{"title":"A Secure Switch Migration Scheduling based on Prediction for Load Balancing in SDN","authors":"Rawaa Al-quraan, A. Alma'aitah","doi":"10.1109/ICICS52457.2021.9464560","DOIUrl":"https://doi.org/10.1109/ICICS52457.2021.9464560","url":null,"abstract":"The control plane of Software Defined Networking (SDN) Featured in the distributed architecture to achieve the scalability, thus the load is divided between the controllers. each controller must have the appropriate load. so, the control plane needs an accurate and beneficial design to meet the load balancing between the controllers. Thus, the switch migration is the most proposed solution for load balancing in SDN. Most of the proposed algorithms are implemented to produce efficient switch migration. In this paper, we propose a secure switch migration scheduling algorithm to enhance the load balancing and achieve the security between the switch and the destination controller. Our algorithm uses an ARIMA prediction approach to predict the load of the controllers and then according to the prediction, the switch migration operation is performed. It reduces the load for the controller which has a very large load by offloading it quickly and it makes the open-flow switch distinguishes if the destination controller is malicious or not. The results show that our algorithm enhances the load balancing compared to [10] and it confirms the security between the switch and the destination controller.","PeriodicalId":421803,"journal":{"name":"2021 12th International Conference on Information and Communication Systems (ICICS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129499457","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}
{"title":"Team Wa’ed Al-Shrida at the Mowjaz Multi-Topic Labelling Task","authors":"Wa’ed Al-Shrida","doi":"10.1109/ICICS52457.2021.9464574","DOIUrl":"https://doi.org/10.1109/ICICS52457.2021.9464574","url":null,"abstract":"This paper describes an attempt in the \"Mowjaz Multi-topic Labelling Task\", the competition is about classifying the Arabic articles to its topic by using artificial intelligence and neural networks, the programming language that was used to classify the datasets is \"Python\". The attempt was started by uploading the datasets from the \"Github website\", the datasets that were used in the system include three groups, train, validation, and test datasets. The \"Pyarabic\" and simple-transformers\" libraries were used to allow the system to manipulate Arabic letters and simplify the usage of Transformer models without having to compromise on utility, respectively. The model’s type that I used is \"Bert\" and its name is \"Asafaya/Bert-base-Arabic\". The accuracy of the result that was gotten is as follows: F1 macro: 0.864, F1 micro: 0.869, competition website on Codalab: 0.8430.","PeriodicalId":421803,"journal":{"name":"2021 12th International Conference on Information and Communication Systems (ICICS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124504396","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}
Mohammed AbuTaha, M. Ababneh, Khaled W. Mahmoud, Sherenaz W. Al-Haj Baddar
{"title":"URL Phishing Detection using Machine Learning Techniques based on URLs Lexical Analysis","authors":"Mohammed AbuTaha, M. Ababneh, Khaled W. Mahmoud, Sherenaz W. Al-Haj Baddar","doi":"10.1109/ICICS52457.2021.9464539","DOIUrl":"https://doi.org/10.1109/ICICS52457.2021.9464539","url":null,"abstract":"Phishing URLs mainly target individuals and/or organizations through social engineering attacks by exploiting the humans’ weaknesses in information security awareness. These URLs lure online users to access fake websites, and harvest their confidential information, such as debit/credit card numbers and other sensitive information. In this work, we introduce a phishing detection technique based on URL lexical analysis and machine learning classifiers. The experiments were carried out on a dataset that originally contained 1056937 labeled URLs (phishing and legitimate). This dataset was processed to generate 22 different features that were reduced further to a smaller set using different features reduction techniques. Random Forest, Gradient Boosting, Neural Network and Support Vector Machine (SVM) classifiers were all evaluated, and results show the superiority of SVMs, which achieved the highest accuracy in detecting the analyzed URLs with a rate of 99.89%. Our approach can be incorporated within add-on/middleware features in Internet browsers for alerting online users whenever they try to access a phishing website using only its URL.","PeriodicalId":421803,"journal":{"name":"2021 12th International Conference on Information and Communication Systems (ICICS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131293324","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}
