Characterization of Differentially Private Logistic Regression

The purpose of this paper is to present an approach that can help data owners select suitable values for the privacy parameter of a differentially private logistic regression (DPLR), whose main intention is to achieve a balance between privacy strength and classification accuracy. The proposed approach implements a supervised learning technique and a feature extraction technique to address this challenging problem and generate solutions. The supervised learning technique selects subspaces from a training data set and generates DPLR classifiers for a range of values of the privacy parameter. The feature extraction technique transforms an original subspace to a differentially private subspace by querying the original subspace multiple times using the DPLR model and the privacy parameter values that were selected by the supervised learning module. The proposed approach then employs a signal processing technique called signal-interference-ratio as a measure to quantify the privacy level of the differentially private subspaces; hence, allows data owner learn the privacy level that the DPLR models can provide for a given subspace and a given classification accuracy

Dynamic Hierarchical Cache Management for Cloud RAN and Multi- Access Edge Computing in 5G Networks

Cloud Radio Access Networks (CRAN) and Multi-Access Edge Computing (MEC) are two of the many emerging technologies that are proposed for 5G mobile networks. CRAN provides scalability, flexibility, and better resource utilization to support the dramatic increase of Internet of Things (IoT) and mobile devices. MEC aims to provide low latency, high bandwidth and real- time access to radio networks. Cloud architecture is built on top of traditional Radio Access Networks (RAN) to bring the idea of CRAN and in MEC, cloud computing services are brought near users to improve the user’s experiences. A cache is added in both CRAN and MEC architectures to speed up the mobile network services. This research focuses on cache management of CRAN and MEC because there is a necessity to manage and utilize this limited cache resource efficiently. First, a new cache management algorithm, H-EXD-AHP (Hierarchical Exponential Decay and Analytical Hierarchy Process), is proposed to improve the existing EXD-AHP algorithm. Next, this paper designs three dynamic cache management algorithms and they are implemented on the proposed algorithm: H-EXD-AHP and an existing algorithm: H-PBPS (Hierarchical Probability Based Popularity Scoring). In these proposed designs, cache sizes of the different Service Level Agreement (SLA) users are adjusted dynamically to meet the guaranteed cache hit rate set for their corresponding SLA users. The minimum guarantee of cache hit rate is for our setting. Net neutrality, prioritized treatment will be in common practice. Finally, performance evaluation results show that these designs achieve the guaranteed cache hit rate for differentiated users according to their SLA

On the Security of Bluetooth Low Energy in Two Consumer Wearable Heart Rate Monitors/Sensing Devices

Since its inception in 2013, Bluetooth Low Energy (BLE) has become the standard for short-distance wireless communication in many consumer devices, as well as special-purpose devices. In this study, we analyze the security features available in Bluetooth LE standards and evaluate the features implemented in two BLE wearable devices (a Fitbit heart rate wristband and a Polar heart rate chest wearable) and a BLE keyboard to explore which security features in the BLE standards are implemented in the devices. In this study, we used the ComProbe Bluetooth Protocol Analyzer, along with the ComProbe software to capture the BLE traffic of these three devices. We found that even though the standards provide security mechanisms, because the Bluetooth Special Interest Group does not require that manufacturers fully comply with the standards, some manufacturers fail to implement proper security mechanisms. The circumvention of security in Bluetooth devices could leak private data that could be exploited by rogue actors/hackers, thus creating security, privacy, and, possibly, safety issues for consumers and the public. We propose the design of a Bluetooth Security Facts Label (BSFL) to be included on a Bluetooth/BLE enabled device’s commercial packaging and conclude that there should be better mechanisms for informing users about the security and privacy provisions of the devices they acquire and use and to educate the public on protection of their privacy when buying a connected device

Efficient Mining Support-Confidence Based Framework Generalized Association Rules

Mining association rules are one of the most critical data mining problems, intensively studied since their inception. Several approaches have been proposed in the literature to extend the basic association rule framework to extract more general rules, including the negation operator. Thereby, this extension is expected to bring valuable knowledge about an examined dataset to the user. However, the efficient extraction of such rules is challenging, especially for sparse datasets. This paper focuses on the extraction of literalsets, i.e., a set of present and absent items. By consequence, generalized association rules can be straightforwardly derived from these literalsets. To this end, we introduce and prove the soundness of a theorem that paves the way to speed up the costly computation of the support of a literalist. Furthermore, we introduce FasterIE, an efficient algorithm that puts the proved theorem at work to efficiently extract the whole set of frequent literalets. Thus, the FasterIE algorithm is shown to devise very efficient strategies, which minimize as far as possible the number of node visits in the explored search space. Finally, we have carried out experiments on benchmark datasets to back the effectiveness claim of the proposed algorithm versus its competitors

