Adaptive Learning Based Whale Optimization and Convolutional Neural Network Algorithm for Distributed Denial of Service Attack Detection in Software Defined Network Environment

SDNs (Software Defined Networks) have emerged as a game-changing network concept. It can fulfill the ever-increasing needs of future networks and is increasingly being employed in data centres and operator networks. It does, however, confront certain fundamental security concerns, such as DDoS (Distributed Denial of Service) assaults. To address the aforementioned concerns, the ALWO+CNN method, which combines ALWOs (Adaptive Learning based Whale Optimizations) with CNNs (Convolution Neural Networks), is suggested in this paper. Initially, preprocessing is performed using the KMC (K-Means Clustering) algorithm, which is used to significantly reduce noise data. The preprocessed data is then used in the feature selection process, which is carried out by ALWOs. Its purpose is to pick out important and superfluous characteristics from the dataset. It enhances DDoS classification accuracy by using the best algorithms.  The selected characteristics are then used in the classification step, where CNNs are used to identify and categorize DDoS assaults efficiently. Finally, the ALWO+CNN algorithm is used to leverage the rate and asymmetry properties of the flows in order to detect suspicious flows specified by the detection trigger mechanism. The controller will next take the necessary steps to defend against DDoS assaults. The ALWO+CNN algorithm greatly improves detection accuracy and efficiency, as well as preventing DDoS assaults on SDNs. Based on the experimental results, it was determined that the suggested ALWO+CNN method outperforms current algorithms in terms of better accuracies, precisions, recalls, f-measures, and computational complexities

2-D Attention Based Convolutional Recurrent Neural Network for Speech Emotion Recognition

Recognizing speech emotions  is a formidable challenge due to the complexity of emotions. The function of Speech Emotion Recognition(SER) is significantly impacted by the effects of emotional signals retrieved from speech. The majority of emotional traits, on the other hand, are sensitive to emotionally neutral elements like the speaker, speaking manner, and gender. In this work, we postulate that computing deltas  for individual features maintain useful information which is mainly relevant to emotional traits while it minimizes the loss of emotionally irrelevant components, thus leading to fewer misclassifications. Additionally, Speech Emotion Recognition(SER) commonly experiences silent and emotionally unrelated frames. The proposed technique is quite good at picking up important feature representations for emotion relevant features. So here is a two  dimensional convolutional recurrent neural network that is attention-based to learn distinguishing characteristics and predict the emotions. The Mel-spectrogram is used for feature extraction. The suggested technique is conducted on IEMOCAP dataset and it has better performance, with 68% accuracy value

Preliminary phytochemical screening and antimicrobial activity of Samanea saman

Samanea saman is a tropically distributed medicinal plant. Antimicrobial activity of aqueous extract of this plant was investigated by Well-diffusion method against three organisms: Escherichia coli, Staphylococcus aureus and Candida albicans. The plant extract showed inhibitory activity against all the tested organisms. Five mg/ml inhibited the growth of E. coli but slightly higher concentration of 10 mg/mL was necessary to show inhibition against S. aureus and C. albicans. Phytochemical screening of the plant revealed the presence of tannins, flavonoides, saponins, steroids, cardiac glycosides and terpenoids. The study scientifically validates the use of plant in traditional medicine

Analysis of Antibody and Cytokine Markers for Leprosy Nerve Damage and Reactions in the INFIR Cohort in India

Leprosy is one of the oldest known diseases. In spite of the established fact that it is least infectious and a completely curable disease, the social stigma associated with it still lingers in many countries and remains a major obstacle to self reporting and early treatment. The nerve damage that occurs in leprosy is the most serious aspect of this disease as nerve damage leads to progressive impairment and disability. It is important to identify markers of nerve damage so that preventive measures can be taken. This prospective cohort study was designed to look at the potential association of some serological markers with reactions and nerve function impairment. Three hundred and three newly diagnosed patients from north India were recruited for this study. The study attempts to reflect a model of nerve damage initiated by mycobacterial antigens and maintained by ongoing inflammation through cytokines such as Tumour Necrosis Factor alpha and perhaps extended by antibodies against nerve components

© Impact Journals SECURE ROUTING PROTOCOL IN SENSOR NETWORK FOR VAMPIRE ATTACK

Ad hoc low-power wireless sensor networks are an exciting research direction in sensing and pervasive computing. Prior security work in this area has focused primarily on denial of communication at the routing or medium access control levels. This paper makes three primary contributions. First, we thoroughly evaluate the vulnerabilities of existing protocols to routing layer battery depletion attacks. We observe that security measures to prevent Vampire attacks are orthogonal to those used to protect routing infrastructure, and so existing secure routing protocols such as Ariadne, SAODV and SEAD do not protect against Vampire attacks. Existing work on secure routing attempts to ensure that adversaries cannot cause path discovery to return an invalid network path, but Vampires do not disrupt or alter discovered paths, instead using existing valid network paths and protocol-compliant messages. Protocols that maximize power efficiency are also inappropriate, since they rely on cooperative node behavior and cannot optimize out malicious action