The world has experienced a radical change due to the internet. As a matter of fact, it assists p... more The world has experienced a radical change due to the internet. As a matter of fact, it assists people in maintaining their social networks and links them to other members of their social networks when they require assistance. In effect sharing professional and personal data comes with several risks to individuals and organizations. Internet became a crucial element in our daily life, therefore, the security of our DATA could be threatened at any time. For this reason, IDS plays a major role in protecting internet users against any malicious network attacks. (IDS) Intrusion Detection System is a system that monitors network traffic for suspicious activity and issues alerts when such activity is discovered. In this paper, the focus will be on three different classifications; starting by machine learning, algorithms NB, SVM and KNN. These algorithms will be used to define the best accuracy by means of the USNW NB 15 DATASET in the first stage. Based on the result of the first stage, the second one is used to process our database with the most efficient algorithm. Two different datasets will be operated in our experiments to evaluate the model performance. NSL-KDD and UNSW-NB15 datasets are used to measure the performance of the proposed approach in order to guarantee its efficiency.
International Journal of Online and Biomedical Engineering (iJOE)
In the last decades, many works have been done to enhance data performances in the computer field... more In the last decades, many works have been done to enhance data performances in the computer field. Data performance consists to describe all improvements which can be added to data traffic. More precisely, we are talking about techniques allowing improving the evaluation of big data using machine learning. Data evaluation is composed of several variables such as security, quality of service, data synchronization, scalability, and data structuring. In this work, we complete our proceedings done to supervise the continuity of technological evolution in terms of big data and safety. In other words, we aim to add brick to our previous processes to take into consideration the enhancement of the analysis of the causes generating frauds and intrusions preventing data traffic. To achieve this end, we increase current machine learning techniques with prior knowledge based on data thresholds set by experts in the first place. We also aim to integrate knowledge facilitating the interpretation ...
The world has experienced a radical change due to the internet. As a matter of fact, it assists p... more The world has experienced a radical change due to the internet. As a matter of fact, it assists people in maintaining their social networks and links them to other members of their social networks when they require assistance. In effect sharing professional and personal data comes with several risks to individuals and organizations. Internet became a crucial element in our daily life, therefore, the security of our DATA could be threatened at any time. For this reason, IDS plays a major role in protecting internet users against any malicious network attacks. (IDS) Intrusion Detection System is a system that monitors network traffic for suspicious activity and issues alerts when such activity is discovered. In this paper, the focus will be on three different classifications; starting by machine learning, algorithms NB, SVM and KNN. These algorithms will be used to define the best accuracy by means of the USNW NB 15 DATASET in the first stage. Based on the result of the first stage, t...
Communications in Computer and Information Science, 2011
Background: Severe obesity is a global public health threat of growing proportions. Accurate mode... more Background: Severe obesity is a global public health threat of growing proportions. Accurate models to predict severe postoperative complications could be of value in the preoperative assessment of potential candidates for bariatric surgery. So far, traditional statistical methods have failed to produce high accuracy. We aimed to find a useful machine learning (ML) algorithm to predict the risk for severe complication after bariatric surgery. Methods: We trained and compared 29 supervised ML algorithms using information from 37,811 patients that operated with a bariatric surgical procedure between 2010 and 2014 in Sweden. The algorithms were then tested on 6250 patients operated in 2015. We performed the synthetic minority oversampling technique tackling the issue that only 3% of patients experienced severe complications. Results: Most of the ML algorithms showed high accuracy (>90%) and specificity (>90%) in both the training and test data. However, none of the algorithms achieved an acceptable sensitivity in the test data. We also tried to tune the hyperparameters of the algorithms to maximize sensitivity, but did not yet identify one with a high enough sensitivity that can be used in clinical praxis in bariatric surgery. However, a minor, but perceptible, improvement in deep neural network (NN) ML was found. Conclusion: In predicting the severe postoperative complication among the bariatric surgery patients, ensemble algorithms outperform base algorithms. When compared to other ML algorithms, deep NN has the potential to improve the accuracy and it deserves further investigation. The oversampling technique should be considered in the context of imbalanced data where the number of the interested outcome is relatively small.
Bulletin of the Polish Academy of Sciences Mathematics, 2010
Presented by Czes law BESSAGA Summary. It is shown that deleting a point from the topologist's si... more Presented by Czes law BESSAGA Summary. It is shown that deleting a point from the topologist's sine curve results in a locally compact connected space whose autohomeomorphism group is not a topological group when equipped with the compact-open topology.
