The enormous increase in the use of the Internet in daily life has provided an opportunity for the intruder attempt to compromise the security principles of availability, confidentiality, and integrity. As a result, organizations are working to increase the level of security by using attack detection techniques such as Network Intrusion Detection System (NIDS), which monitors and analyzes network flow and attacks detection. There are a lot of researches proposed to develop the NIDS and depend on the dataset for the evaluation. Datasets allow evaluating the ability in detecting intrusion behavior. This paper introduces a detailed analysis of benchmark and recent datasets for NIDS. Specifically, we describe eight well-known datasets that include: KDD99, NSL-KDD, KYOTO 2006+, ISCX2012, UNSW-NB 15, CIDDS-001, CICIDS2017, and CSE-CIC-IDS2018. For each dataset, we provide a detailed analysis of its instances, features, classes, and the nature of the features. The main objective of this paper is to offer overviews of the datasets are available for the NIDS and what each dataset is comprised of. Furthermore, some recommendations were made to use network-based datasets.

In fact, most of information retrieval systems retrieve documents based on keywords matching, which are certainly fail at retrieving documents that have similar meaning with syntactical different keywords (form). One of the well-known approaches to overcome this limitation is query expansion (QE). There are several approaches in query expansion field such as statistical approach. This approach depends on term frequency to generate expansion features; nevertheless it does not consider meaning or term dependency. In addition, there are other approaches such as semantic approach which depends on a knowledge base that has a limited number of terms and relations. In this paper, researchers propose a hybrid approach for query expansion which utilizes both statistical and semantic approach. To select the optimal terms for query expansion, researchers propose an effective weighting method based on particle swarm optimization (PSO). A system prototype was implemented as a proof-of-concept, and its accuracy was evaluated. The experimental was carried out based on real dataset. The experimental results confirm that the proposed approach enhances the accuracy of query expansion.

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in recent years, a lot of research is focused on wireless sensor network applications, which is focused on field of performance, security, and energy. This paper addressed the difficulties and challenges facing the wireless sensor networks on the battlefield. Which is often vulnerable to attacker's networks either in the data or corrupting control devices and attempt to consume a lot of energy by sending a large quantity of useless packets, which contributes to excessive consumption of energy and leads to exit nodes from work. Since technology has become widespread on battlefields at the present time, then the sensor nodes are vulnerable to attackers from both sides. This research discussed many challenges and gave appropriate solutions. The simulations showed that these solutions can help secure data and saved 40% of energy consumed.