Public cloud storage is a fundamental cloud computing service. Currently, most owners of large data outsource their data to cloud storage services-even high-profile owners such as governments. However, public cloud storage services are not optimal for ensuring the possession and integrity of the outsourced data, a situation that has given rise to many proposed provable data possession check schemes (PDP). A PDP scheme allows data owners to efficiently, periodically, and securely verify that a cloud storage provider possesses the outsourced data. Most of the currently available provable data possession check schemes make selective (i.e., probabilistic) checks using random data blocks to verify data integrity rather than checking the entire dataset. Therefore, these schemes are considered inadequate by critical infrastructure sectors that involve highly sensitive data (critical data). In this paper, a new and efficient deterministic data integrity check scheme called cryptographic-accumulator provable data possession (CAPDP) is proposed. The CAPDP surpasses the common limitations exhibited by other currently proposed schemes. The underlying scheme of the CAPDP is based on a modified RSA-based cryptographic accumulator that has the following advantages: 1) it allows the data owner to perform an unlimited number of data integrity checks; 2) it supports data dynamics; 3) it is efficient in terms of communication, computation and storage costs for both the data owner and the cloud storage provider; 4) the verification operation in the proposed scheme is independent of the number of blocks being verified; 5) it minimizes the burden and cost of the verification process on the data owner's side, enabling verification to be performed even on low-power devices; and 6) it prevents tag forgery, data deletion, replacement, and data leakage attacks and detects replay attacks. Moreover, the prototype implementation and experimental results show that the scheme is applicable in real-life applications.INDEX TERMS Cloud storage, cryptographic accumulator, data integrity verification, dynamic operations.

Providing complete mobility along with minimizing the poor quality of service (QoS) is one of the highest essential challenges in mobile wireless networks. Handover prediction can overcome these challenges. In this paper, two novel prediction schemes are proposed. The first, depends on scanning the quality of all signals among mobile station and all nearby stations in the surrounding area, while the second one is based on a multi-criteria prediction decision using both the signal-to-noise ratio SNR value and station’s bandwidth. Moreover, the prediction efficiency is improved by reducing the number of redundant/ unnecessary handovers. The proposed schemes are evaluated using different scenarios with several mobile stations’ numbers, different WLAN access points, LTE-base station number & location, and random mobile station movement manner. The proposed schemes achieved a success rate of 99% with the different scenarios using LTE-WLAN architecture. The performance of the proposed prediction schemes outperformed the performance of the existing prediction schemes in terms of the accuracy percentage.

Smartphones and mobile tablets play significant roles in daily life and have led to an increase in the number of users of this technology. The rising number of mobile device end-users has resulted in the generation of malware by hackers. Thus, mobile devices are becoming vulnerable to malware. Machine learning plays an important role in the detection of mobile malware applications. In this study, we focus on static analysis for Android malware detection. The ultimate goal of this research is to find out the symmetric features across the malware Android application to easily detect them. Many state-of-the-art methods focus on extracting asymmetric patterns of the category of features, e.g., application permissions to distinguish the malware application from the benign application. In this work, we propose a compromise by considering different types of static features and select the most important features that affect the detection process. These features represent the symmetric pattern to be used for the classification task. Inspired by TF-IDF, we propose a novel method of feature selection. Moreover, we propose a new method for merging the Android application URLs into a single feature called the URL_score. Several linear machine learning classifiers are utilized to evaluate the proposed method. The proposed methods significantly reduce the feature space, i.e., the symmetric pattern, of the Android application dataset and the memory size of the final model. In addition, the proposed model achieves the highest reported accuracy for the Drebin dataset to date. Based on the evaluation results, the linear support vector machine achieves an accuracy of 99%.

No abstract

No abstract