Software defined networks (SDN) are a recently developed form for controlling network management by providing centralized control unit called the Controller. This master Controller is a great power... Show More

Software defined networks (SDN) are a recently developed form for controlling network management by providing centralized control unit called the Controller. This master Controller is a great power point but at the same time it is unfortunately a failure point and a serious loophole if it is targeted and dropped by attacks. One of the most serious types of attacks is the inability to access the Controller, which is known as the distributed denial of service (DDoS) attack. This research shows how DDoS attack can deplete the resources of the Controller and proposes a lightweight mechanism, which works at the Controller and detects a DDoS attack in the early stages. The proposed mechanism can not only detect the attack, but also identify attack paths and initiate a mitigation process to provide some degree of protection to network devices immediately after the attack is detected. The proposed mechanism depends on a hybrid technique that merges between the average flow initiation rate, and the flow specification of the coming traffic to the network.

Digital image forgery is a serious problem of an increasing attention from the research society. Image splicing is a well-known type of digital image forgery in which the forged image is synthesized... Show More

Digital image forgery is a serious problem of an increasing attention from the research society. Image splicing is a well-known type of digital image forgery in which the forged image is synthesized from two or more images. Splicing forgery detection is more challenging when compared with other forgery types because the forged image does not contain any duplicated regions. In addition, unavailability of source images introduces no evidence about the forgery process. In this study, an automated image splicing forgery detection scheme is presented. It depends on extracting the feature of images based on the analysis of color filter array (CFA). A feature reduction process is performed using principal component analysis (PCA) to reduce the dimensionality of the resulting feature vectors. A deep belief network-based classifier is built and trained to classify the tested images as authentic or spliced images. The proposed scheme is evaluated through a set of experiments on Columbia Image Splicing Detection Evaluation Dataset (CISDED) under different scenarios including adding postprocessing on the spliced images such JPEG compression and Gaussian Noise. The obtained results reveal that the proposed scheme exhibits a promising performance with 95.05% precision, 94.05% recall, 94.05% true positive rate, and 98.197% accuracy. Moreover, the obtained results show the superiority of the proposed scheme compared to other recent splicing detection method.

High data rate communication systems usually implement Orthogonal Frequency Division Multiplexing (OFDM) to face frequency selectivity. High Peak to Average Power Ratio (PAPR) is an OFDM... Show More

High data rate communication systems usually implement Orthogonal Frequency Division Multiplexing (OFDM) to face frequency selectivity. High Peak to Average Power Ratio (PAPR) is an OFDM disadvantage that causes Bit Error Rate (BER) degradation and out-of-band (OOB) radiation when OFDM signal pass through nonlinear Power Amplifier (PA). In order to overcome this problem larger Input Back-Off (IBO) is required. However, large IBO decreases the PA efficiency. PAPR reduction techniques are used to reduce the required IBO, so that PA efficiency is saved. Several PAPR reduction methods are introduced in literature, among them Tone Reservation based on Null Subcarriers (TRNS) is downward compatible version of Tone Reservation (TR) with small excess in the average power and low computational complexity compared to others. As will be shown, TRNS is the best practical one of the four downward compatible techniques. Performance of TRNS is controlled by three parameters; number of peak reduction tones (PRTs), predefined threshold (Amax), and number of iterations (Itr). In order to increase PAPR reduction gain, enhance BER performance, and reduce the required IBO to follow the given power spectral density (PSD), we have to choose the values of these parameters adequately. Results showed that, we have to reduce the threshold value to the average (i.e. Amax =0 dB). Also, we have to increase number of PRTs. However, we have to maintain the spectrum shape. Finally, we have to choose moderate number of iterations (e.g. Itr ≈50), as excessive increase in number of iterations is not useful, especially at high PAPR values.

In mobile wireless networks, the challenge of providing full mobility without affecting the quality of service (QoS) is becoming essential. These challenges can be overcome using handover... Show More

In mobile wireless networks, the challenge of providing full mobility without affecting the quality of service (QoS) is becoming essential. These challenges can be overcome using handover prediction. The process of determining the next station which mobile user desires to transfer its data connection can be termed as handover prediction. A new proposed prediction scheme is presented in this article dependent on scanning all signal quality between the mobile user and all neighboring stations in the surrounding areas. Additionally, the proposed scheme efficiency is enhanced essentially for minimizing the redundant handover (unnecessary handovers) numbers. Both WLAN and long term evolution (LTE) networks are used in the proposed scheme which is evaluated using various scenarios with several numbers and locations of mobile users and with different numbers and locations of WLAN access point and LTE base station, all randomly. The proposed prediction scheme achieves a success rate of up to 99% in several scenarios consistent with LTE-WLAN architecture. To understand the network characteristics for enhancing efficiency and increasing the handover successful percentage especially with mobile station high speeds, a neural network model is used. Using the trained network, it can predict the next target station for heterogeneous network handover points. The proposed neural network-based scheme added a significant improvement in the accuracy ratio compared to the existing schemes using only the received signal strength (RSS) as a parameter in predicting the next station. It achieves a remarkable improvement in successful percentage ratio up to 5% compared with using only RSS.

Big Data emerged after a big explosion of data from the Web 2.0, digital sensors, and social media applications such as Facebook, Twitter, etc. In this constant growth of data, many domains are... Show More

Big Data emerged after a big explosion of data from the Web 2.0, digital sensors, and social media applications such as Facebook, Twitter, etc. In this constant growth of data, many domains are influenced, especially the decisional support system domain, where the integration of processes should be adapted to support this huge amount of data to improve analysis goals. The basic purpose of this research article is to adapt extract-transform-load processes with Big Data technologies, in order to support not only this evolution of data but also the knowledge discovery. In this article, a new approach called Big Dimensional ETL (BigDimETL) is suggested to deal with ETL basic operations and take into account the multidimensional structure. In order to accelerate data handling, the MapReduce paradigm is used to enhance data warehousing capabilities and HBase as a distributed storage mechanism. Experimental results confirm that the ETL operation performs well especially with adapted operations.