No abstract

Selective forwarding is a major problem in wireless sensor networks (WSNs). The nature of sensor environments and the sensitivity of collected measurements in some fields such as war fields increase the need to prevent, detect, or mitigate the problem. One of the most used countermeasures for such problem is the use of voting system based on watchdogs' votes. However, this approach is not applicable in the case of mobile sensors. Mobile WSNs (MWSNs) is growing immensely due to the exposure of applications of mobile computing, vehicular networks, and Internet of things. This exposure has shed light on the security of using mobile sensors and raises the need to set appropriate methods for securing MWSNs against many attacks such as selective forwarding attacks. This paper introduces the problem of selective forwarding in MWSNs and discusses how the voting system used for mitigation; this problem in WSNs is not applicable in handling the problem in MWSNs due to sensors mobility. Therefore, the paper proposes a model that provides a global monitoring capability for tracing moving sensors and detecting malicious ones. The model leverages the infrastructure of fog computing to achieve this purpose. In addition, the paper suggests using software defined systems to be used along with the proposed model, which generalize the model to be used to secure MWSNs against other types of attacks easily and flexibly. The paper provides a complete algorithm, a comprehensive discussion and experiments that show the correctness and importance of the proposed approach.

No abstract

No abstract

This paper investigates the problem of knowledge acquisition by an unauthorized insider using dependencies between objects in relational databases. It defines various types of knowledge. In addition, it introduces the Neural Dependency and Inference Graph (NDIG), which shows dependencies among objects and the amount of knowledge that can be inferred about them using dependency relationships. Moreover, it introduces an algorithm to determine the knowledgebase of an insider and explains how insiders can broaden their knowledge about various relational database objects to which they lack appropriate access privileges. In addition, it demonstrates how NDIGs and knowledge graphs help in assessment of insider threats and what security officers can do to avoid such threats.

The performance of spatial queries depends mainly on the underlying index structure used to handle them. R-tree, a well-known spatial index structure, suffers largely from high overlap and high coverage resulting mainly from splitting the overflowed nodes. Assigning the remaining entries to the underflow node in order to meet the R-tree minimum fill constraint ( Remaining Entries problem) may induce high overlap or high coverage. This is done without considering the geometric features of the remaining entries and this may cause a very non-optimized expansion of that particular node. This paper presents a solution to the above problem. The proposed solution to this problem distributes rectangles as follows: (1) assign m entries to the first node, which are nearest to the first seed; (2) assign other m entries to the second node, which are nearest to the second seed; (3) assign the remaining entries one by one to the nearest seed. Several experiments on real data, as well as synthetic data, show that the proposed splitting algorithm outperforms the efficient version of the original R-tree in terms of query performance.

Most research in Arabic roots extraction focuses on removing affixes from Arabic words. This process adds processing overhead and may remove non-affix letters, which leads to the extraction of incorrect roots. This paper advises a new approach to dealing with this issue by introducing a new algorithm for extracting Arabic words’ roots. The proposed algorithm, which is called the Word Substring Stemming Algorithm, does not remove affixes during the extraction process. Rather, it is based on producing the set of all substrings of an Arabic word, and uses the Arabic roots file, the Arabic patterns file and a concrete set of rules to extract correct roots from substrings. The experiments have shown that the proposed approach is competitive and its accuracy is 83.9%, Furthermore, its accuracy can be enhanced more in the sense that, for about 9.9% of the tested words, the WSS algorithm retrieves two candidates (in most cases) for the correct root.

No abstract