Distributed denial of service (DDoS) attacks pose an increasing threat to businesses and government agencies. They harm internet businesses, limit access to information and services, and damage corporate brands. Attackers use application layer DDoS attacks that are not easily detectable because of impersonating authentic users. In this study, we address novel application layer DDoS attacks by analyzing the characteristics of incoming packets, including the size of HTTP frame packets, the number of Internet Protocol (IP) addresses sent, constant mappings of ports, and the number of IP addresses using proxy IP. We analyzed client behavior in public attacks using standard datasets, the CTU-13 dataset, real weblogs (dataset) from our organization, and experimentally created datasets from DDoS attack tools: Slow Lairs, Hulk, Golden Eyes, and Xerex. A multilayer perceptron (MLP), a deep learning algorithm, is used to evaluate the effectiveness of metrics-based attack detection. Simulation results show that the proposed MLP classification algorithm has an efficiency of 98.99% in detecting DDoS attacks. The performance of our proposed technique provided the lowest value of false positives of 2.11% compared to conventional classifiers, i.e., Naïve Bayes, Decision Stump, Logistic Model Tree, Naïve Bayes Updateable, Naïve Bayes Multinomial Text, AdaBoostM1, Attribute Selected Classifier, Iterative Classifier, and OneR.

Underwater wireless sensor networks (UWSNs) are comprised of sensor nodes that are deployed under the water having limited battery power and other limited resources. Applications of UWSNs include monitoring the quality of the water, mine detection, environment monitoring, military surveillance, disaster prediction, and underwater navigation. UWSNs are more vulnerable to security attacks as compared to their counterparts such as wireless sensor networks (WSNs). The possible attacks in UWSNs can abrupt the operation of entire network. This research work presents the analysis of relevant research done on security-based schemes in UWSNs. The security-based schemes are categorized into five sub-categories. Each technique in each category is analyzed in detail. The major contribution in each security-based scheme along with technique used, possible future research issues and implementation tool are discussed in detail. The open research issues and future trends identified and presented in this research can be further explored by the research community.

KSII Transactions on Internet and Information Systems TIIS Paper Details

This paper focus on DC-DC converter called Novel Cascaded topology. This converter lies in the category of Non-Isolated DC-DC boost converters with upgraded voltage gain based on conventional topology with LD cell for micro-source grids. The proposed novel cascaded topology increases the voltage boost ability. There are some merits of our proposed topology that includes small voltages stresses across the switch, as well as upgraded gain. We prefered to use switch having small voltage rating as well as small resistance in ON-mode (R_DS-ON), it leads to provide high efficiency. In our cascaded topology with LD cell,we did not use large duty cycle, coupling inductors as well as transformer.We will utilize the Continuous Conduction Mode (CCM) for the analysis of proposed novel cascaded topology. This topology is designed to operated on 12V supplying voltage, 55% duty cycle, 265W output power and frequency of 12KHz. The continous conduction mode (CCM) has been analyzed theoretically as well as practically based derived equations. The novel cascaded converter has been simulated in PSIM as well as MATLAB/SIMULINK, while explanations of the overall results are provided based on PSIM.

Urdu is a cursive script language and has similarities with Arabic and many other South Asian languages. Urdu is difficult to classify due to its complex geometrical and morphological structure. Character classification can be processed further if segmentation technique is efficient, but due to context sensitivity in Urdu, segmentation-based recognition often results with high error rate. Our proposed approach for Urdu optical character recognition system is a component-based classification relying on automatic feature learning technique called convolutional neural network. CNN is trained and tested on Urdu text dataset, which is generated through permutation process of three characters and further proceeds to discarding unnecessary images by applying connected component technique in order to obtain ligature only. Hierarchical neural network is implemented with two levels to deal with three degrees of character permutations and component classification Our model successfully achieved 0.99% for component classification.

Mobile Ad hoc network is the collection of nodes without having any physical structure involved i.e. access points, routers etc. MANETs are wide-open to similar forms of threats as other wireless mobile communication systems. In Ad-hoc Network nodes performing both as end-points of the communication and routers which makes the Ad-hoc routing protocols further prone towards the security attacks. Black Hole attack is a common security issue encountered in MANET routing protocols. The Black-Hole attack is security attack in which a malicious node imposters themselves as a node with the shortest hop count to the destination node during a packet transmission. A malicious node is capable of disturbing the network with Black Hole attack pretends to have the minimum hop-count route to the destination node (DS). This node responds to all route requests (RREQ) messages in positive and thus catches all the transmission to it. The source node (SN) not knowing the malicious nature of the Black-Hole node thus transmits all the important data. The Black Hole node discards all the important data packets. In this paper a comparatively effective, efficient and easy implemented way for identifying and therefore eluding the attacks of Black-Hole in mobile Ad-hoc networks is presented. The Network Simulator (NS-2) has been used for the implementation of our proposed solution to assess its work in terms of Network Routing load, End-to-End delay and Packet delivery ratio. The results show a considerable improvement in the performance metrics.