Benchmark-Based Reference Model for Evaluating Botnet Detection Tools Driven by Traffic-Flow Analytics

Botnets are some of the most recurrent cyber-threats, which take advantage of the wide heterogeneity of endpoint devices at the Edge of the emerging communication environments for enabling the malicious enforcement of fraud and other adversarial tactics, including malware, data leaks or denial of service. There have been significant research advances in the development of accurate botnet detection methods underpinned on supervised analysis but assessing the accuracy and performance of such detection methods requires a clear evaluation model in the pursuit of enforcing proper defensive strategies. In order to contribute to the mitigation of botnets, this paper introduces a novel evaluation scheme grounded on supervised machine learning algorithms that enable the detection and discrimination of different botnets families on real operational environments. The proposal relies on observing, understanding and inferring the behavior of each botnet family based on network indicators measured at flow-level. The assumed evaluation methodology contemplates six phases that allow building a detection model against botnet-related malware distributed through the network, for which five supervised classifiers were instantiated were instantiated for further comparisons—Decision Tree, Random Forest, Naive Bayes Gaussian, Support Vector Machine and K-Neighbors. The experimental validation was performed on two public datasets of real botnet traffic—CIC-AWS-2018 and ISOT HTTP Botnet. Bearing the heterogeneity of the datasets, optimizing the analysis with the Grid Search algorithm led to improve the classification results of the instantiated algorithms. An exhaustive evaluation was carried out demonstrating the adequateness of our proposal which prompted that Random Forest and Decision Tree models are the most suitable for detecting different botnet specimens among the chosen algorithms. They exhibited higher precision rates whilst analyzing a large number of samples with less processing time. The variety of testing scenarios were deeply assessed and reported to set baseline results for future benchmark analysis targeted on flow-based behavioral patterns

ENHANCE NMF-BASED RECOMMENDATION SYSTEMS WITH AUXILIARY INFORMATION IMPUTATION

This dissertation studies the factors that negatively impact the accuracy of the collaborative filtering recommendation systems based on nonnegative matrix factorization (NMF). The keystone in the recommendation system is the rating that expresses the user\u27s opinion about an item. One of the most significant issues in the recommendation systems is the lack of ratings. This issue is called cold-start issue, which appears clearly with New-Users who did not rate any item and New-Items, which did not receive any rating. The traditional recommendation systems assume that users are independent and identically distributed and ignore the connections among users whereas the recommendation actually is a social activity. This dissertation aims to enhance NMF-based recommendation systems by utilizing the imputation method and limiting the errors that are introduced in the system. External information such as trust network and item categories are incorporated into NMF-based recommendation systems through the imputation. The proposed approaches impute various subsets of the missing ratings. The subsets are defined based on the total number of the ratings of the user or item before the imputation, such as impute the missing ratings of New-Users, New-Items, or cold-start users or items that suffer from the lack of the ratings. In addition, several factors are analyzed that affect the prediction accuracy when the imputation method is utilized with NMF-based recommendation systems. These factors include the total number of the ratings of the user or item before the imputation, the total number of imputed ratings for each user and item, the average of imputed rating values, and the value of imputed rating values. In addition, several strategies are applied to select the subset of missing ratings for the imputation that lead to increasing the prediction accuracy and limiting the imputation error. Moreover, a comparison is conducted with some popular methods that are in common with the proposed method in utilizing the imputation to handle the lack of ratings, but they differ in the source of the imputed ratings. Experiments on different large-size datasets are conducted to examine the proposed approaches and analyze the effects of the imputation on accuracy. Users and items are divided into three groups based on the total number of the ratings before the imputation is applied and their recommendation accuracy is calculated. The results show that the imputation enhances the recommendation system by capacitating the system to recommend items to New-Users, introduce New-Items to users, and increase the accuracy of the cold-start users and items. However, the analyzed factors play important roles in the recommendation accuracy and limit the error that is introduced from the imputation