Search

COVID-19 pandemic has shaken the world and has taught a lesson to maintain personal cleanliness and hygiene. In these tough times, only people with higher degree of cleanliness and hygiene could beat this deadly virus and survive the pandemic. Everyday millions of people are losing their lives due to COVID-19, which is majorly spreading due to lack of sanitization and unhygienic surroundings. Simple remedies to fight corona virus including washing hands with soaps, regular use of hand and air sanitizers, use of face mask etc. In public places such as railway stations, airports, restaurants, hospitals, cafes, shopping malls etc., the maintenance and monitoring of hygiene conditions of visiting people conditions becomes a big challenge. To help the government authorities and local administration in better surveillance and monitoring of Corona positive cases, or people contaminated with any other kind of germs and bacteria, we came up with the prototype design of ‘Germica-A germ detection machine’. It is savvy medical and clinic framework, which has multiple germ detection features installed like UV lights to detect the germ intensity on hands or any other targeted body parts. Another feature of ‘Germica’ is germ detection cameras; which can take the image of different body parts, example nails and based to its colour, it can tell if a person is sick or healthy. In preparing the prototype of ‘Germica’, scientist have vigorously tested and trained the medical data to see its actual application and degree of accuracy. Small portable device like ‘Germica’ can help in maintaining hygiene conditions in hospitals, clinics and other places. ‘Germica’ in long run can help to combat with COVID-19 pandemic and other disease that spread through human carrier.

Smart home is a latest technology in the field of Internet of Things (IoT) through which one can manage the smart devices at home using internet. This communication takes place via insecure channel which makes it vulnerable to many attacks. Hence, a secure authentication protocol is required to transfer sensitive data within smart home. Recently Shuai et al. have designed two factor user authentication protocol for smart home environment. In this paper, it has been discussed that this protocol is vulnerable to offline password guessing attack, insider attack, replay attack, gateway bypass attack and insecure session key agreement problem. To overcome these attacks, a user authentication protocol has been proposed. Its formal security analysis and verification is given using random oracle model and ProVerif tool, respectively. In performance analysis, the proposed scheme is compared with other related scheme to show that it is more reliable and secure than them.

The major growth in computing technology has done wonders for the medical field. Being a significantly favourable research domain, many algorithms have been proposed in the near past to detect the presence of malaria parasites. The target of this work was to originate a technique which detects the parasites of malaria easily and classifies in just a few seconds. A hybrid ant colony optimization and support vector machine approach is developed for this task. In this work, ant colony optimization was used for the detection of the boundaries of microscopic images of malaria parasites and by using multi-class support vector machine, classification of malaria parasites is achieved. In SVM various parameters were taken (HSV colour values, auto correlogram, colour moments, mean amplitude, area and wavelet moments), which are used to build different classes or groups. The performance of this approach is verified by simulation results.

OCDMA is basically a coding method used to spread optical pulses. It can support simultaneous users to access optical channels. 16 code keying encryption enhances received power, confidentiality and security of the OCDMA system. On increasing coding from 2 keying to 16 keying, power improves and trails will enhance which is possible due to the XOR gate and NRZ generator with CMUX. The successive improvement using 16 coding technique is because of 2m codewords. Because of the higher latency of XOR gate, MAI is also reduced. The confidentiality and the power improvement is the best in case of 16 code keying encryption.

Ant colony optimization (ACO) is algorithm used for optimization motivated by the natural behaviour of species of ants. In this ants retain pheromone for foraging at the ground. ACO has been originated to detect the edges of microscopic images of blood samples which are affected by malaria disease. The edge detection approach of ACO is used to maintain pheromone matrix which determines the information of edges provided at every image pixel position, based on no. of ants movement that are dispatched to be in motion on the image. Thus, changes in the intensity values of images determine the movement of the ants. The results have been taken to study an approach for Ant Colony Edge Detection method.

Due to dynamic topology and non-wired infrastructure of Wireless Sensor Networks (WSN), they are prone to attacks. Wormhole attack is one of the most popular and serious attack in WSNs. In Wormhole attack two or more malicious nodes makes a covert channel which attracts the traffic towards itself by depicting a low latency link and then start dropping and replaying packets in the multi-path route. This paper proposes promiscuous mode method to detect and isolate the malicious node during wormhole attack by using Ad-hoc on demand distance vector routing protocol (AODV) with omnidirectional antenna. This paper proposes that the nodes which are not participating in multi-path routing generates an alarm message during delay and then detects and isolate the malicious node from network.

With the advancement in technology, industry, e-commerce and research a large amount of complex and pervasive digital data is being generated which is increasing at an exponential rate and often termed as big data. Traditional Data Storage systems are not able to handle Big Data and also analyzing the Big Data becomes a challenge and thus it cannot be handled by traditional analytic tools. Cloud Computing can resolve the problem of handling, storage and analyzing the Big Data as it distributes the big data within the cloudlets. No doubt, Cloud Computing is the best answer available to the problem of Big Data storage and its analyses but having said that, there is always a potential risk to the security of Big Data storage in Cloud Computing, which needs to be addressed. Data Privacy is one of the major issues while storing the Big Data in a Cloud environment. Data Mining based attacks, a major threat to the data, allows an adversary or an unauthorized user to infer valuable and sensitive information by analyzing the results generated from computation performed on the raw data. This thesis proposes a secure k-means data mining approach assuming the data to be distributed among different hosts preserving the privacy of the data. The approach is able to maintain the correctness and validity of the existing k-means to generate the final results even in the distributed environment.