ADVANCED RANDOM TIME QUEUE BLOCKING WITH TRAFFIC PREDICTION FOR DEFENSE OF LOW-RATE DOS ATTACKS AGAINST APPLICATION SERVERS

Among many strategies of Denial of Services, low-rate traffic denial-of-service (DoS) attacks are more significant. This strategy denies the services of a network by detection of the vulnerabilities in performance of the application. In this research, an efficient defence methodology is developed against low-rate DoS attack in the application servers. Though, the Improved Random Time Queue Blocking (IRTQB) technique can eliminate the vulnerabilities in the network and also avoiding the attacker from capturing all the server queue positions by defining a spatial similarity metric (SSM). However, the differentiation of the attack requests from the legitimate users’ is not always efficient since only the source IP addresses and the record timestamp are considered in the SSM. It was improved by using Advanced Random Time Queue Blocking (ARTQB) scheme that employed Bandwidth utilization of attacker in IRTQB to detect the DoS attack that normally consumes a huge number of resources of the server. However, this method becomes ineffective when the attack consumes more network traffic. In this paper, an efficient detection technique called Advanced Random Time Queue Blocking with Traffic Prediction (ARTQB-TP) is proposed for defining SSM which contains, Source IP, timestamp, Bandwidth between two requests and the difference between the attack traffic and legitimate traffic. The ARTQB-TP technique is utilized to reduce the attack efficiency in 18 different server configurations which are more vulnerable to the DoS attacks and where the attacks may also have a chance to improve its effectiveness. Experimental results show that the proposed system performs better protection of application servers against the LRDoS attacks by solving its impacts on any kind of server architectures and reduced the attack efficiencies of all the types of attack strategies

Feasibility of bilateral salpingo-oophorectomy during vaginal hystertectomy for benign uterine diseases

Background: Prophylactic bilateral salpingo-oophorectomy (BSO) remains the best measure in the prevention of ovarian cancer as it lacks an effective screening tool. The need to perform prophylactic BSO shouldn't dictate the route of surgery. To assess the feasibility of prophylactic BSO during vaginal hysterectomy. To analyze the safety of the vaginal BSO.Methods: This was a retrospective study conducted in the Department of Obstetrics and Gynaecology at Velammal Medical College, Madurai, Tamilnadu, India from June 2016 to June 2018 over a period of 3 years. 54 women who underwent hysterectomy for benign uterine disease in whom concomitant prophylactic BSO was attempted were included in the study. Preoperative data like age, parity, menopausal status, BMI, previous pelvic surgeries were noted from the admission record. Intraoperative details like indication for surgery, surgical procedure, duration of surgery and complications like hemorrhage, bladder, ureter and bowel injury were collected from the operative record. Postoperative recovery details were also noted down from the case sheet. The collected data were then analyzed.Results: Of the 54 women included in the study, transvaginal BSO was successful in 53 (98.1%) women. There was one case of primary haemorrhage due to slippage of ovarian pedicle, another patient required laparotomy for completing BSO. None had bladder, ureter or bowel injury.Conclusions: Prophylactic BSO is both feasible and safe in almost all patients undergoing vaginal hysterectomy. Developing the skill to perform transvaginal BSO can inspire gynaecologists to move a step forward and deal with benign adnexal pathology concomitantly at vaginal hysterectomy. The risk of remnant ovarian syndrome post vaginal oophorectomy is unknown

User Selection and Pairing for Future Power Domain Non-Orthogonal Multiple Access (PD-NOMA) using Deep Learning Techniques

The next-generation wireless networks and communications such as 5G/6G offers various benefits such as low latency, high data rates, and improvement in user numbers with increased base station capacity and quality of service. These advantages are obtained from the increasing receiver complexity through the non-orthogonal multiple access (NOMA) of users. It is the promising radio access approach used to enhance next-generation wireless communications. Among the techniques of NOMA such as power and code domain, this paper concentrates on power domain NOMA. The user in the network for transmission is selected using a deep learning approach called deep neural network (DNN).  This user selection results are the training data and the loss function is modified for the selection of users that could meet the constraint the selected user cannot be in both strong and weak groups. The user aggregation/user pairing among the sub-channels is performed through the exhaustive analysis using particle swarm optimization (PSO). The usage of DNN-PSO enables the transmitter and required minimum uplink and downlink transmitting power and guaranteed for Quality of Service of each user. The simulation and comprehensive statistical evaluation are performed with the comparative analysis of energy efficiency and maximum achievable rate with the given spectrum efficiency (SE) of PD-NOMA. The proposed model ensures reduced latency, increased throughput, less energy, achievable data rate, user fairness and increased reliability and quality of service