SSC systems. An experiment was performed using modules equipped with various wireless interfaces. As the experimental results, the nodes far from the gateway can exchange data with the remote server in the mesh network.
{"title":"A Hybrid Wireless Mesh Network for Sensor and Actuator Management in Smart Sustainable Cities","authors":"V. Pham, Anton Ovchinnikov, A. Zadorozhnaya, R. Kirichek, L. Myrova","doi":"10.1145/3440749.3442624","DOIUrl":"https://doi.org/10.1145/3440749.3442624","url":null,"abstract":"In developing Smart Sustainable Cities (SSC), we consider providing communication networks to communicate with sensors and actuators. Internet of Things devices are expected to send data over a long distance and save energy consumption. Understanding the challenges of data transmission in the urban area, the authors propose a hybrid wireless mesh network as a network infrastructure for managing sensors and actuators deployed in the SSC. Studying wireless technologies proposed for IoT networks, the authors describe the hybrid network architecture within SSC systems. An experiment was performed using modules equipped with various wireless interfaces. As the experimental results, the nodes far from the gateway can exchange data with the remote server in the mesh network.","PeriodicalId":344578,"journal":{"name":"Proceedings of the 4th International Conference on Future Networks and Distributed Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130297796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sana Sahar Guia, A. Laouid, R. Euler, Mohammed Amine Yagoub, A. Bounceur, Mohammad Hammoudeh
Nowadays, multimedia and visual computing advances in digital technology make a potential change in human life. Many applications exploit the captured images from autonomous entities as data sources for several goals. In fact, these captured images need to be interpreted in order to extract their external environment. The researchers of this domain will meet some challenges such as how to detect and interpret the images’ context. This paper is to propose an efficient technique that detects objects of a given image based on the color divergence. The results clearly show the accuracy and the computation speed of the proposed technique compared with other methods.
{"title":"A Salient Object Detection Technique Based on Color Divergence","authors":"Sana Sahar Guia, A. Laouid, R. Euler, Mohammed Amine Yagoub, A. Bounceur, Mohammad Hammoudeh","doi":"10.1145/3440749.3442593","DOIUrl":"https://doi.org/10.1145/3440749.3442593","url":null,"abstract":"Nowadays, multimedia and visual computing advances in digital technology make a potential change in human life. Many applications exploit the captured images from autonomous entities as data sources for several goals. In fact, these captured images need to be interpreted in order to extract their external environment. The researchers of this domain will meet some challenges such as how to detect and interpret the images’ context. This paper is to propose an efficient technique that detects objects of a given image based on the color divergence. The results clearly show the accuracy and the computation speed of the proposed technique compared with other methods.","PeriodicalId":344578,"journal":{"name":"Proceedings of the 4th International Conference on Future Networks and Distributed Systems","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130699998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
I. Grishin, V. Pham, Darina Okuneva, R. Kirichek, L. Myrova
Nowadays, wireless sensor networks (WSNs) are widely used for developing the Internet of Things applications, which provide services such as sensor data collection or monitor, actuator management. In some cases, there are many sensor nodes deployed randomly in the network. In this paper, we consider determining the coordinates of unknown nodes based on the multidimensional scaling method while assuming that the distances between nodes are estimated on the server. However, there are also data gaps about these distances. The article discusses a method for reducing the number of computations required to estimate the distances between nodes.
{"title":"Applying Multidimensional Scaling Method to Determine Spatial Coordinates of WSN Nodes","authors":"I. Grishin, V. Pham, Darina Okuneva, R. Kirichek, L. Myrova","doi":"10.1145/3440749.3442645","DOIUrl":"https://doi.org/10.1145/3440749.3442645","url":null,"abstract":"Nowadays, wireless sensor networks (WSNs) are widely used for developing the Internet of Things applications, which provide services such as sensor data collection or monitor, actuator management. In some cases, there are many sensor nodes deployed randomly in the network. In this paper, we consider determining the coordinates of unknown nodes based on the multidimensional scaling method while assuming that the distances between nodes are estimated on the server. However, there are also data gaps about these distances. The article discusses a method for reducing the number of computations required to estimate the distances between nodes.","PeriodicalId":344578,"journal":{"name":"Proceedings of the 4th International Conference on Future Networks and Distributed Systems","volume":"162 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115394942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Currently, m-ary radio communication systems are gaining popularity for transferring information between motile objects. This paper describes a possible way to increase the noise immunity of such systems. This is especially true for cases of long distances in poor climatic conditions. In such a situation, the power of the transmitted signal decreases with increasing distance to the receiver. When using the proposed method of increasing noise immunity in the construction of m-ary communication systems, it is possible to reduce the loss of data during transmission over the radio channel.
