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Software-defined networking (SDN) is a trending networking paradigm that focuses on decoupling of the control logic from the data plane. This decoupling brings programmability and flexibility for the network management by introducing centralized infrastructure. The complete control logic resides in the controller, and thus it becomes the intellectual and most important entity of the SDN infrastructure. With these advantages, SDN faces several security issues in various SDN layers that may prevent the growth and global adoption of this groundbreaking technology. Control plane exhaustion and switch buffer overflow are examples of such security issues. Distributed denial-of-service (DDoS) attacks are one of the most severe attacks that aim to exhaust the controller's CPU to discontinue the whole functioning of the SDN network. Hence, it is necessary to design a quick as well as accurate detection scheme to detect the attack traffic at an early stage. In this paper, we present a defense solution to detect and mitigate spoofed flooding DDoS attacks. The proposed defense solution is implemented in the SDN controller. The detection method is based on the idea of an statistical measure — Interquartile Range (IQR). For the mitigation purpose, the existing SDN-in-built capabilities are utilized. In this work, the experiments are performed considering the spoofed SYN flooding attack. The proposed solution is evaluated using different performance parameters, i.e., detection time, detection accuracy, packet_in messages, and CPU utilization. The experimental results reveal that the proposed defense solution detects and mitigates the attack effectively in different attack scenarios.

Epilepsy is one of the most occurring neurological diseases. The main characteristic of this disease is a frequent seizure, which is an electrical imbalance in the brain. It is generally accompanied by shaking of body parts and even leads (fainting). In the past few years, many treatments have come up. These mainly involve the use of anti-seizure drugs for controlling seizures. But in 70% of cases, these drugs are not effective, and surgery is the only solution when the condition worsens. So patients need to take care of themselves while having a seizure and be safe. Wearable electroencephalogram (EEG) devices have come up with the development in medical science and technology. These devices help in the analysis of brain electrical activities. EEG helps in locating the affected cortical region. The most important is that it can predict any seizure in advance on-site. This has resulted in a sudden increase in demand for effective and efficient seizure prediction and diagnosis systems. A novel approach to epileptic seizure prediction and diagnosis system “EpilNet” is proposed in the present paper. It is a one-dimensional (1D) convolution neural network. EpilNet gives the testing accuracy of 79.13% for five classes, leading to a significant increase of about 6-7% compared to related works. The developed Web API helps in bringing EpilNet into practical use. Thus, it is an integrated system for both patients and doctors. The system will help patients prevent injury or accidents and increase the efficiency of the treatment process by doctors in the hospitals.

American Sign Language (ASL) is widely used for communication by deaf and mute people. In fingerspelling, the letters of the writing system are represented using only hands. Generally, hearing people do not understand sign language and this creates a communication gap between the signer and speaker community. A real-time ASL fingerspelling recognizer can be developed to solve this problem. Sign language recognizer can also be trained for other applications such as human-computer interaction. In this paper, a hybrid Discrete Wavelet TransformGabor filter is used on the colour images to extract features. Classifiers are evaluated on signer dependent and independent datasets. For evaluation, it is very important to consider signer dependency. Random Forest, Support Vector Machine and K-Nearest Neighbors classifiers are evaluated on the extracted set of features to classify the 24 classes of ASL alphabets with 95.8%, 94.3% and 96.7% accuracy respectively on signer dependent dataset and 49.16%, 48.75% and 50.83% accuracy respectively on signer independent dataset. Lastly, Convolutional Neural Network was also trained and evaluated on both, which produced 97.01% accuracy on signer dependent and 76.25% accuracy on signer independent dataset.

The failure rate of sensor nodes in Heterogeneous Wireless Sensor Networks is high due to the use of low battery-powered sensor nodes in a hostile environment. Networks of this kind become non-operational and turn into disjoint segmented networks due to large-scale failures of sensor nodes. This may require the placement of additional highpower relay nodes. In this paper, we propose a network partition recovery solution called Grey Wolf, which is an optimizer algorithm for repairing segmented heterogeneous wireless sensor networks. The proposed solution provides not only strong bi-connectivity in the damaged area, but also distributes traffic load among the multiple deployed nodes to enhance the repaired network’s lifetime. The experiment results show that the Grey Wolf algorithm offers a considerable performance advantage over other state-of-the-art approaches.

The lost connectivity due to failure of large-scale nodes plays major role to degrade the system performance by generating unnecessary overhead or sometimes totally collapse the active network. There are many issues and challenges to restore the lost connectivity in an unattended scenario, i.e. how many recovery nodes will be sufficient and on which locations these recovery nodes have to be placed. A very few centralized and distributed approaches have been proposed till now. The centralized approaches are good for a scenario where information about the disjoint network, i.e. number of disjoint segments and their locations are well known in advance. However, for a scenario where such information is unknown due to the unattended harsh environment, a distributed approach is a better solution to restore the partitioned network. In this paper, we have proposed and implemented a semi-distributed approach called Relay node Placement using Fermat Point. The proposed approach is capable of restoring lost connectivity with small number of recovery relay nodes and it works for any number of disjoint segments. The simulation experiment results show effectiveness of our approach as compared to existing benchmark approaches.