This study aims to investigate the effect of Bluetooth on WLAN 802.11 performance. In contrast to other studies, we distinguish bluetooth into two mechanisms, namely Asynchronous Connectionless (ACL) and Synchronous Connection-Oriented (SCO). Various scenarios (with range variation between the sender node and the access point (AP) and also the presence of ACL or SCO transmission as interference) was designed to conduct experiment. In general, experiment was conducted with two nodes that act as sender and receiver node that connected through internet. In addition, to determine the effect of bluetooth on WLAN performance we use several test parameters, which are received signal strength indication (RSSI), signal to noise ratio (SNR), upstream and downstream, jitter, and packet loss rate (PLR). The study revealed the both ACL and SCO did not significantly affect WLAN performance, because they can only reduce the performance based on certain parameters and scenarios. But when they were compared, SCO has worst effect on WLAN performance, particularly on upstream, jitter, and PLR.

The network infrastructure of any organization is always under constant threat to a variety of attacks; namely, break-ins, security breach or system misuse. The Network Intrusion Detection System (NIDS) employed in a network detects such penetration attacks and intrusions within a network. Known classes of attacks can be detected easily by performing pattern matching while the unknown attacks are harder to detect. An attempt has been made to design a system using a deep learning approach for intrusion detection that not only learns but also adjusts itself to the patterns not defined earlier. Sparse auto-encoder has been used for unsupervised feature learning. Logistic classifier is then utilized for classification on NSL-KDD dataset. The performance of the system has been measured with respect to accuracy, precision and recall and the results have been found to be very promising for future use and modifications.

An unbalanced traffic load distribution leads to a degradation of network performance; most of nodes in the network are heavily loaded, resulting in a large queue, a high packet delay, and high energy consumption. The optimization of load balancing to avoid congestion has been the subject of many researches in recent years. Many authors have proposed different solutions to anticipate the failure of route in Manets by adding a function that predicts the failure of the links to distribute the traffic load on all nodes of the network. In this paper we propose an extension to the AODV-balanced protocol named AODV_SPB ? ad hoc on demand distance vector with stable path, less congested with load balancing ?, which looks for a stable and less overloaded path .A comparative study is done under the NS2 simulator with AODV and AODV-SPB. This last protocol shows its effectiveness with respect to the two protocols cited in terms of; overload, delivery rates of packets and the average of delay with 4 simulation scenarios.

Cloud computing provides and delivers a pool of on-demand and configurable resources and services that are delivered across the usage of the internet. Providing privacy and security to protect cloud assets and resources still a very challenging issue, since the distributed architecture of the cloud makes it vulnerable to the intruders. To mitigate this issue, intrusion detection systems (IDSs) play an important role in detecting the attacks in the cloud environment. In this paper, an anomaly-based network intrusion detection system (NIDS) is proposed which can monitor and analyze the network traffics flow that targets a cloud environment. The network administrator should be notified about the nature of these traffics to drop and block any intrusive network connections. Support vector machine (SVM) is employed as the classifier of the network connections. The binary-based Particle Swarm Optimization (BPSO) is adopted for selecting the most relevant network features, while the standard-based Particle Swarm Optimization (SPSO) is adopted for tuning the SVM control parameters. The benchmark NSL-KDD dataset is used as the network data source to build and evaluate the proposed system. Acceptable evaluation results state that the proposed system is characterized by detecting the intrusive network connections with high detection accuracy and low false alarm rates (FARs).

The use of wireless sensor networks has been increasing tremendously in the past decades mainly because of its applications in military, medicine, under water survey and various other fields. Depending on the applications the sensor nodes are placed in different areas and the data sensed will be sent to the base station. The process of transmitting and receiving data sensed by the sensor nodes continues till the sensors have battery life. This leads to generate data redundancy and reduces efficiency of the network. In order to overcome the limitations faced by the wireless sensor networks, the fusion of data known as data aggregation is introduced. In data aggregation, the data sensed by the various nodes are aggregated and sent to the base station as a single data packet. In this paper, a brief review on various data aggregation methods, challenges and issues are addressed. In addition to this, performance parameters of various data fusion methods to measure the efficiency of the network are discussed. The design of single aggregator models are easy compared to the multiple aggregator models. However, the security to most of the data fusion schemes is provided by using message authentication code. It also uses public keys and symmetric to achieve end to end or hop by hop encryptions.

The article presents a conceptual model for the national intellectual system aiming the safety provision of the children in Internet environment. The structucal components and work principles of the proposed model are explained. This model employs web-analytics, data sanitization (cleaning) technology, expert systems, text mining, clustering and classification methods, content filtering and etc. to protect children from harmful information in virtual environment. By using data sanitization methods, the study presents a conceptual model for obtaining more important, useful and age-corresponding information from internet resources and preventing harmful information.