{"title":"Multi-Topic Labelling Classification Based on LSTM","authors":"Duha AlBatayha","doi":"10.1109/ICICS52457.2021.9464531","DOIUrl":"https://doi.org/10.1109/ICICS52457.2021.9464531","url":null,"abstract":"The necessity for automatic classification of some resources has become extremely important given the fast-increasing number of electronic resources. People's opinions have been extracted from social media sites using Artificial Intelligence (AI). Despite this, the majority of current research focuses on extrapolating features from texts. Multi-label textual data classification is a significant problem in terms of the increasing amount of data available and the growing difficulties of assigning each text piece with one label. Examples include news and email articles. This work focuses on multi-label classification of Arabic texts. After dataset collection; several architectures were tested for this task. Bidirectional Long Short-Term Memory networks (BiLSTM) showed the superior results with F-score equal 86.6 in development set, and F1-score equal 82.24 in leaderboard Mowjaz competition.","PeriodicalId":421803,"journal":{"name":"2021 12th International Conference on Information and Communication Systems (ICICS)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127679007","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}
Khalid Alkhatib, Ahmad Al-Aiad, Mothanna Almahmoud, Omar N. Elayan
{"title":"Credit Card Fraud Detection Based on Deep Neural Network Approach","authors":"Khalid Alkhatib, Ahmad Al-Aiad, Mothanna Almahmoud, Omar N. Elayan","doi":"10.1109/ICICS52457.2021.9464555","DOIUrl":"https://doi.org/10.1109/ICICS52457.2021.9464555","url":null,"abstract":"Currently, the financial companies’ reliance has become on the new technologies and developments, in which these companies provide the customers with various services, electronically. One the most common financial services is the credit cards, where the customers can complete any financial payments using a credit card, such as; Visa card and MasterCard. However, criminals started to find ways to attack customers’ cards for fraud purposes, and that has caused a big issue for both of companies and customers. This work aim to propose a new for credit card fraud detection based on a dataset for customers’ transactions of a well-known company called Vesta, where the utilization of deep learning approaches has achieved a method with efficient performance in identifying transactions that was classified as fraud with 99.1% score of area under ROC curve.","PeriodicalId":421803,"journal":{"name":"2021 12th International Conference on Information and Communication Systems (ICICS)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121806618","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}
{"title":"Alive+: A Private Cloud Messaging System for Android Devices","authors":"Amjad Bashayreh, Nida’a Alsalman, A. Alzu’bi","doi":"10.1109/ICICS52457.2021.9464542","DOIUrl":"https://doi.org/10.1109/ICICS52457.2021.9464542","url":null,"abstract":"Conventional private messaging systems over the network often focus on the privacy problem but pay little attention to important performance qualities such as system reliability, scalability and fault tolerance. The existing systems usually meet the security requirements using verifiable shuffles to prevent a message loss or by generating an efficient noise across many servers using bloom filters. This paper introduces Alive+ system as a private cloud messaging to forward messages over the Android devices securely. Alive+ employs a cryptography scheme to guarantee the confidentiality of messaging process. Messages are encrypted and decrypted over the cloud server using AES algorithm and SHA-256 hash function to generate unique secret keys, thus strengthening Alive+ against possible network attacks. Alive+ is connected to a reliable cloud-based database designed to handle the user requests and communications efficiently. We evaluate the performance of Alive+ in terms of reliability, latency, and scalability under various scenarios and settings. The performance of Alive+ is also evaluated in terms of fault tolerance, CPU utilization, memory usage, and network traffic. The experimental results show that Alive+ is secure, reliable, and error-tolerant with high scalability levels, thereby serving a large number of users simultaneously.","PeriodicalId":421803,"journal":{"name":"2021 12th International Conference on Information and Communication Systems (ICICS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134558862","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}
{"title":"Arabic Topic Labeling using Naïve Bayes (NB)","authors":"Asseel S. Hourani","doi":"10.1109/ICICS52457.2021.9464537","DOIUrl":"https://doi.org/10.1109/ICICS52457.2021.9464537","url":null,"abstract":"The Artificial Intelligence (AI) has widely spread out in the last decades. It represents a reliable system for different problem solving. In this paper, one of the most used machine learning algorithms; Naïve Bayes (NB) is used to classify over than 7000 articles according to their topics. NB is compared to other machine learning algorithms with respect to the accuracy that results from applying both validation data and test data.","PeriodicalId":421803,"journal":{"name":"2021 12th International Conference on Information and Communication Systems (ICICS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129137833","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}
