Due to the increased competition between telecommunication operators and growing customers' churn rate, telecommunication companies were seeking to improve customer loyalty. In order to increase customer satisfaction, most telecom companies resort to customer segmentation which entails separating the targeted customers into different groups based on demographics or usage perspective including gender, age-group, buying behavior, usage pattern, special interests and other features that represent the customer. Customer segmentation adopted by most telecom operators to provide the customer with the right offer.
Signature-based Intrusion detection systems are not suitable anymore to be used in nowadays network environment. Because signature-based models are not able to detect new threats and unknown attacks. Due to technology improvement, the number of attacks is increasing exponentially. Statistics show that attacks number increases with a rate of 100% each year causing huge money loss, about tens of millions of dollars for ransomware attacks only. This high number of millions of new threats that are developed every day, reduces the effectiveness of signature-based IDS because it is not a practical solution to update the signatures databases every few minutes. Anomaly-based IDS can be a better alternative of signature-based IDS because it is more suitable for nowadays Abstract Anomaly-based Intrusion Detection System (IDS) has been a hot research topic because of its ability to detect new threats rather than only memorized signatures threats of signature-based IDS. Especially after the availability of advanced technologies that increase the number of hacking tools and increase the risk impact of an attack. The problem of any anomaly-based model is its high false-positive rate. The high false-positive rate is the reason why anomaly IDS is not commonly applied in practice. Because anomaly-based models classify an unseen pattern as a threat where it may be normal but not included in the training dataset. This type of problem is called overfitting where the model is not able to generalize. Optimizing Anomaly-based models by having a big training dataset that includes all possible normal cases may be an optimal solution but could not be applied in practice. Although we can increase the number of training samples to include much more normal cases, still we need a model that has more ability to generalize. In this research paper, we propose applying deep model instead of traditional models because it has more ability to generalize. Thus, we will obtain less false-positive by using big data and deep model. We made a comparison between machine learning and deep learning algorithms in the optimization of anomaly-based IDS by decreasing the false-positive rate. We did an experiment on the NSL-KDD benchmark and compared our results with one of the best used classifiers in traditional learning in IDS optimization. The experiment shows 10% lower false-positive by using deep learning instead of traditional learning.
Recently, we have seen lots of real-life examples of attacks' huge impacts in different domains such as politics and economics. Hacking has become more critical and more dangerous than ever before. The number of hacking attacks is growing exponentially every few months. That means signature-based IDS is not useful anymore as we cannot update it with new signatures every few minutes. Also with developing technologies attacks become more sophisticated, APT attacks are more common than ever before.
This paper presents fraud detection problem as one of the most common problems in secure banking research field, due to its importance in reducing the losses of banks and e-transactions companies. Our work will include: applying the common classification algorithms such as logistic regression (LR), random forest (RF), alongside with modern classifiers with state-of-the-art results as XGBoost (XG) and CatBoost (CB), testing the effect of the unbalanced data through comparing their results with and without balancing, then focusing on the savings measure to test the effect of cost-sensitive wrapping of Bayes minimum risk (BMR), we will concentrate on using F1-score, AUC and Savings measures after using the traditional measures duo to their suitability to our problem. The results show that CB has the best savings (0.7158) alone, (0.971) when using SMOTE and (0.9762) with SMOTE and BMR, while XG has the best savings (0.757) when using BMR without SMOTE.
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