{"title":"Increasing the noise immunity of m-ary radio communication system between motile objects in the microwave range","authors":"I. Zharikov, V. Fadeenko, V. Davydov, A. Valov","doi":"10.1145/3440749.3442603","DOIUrl":"https://doi.org/10.1145/3440749.3442603","url":null,"abstract":"Currently, m-ary radio communication systems are gaining popularity for transferring information between motile objects. This paper describes a possible way to increase the noise immunity of such systems. This is especially true for cases of long distances in poor climatic conditions. In such a situation, the power of the transmitted signal decreases with increasing distance to the receiver. When using the proposed method of increasing noise immunity in the construction of m-ary communication systems, it is possible to reduce the loss of data during transmission over the radio channel.","PeriodicalId":344578,"journal":{"name":"Proceedings of the 4th International Conference on Future Networks and Distributed Systems","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115519169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Afaf D. Althobiti, Rabab M. Almohayawi, O. Bamasag
This paper proposes network security enhancement solution aiming to improving the level of performance in the detection of cyber-attacks on Software Defined Network (SDN) it will prevent against Denial of Service Attack. We are going to employ two solution and comparing on the SDN attack detection performance. The first approach is the performance accuracy of the SDN with IDS procedural, and the second approach is the integration of SDN with Machine Learning. The project serves the organization generally in the field of information security, network security and cybersecurity awareness. The system performance evaluation results prove the system is capable to provide the effective DDoS attack detection and provide security enhancement in Software Defined Network.
{"title":"Machine Learning approach to Secure Software Defined Network: Machine Learning and Artificial Intelligence","authors":"Afaf D. Althobiti, Rabab M. Almohayawi, O. Bamasag","doi":"10.1145/3440749.3442597","DOIUrl":"https://doi.org/10.1145/3440749.3442597","url":null,"abstract":"This paper proposes network security enhancement solution aiming to improving the level of performance in the detection of cyber-attacks on Software Defined Network (SDN) it will prevent against Denial of Service Attack. We are going to employ two solution and comparing on the SDN attack detection performance. The first approach is the performance accuracy of the SDN with IDS procedural, and the second approach is the integration of SDN with Machine Learning. The project serves the organization generally in the field of information security, network security and cybersecurity awareness. The system performance evaluation results prove the system is capable to provide the effective DDoS attack detection and provide security enhancement in Software Defined Network.","PeriodicalId":344578,"journal":{"name":"Proceedings of the 4th International Conference on Future Networks and Distributed Systems","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123408588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abdel-Nasser Ateeq, Israa Obaid, O. Othman, Ahmed Awad
Wireless Sensor Network (WSN) is a self-configured network of light-weight nodes, that are used in many applications by sending their data through this network to a Base Station (BS), which in turn, delivers the data to its final destination. In WSN, the sensor node’s energy is usually limited, and thus, the overall power consumption of the network is a real challenge. Numerous methods have been proposed to overcome this challenge by improving the overall power consumption and thus, prolonging the lifetime of the network. Low Energy Adaptive Clustering Hierarchy (LEACH) protocol has been considered as a leading routing protocol in WSN, due to its low power consumption resultant from its cluster-based behavior. However, for dense networks, LEACH suffers from large burden over cluster head (CH), which might result in packet loss due to the induced congestion in the CH. In this paper, an improved algorithm based on LEACH is proposed to maximize the lifetime of WSN, by using another node to relieve CH’s burden. The routing path selection process is refined through selecting a new node that has the highest residual energy in each cluster to be an alternative gateway for the cluster head. Then, the routing path will be decided based on distance. This modified algorithm outperforms the original LEACH by 4.35% increase in the residual energy, which prolongs the network lifetime. However, there is a slight increase in the overhead ratio.
