Deduplication is maturing as a standard attribute on backup and archives, whereby the aim is to free the storage space by removing the duplicates. Considering the storage room demand and justifiable deletion, this paper proposes a multi-container intelligent deduplication image archive system; where analogous images are disposed from the system based on the similarity approach. Similarity-aware image deduplication is achieved by calculating image fingerprints and the images are deleted when their hamming distance matches the predefined threshold. A probability model is addressed for the overall probability of getting similar images on the respective containers based on their relative storage and similarity scores of the images. In addition, the linear optimisation model is formulated to target data minimisation and storage space maximisation; which is further verified with the dataset. We perform experimentation of our work on the existing as well as synthesised datasets and various accuracy metrics are calculated in terms of precision, recall, f-score and deduplication ratio. It is observed that binary hashes used in our system give the fair contribution in removing similar images.
Even though the organisation uses various security measures the attacks over the internet are increasing day by day. This paper proposes a hybrid feature selection model for the detection of a DDoS attack. In this paper, the two-step hybrid feature selection method is used. The CICIDS2017 dataset with 84 features is used for the implementation. Information gain, gain ratio, and correlation filter-based algorithms are used for the ranking of features and then the forward selection approach is used to reduce the features up to 32. The system gives higher accuracy of 88.7373% for the correctly classifying DDoS attack.
Communication among the unmanned aerial vehicles (UAVs) in flying ad hoc network (FANET) is a vital design aspect. This is ascribed to the highly dynamic nature of the UAVs, along with the constraints in the battery resources encountered. Devising a technique that can improve the efficiency in routing along with a stable topology in FANETs is essential. In order to do this, the paper attempts to provide a comparative analysis of two different clustering methodologies for improving the lifetime of operation of FANETs. The paper implements a clustering methodology, which employs a hyper heuristic method for selecting optimal clusters and cluster heads (CHs) using glowworm swarm optimisation (GSO) and firefly algorithm (FA). Secondly, a hybrid algorithm based on particle swarm optimisation (PSO) and firefly algorithm (FA) is applied. Connectivity, distance, energy, and neighbourhood degree are the key factors considered for the optimal selection purpose. Extensive simulations were carried out over different network areas and node densities to evaluate and compare the performances of the methods. The evaluation was based on the cluster building time (CBT), energy consumption by the network, alive node analysis and overall improvement in the network lifetime. Results largely validated the better performance of the hybrid PSOFA-based clustering scheme.
There has been tremendous growth of laparoscopic surgical video archives in the past few years. Finding the surgical videos manually from the database is very troublesome and time consuming. As a consequence, efficient methods for retrieval of such videos from a database are needed. In this paper, a content-based video retrieval (CBVR) system is presented using average intensity, colour and shape feature. In the proposed retrieval system, average intensity, colour and shape feature are used. The ordinal measure introduced by Bhat and Nayar, which is based on ranks rather than of their values is used in this work. This is important in the laparoscopic videos because adjacent frames can be distinguished on the basis of ranking of their average intensities. Experimental results reveal that mean average precision of the proposed technique is better than the existing motion intensity and direction descriptor (MIDD) approach. This reflects the effectiveness of proposed method.
The internet of things (IoT) and the fog computing paradigm are the next wave in the era of computing and have been emerging technologies in the fields of computer science and information technology. Prior research concentrates on the integration of IoT and fog computing but is limited because it cannot be extended to all applications due to inadequate security architecture. In this paper, we propose a biocryptography approach using biometric key generation for elliptic curve cryptography (ECC) based on the authentication of fog computing. In biometric-based ECC, a private key is generated via the fingerprint of the user ensures complete protection against security risks such as confidentiality, authentication, integrity, and privacy. The security strengths of various public key cryptosystems are analysed, and ECC is one of the best cryptosystems for fog-based IoT middleware.