The world has experienced a radical change due to the internet. As a matter of fact, it assists p... more The world has experienced a radical change due to the internet. As a matter of fact, it assists people in maintaining their social networks and links them to other members of their social networks when they require assistance. In effect sharing professional and personal data comes with several risks to individuals and organizations. Internet became a crucial element in our daily life, therefore, the security of our DATA could be threatened at any time. For this reason, IDS plays a major role in protecting internet users against any malicious network attacks. (IDS) Intrusion Detection System is a system that monitors network traffic for suspicious activity and issues alerts when such activity is discovered. In this paper, the focus will be on three different classifications; starting by machine learning, algorithms NB, SVM and KNN. These algorithms will be used to define the best accuracy by means of the USNW NB 15 DATASET in the first stage. Based on the result of the first stage, the second one is used to process our database with the most efficient algorithm. Two different datasets will be operated in our experiments to evaluate the model performance. NSL-KDD and UNSW-NB15 datasets are used to measure the performance of the proposed approach in order to guarantee its efficiency.
International Journal of Online and Biomedical Engineering (iJOE)
In the last decades, many works have been done to enhance data performances in the computer field... more In the last decades, many works have been done to enhance data performances in the computer field. Data performance consists to describe all improvements which can be added to data traffic. More precisely, we are talking about techniques allowing improving the evaluation of big data using machine learning. Data evaluation is composed of several variables such as security, quality of service, data synchronization, scalability, and data structuring. In this work, we complete our proceedings done to supervise the continuity of technological evolution in terms of big data and safety. In other words, we aim to add brick to our previous processes to take into consideration the enhancement of the analysis of the causes generating frauds and intrusions preventing data traffic. To achieve this end, we increase current machine learning techniques with prior knowledge based on data thresholds set by experts in the first place. We also aim to integrate knowledge facilitating the interpretation ...
The world has experienced a radical change due to the internet. As a matter of fact, it assists p... more The world has experienced a radical change due to the internet. As a matter of fact, it assists people in maintaining their social networks and links them to other members of their social networks when they require assistance. In effect sharing professional and personal data comes with several risks to individuals and organizations. Internet became a crucial element in our daily life, therefore, the security of our DATA could be threatened at any time. For this reason, IDS plays a major role in protecting internet users against any malicious network attacks. (IDS) Intrusion Detection System is a system that monitors network traffic for suspicious activity and issues alerts when such activity is discovered. In this paper, the focus will be on three different classifications; starting by machine learning, algorithms NB, SVM and KNN. These algorithms will be used to define the best accuracy by means of the USNW NB 15 DATASET in the first stage. Based on the result of the first stage, t...
Communications in Computer and Information Science, 2011
Background: Severe obesity is a global public health threat of growing proportions. Accurate mode... more Background: Severe obesity is a global public health threat of growing proportions. Accurate models to predict severe postoperative complications could be of value in the preoperative assessment of potential candidates for bariatric surgery. So far, traditional statistical methods have failed to produce high accuracy. We aimed to find a useful machine learning (ML) algorithm to predict the risk for severe complication after bariatric surgery. Methods: We trained and compared 29 supervised ML algorithms using information from 37,811 patients that operated with a bariatric surgical procedure between 2010 and 2014 in Sweden. The algorithms were then tested on 6250 patients operated in 2015. We performed the synthetic minority oversampling technique tackling the issue that only 3% of patients experienced severe complications. Results: Most of the ML algorithms showed high accuracy (>90%) and specificity (>90%) in both the training and test data. However, none of the algorithms achieved an acceptable sensitivity in the test data. We also tried to tune the hyperparameters of the algorithms to maximize sensitivity, but did not yet identify one with a high enough sensitivity that can be used in clinical praxis in bariatric surgery. However, a minor, but perceptible, improvement in deep neural network (NN) ML was found. Conclusion: In predicting the severe postoperative complication among the bariatric surgery patients, ensemble algorithms outperform base algorithms. When compared to other ML algorithms, deep NN has the potential to improve the accuracy and it deserves further investigation. The oversampling technique should be considered in the context of imbalanced data where the number of the interested outcome is relatively small.
Bulletin of the Polish Academy of Sciences Mathematics, 2010
Presented by Czes law BESSAGA Summary. It is shown that deleting a point from the topologist's si... more Presented by Czes law BESSAGA Summary. It is shown that deleting a point from the topologist's sine curve results in a locally compact connected space whose autohomeomorphism group is not a topological group when equipped with the compact-open topology.
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Papers by rachid tahri