{"title":"Lifetime Enhancement of WSN Based on Improved LEACH with Cluster Head Alternative Gateway","authors":"Abdel-Nasser Ateeq, Israa Obaid, O. Othman, Ahmed Awad","doi":"10.1145/3440749.3442615","DOIUrl":"https://doi.org/10.1145/3440749.3442615","url":null,"abstract":"Wireless Sensor Network (WSN) is a self-configured network of light-weight nodes, that are used in many applications by sending their data through this network to a Base Station (BS), which in turn, delivers the data to its final destination. In WSN, the sensor node’s energy is usually limited, and thus, the overall power consumption of the network is a real challenge. Numerous methods have been proposed to overcome this challenge by improving the overall power consumption and thus, prolonging the lifetime of the network. Low Energy Adaptive Clustering Hierarchy (LEACH) protocol has been considered as a leading routing protocol in WSN, due to its low power consumption resultant from its cluster-based behavior. However, for dense networks, LEACH suffers from large burden over cluster head (CH), which might result in packet loss due to the induced congestion in the CH. In this paper, an improved algorithm based on LEACH is proposed to maximize the lifetime of WSN, by using another node to relieve CH’s burden. The routing path selection process is refined through selecting a new node that has the highest residual energy in each cluster to be an alternative gateway for the cluster head. Then, the routing path will be decided based on distance. This modified algorithm outperforms the original LEACH by 4.35% increase in the residual energy, which prolongs the network lifetime. However, there is a slight increase in the overhead ratio.","PeriodicalId":344578,"journal":{"name":"Proceedings of the 4th International Conference on Future Networks and Distributed Systems","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114464146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Security researchers have used Natural Language Processing (NLP) and Deep Learning techniques for programming code analysis tasks such as automated bug detection and vulnerability prediction or classification. These studies mainly generate the input vectors for the deep learning models based on the NLP embedding methods. Nevertheless, while there are many existing embedding methods, the structures of neural networks are diverse and usually heuristic. This makes it difficult to select effective combinations of neural models and the embedding techniques for training the code vulnerability detectors. To address this challenge, we extended a benchmark system to analyze the compatibility of four popular word embedding techniques with four different neural networks, including the standard Bidirectional Long Short-Term Memory (Bi-LSTM), the Bi-LSTM applied attention mechanism, the Convolutional Neural Network (CNN), and the classic Deep Neural Network (DNN). We trained and tested the models by using two types of vulnerable function datasets written in C code. Our results revealed that the Bi-LSTM model combined with the FastText embedding technique showed the most efficient detection rate on a real-world but not on an artificially constructed dataset. Further comparisons with the other combinations are also discussed in detail in our result.
{"title":"An Extended Benchmark System of Word Embedding Methods for Vulnerability Detection","authors":"H. Nguyen, Hoang Nguyen Viet, T. Uehara","doi":"10.1145/3440749.3442661","DOIUrl":"https://doi.org/10.1145/3440749.3442661","url":null,"abstract":"Security researchers have used Natural Language Processing (NLP) and Deep Learning techniques for programming code analysis tasks such as automated bug detection and vulnerability prediction or classification. These studies mainly generate the input vectors for the deep learning models based on the NLP embedding methods. Nevertheless, while there are many existing embedding methods, the structures of neural networks are diverse and usually heuristic. This makes it difficult to select effective combinations of neural models and the embedding techniques for training the code vulnerability detectors. To address this challenge, we extended a benchmark system to analyze the compatibility of four popular word embedding techniques with four different neural networks, including the standard Bidirectional Long Short-Term Memory (Bi-LSTM), the Bi-LSTM applied attention mechanism, the Convolutional Neural Network (CNN), and the classic Deep Neural Network (DNN). We trained and tested the models by using two types of vulnerable function datasets written in C code. Our results revealed that the Bi-LSTM model combined with the FastText embedding technique showed the most efficient detection rate on a real-world but not on an artificially constructed dataset. Further comparisons with the other combinations are also discussed in detail in our result.","PeriodicalId":344578,"journal":{"name":"Proceedings of the 4th International Conference on Future Networks and Distributed Systems","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126663334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Security of information while transmitting between parties is an essential factor. The openness of the network increases the possibility of attacking information. Cryptography and steganography are the most significant methods that achieve secrecy. These mechanisms are different from each other, but both maintain data security, confidentiality, and integrity. A combination of cryptography and steganography or a combination of the same method leads to a secure data transmission by providing a multilevel of security. This paper aims to provide a new technique that focuses on achieving secrecy for data and robustness against attacks. The technique relies on the multilevel steganography approach by encrypting the data and then hiding it using an image-based and DNA-based steganography techniques.
{"title":"A Robust Double Layer Steganography Technique Based on DNA Sequences","authors":"Omnia Alharbi, Asia Othman Aljhadli, A. Manaf","doi":"10.1145/3440749.3442644","DOIUrl":"https://doi.org/10.1145/3440749.3442644","url":null,"abstract":"Security of information while transmitting between parties is an essential factor. The openness of the network increases the possibility of attacking information. Cryptography and steganography are the most significant methods that achieve secrecy. These mechanisms are different from each other, but both maintain data security, confidentiality, and integrity. A combination of cryptography and steganography or a combination of the same method leads to a secure data transmission by providing a multilevel of security. This paper aims to provide a new technique that focuses on achieving secrecy for data and robustness against attacks. The technique relies on the multilevel steganography approach by encrypting the data and then hiding it using an image-based and DNA-based steganography techniques.","PeriodicalId":344578,"journal":{"name":"Proceedings of the 4th International Conference on Future Networks and Distributed Systems","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121498652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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