Hafed Zarzour, Bashar Al shboul, M. Al-Ayyoub, Y. Jararweh
{"title":"Sentiment Analysis Based on Deep Learning Methods for Explainable Recommendations with Reviews","authors":"Hafed Zarzour, Bashar Al shboul, M. Al-Ayyoub, Y. Jararweh","doi":"10.1109/ICICS52457.2021.9464601","DOIUrl":"https://doi.org/10.1109/ICICS52457.2021.9464601","url":null,"abstract":"Explainable recommendation systems have gained much attention in the last few years. Most of them use textual reviews to provide users with interpretability about why services or products are liked by users or recommended for them. Sentiment analysis has potential advantages to determine the attitudes of users in online communities using websites such as Twitter, Facebook, and YouTube. However, sentiment analysis of textual reviews in explainable recommendation systems seems to be a really challenging task. In this paper, we present a deep learning-based architecture for sentiment analysis to automatically predict the sentiment of reviews, which are considered as explanations of recommendations. It consists of two instances of the prediction model, one with the Long Short-Term Memory (LSTM) method and the other with the Gated Recurrent Unit (GRU) method. We evaluate their performance on one real-world dataset from Amazon and compare them with one state-of-the-art method. The experimental results show that our methods perform better than the baseline approach.","PeriodicalId":421803,"journal":{"name":"2021 12th International Conference on Information and Communication Systems (ICICS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115199752","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}
{"title":"The Intention to Use E-wallet During Covid-19 Pandemic in Developing Country","authors":"Hala Abushamleh, Narmeen Al-Hiyari, A. Qusef","doi":"10.1109/ICICS52457.2021.9464554","DOIUrl":"https://doi.org/10.1109/ICICS52457.2021.9464554","url":null,"abstract":"In developed and emerging countries, the idea of E-wallets are rapidly being implemented to increase the size and efficiency of an online transaction. Especially, after Corona Virus Disease in 2019 (COVID-19) government officials take steps to facilitate non-contact payments after World Health Organisation has urged consumers to conduct contact-less transactions, payment transfers, under physical distancing policies. This study aims to determine the impact of selected factors on intention to use E-wallet in Jordan during the Covid-19 pandemic. The four selected factors for the study are performance expectancy, perceived usefulness, perceived security, and perceived ease of use. We used an online questionnaire to gather data from Jordanian individuals and evaluated the relationships between those variables using the correlation method. Based on the results, all four variables have a clear connection with intentions to use E-Wallets and Jordanians want to use e-wallets during a pandemic, but they have some security concerns.","PeriodicalId":421803,"journal":{"name":"2021 12th International Conference on Information and Communication Systems (ICICS)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125610767","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}
{"title":"A Probability Based Topology Control Algorithm For WSN-based-IoT","authors":"A. R. Yateem, A. Alma'aitah","doi":"10.1109/ICICS52457.2021.9464559","DOIUrl":"https://doi.org/10.1109/ICICS52457.2021.9464559","url":null,"abstract":"Wireless sensor networks based Internet of Things (WSN-based-IoT) is a descendent domain from (IoT), where sensors join an (IoT) environment through wireless networks for particular and specific purposes, such as surveillance, monitoring, and much more, in various sectors in daily life, such as military, health and marine application. The criticality of sustaining control of network topology of the WSN is the most crucial factor revolved upon in numerous studies. Nevertheless, many studies show that the majority do not adopt various probability factors that affect the overall WSN quality of services as an initial step but as complementary steps, leading to many probable factors to raise at initial clustering events. In this paper, we introduce an enhancement algorithm based on debating probabilities to avoid probable factors from jeopardizing the set-up phase's stability in the LEACH algorithm within a WSN Topology.","PeriodicalId":421803,"journal":{"name":"2021 12th International Conference on Information and Communication Systems (ICICS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131416255","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}