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Cloud forensics is an extension of contemporary forensic science that guards against cybercriminals. However, consolidated data assortment and storage compromise the legitimacy of digital indication. This essay proposes an evolving modern algorithm automated forensic platform based on the blockchain idea. This proposes forensic structure design, evidence gathering, and storage on a blockchain that are peer to peer. Secure Block Verification Mechanism (SBVM) will protect unauthorised users. Secret keys are optimally produced using the cuckoo search optimization method. All data are saved and encrypted at the cloud authentication server for secrecy. Confidentiality-Based Algebraically Homomorphism, a new encryption method, is given to cryptosystem learning. Every data is assigned a block in the SDN controller, and the history is kept as metadata about data. Each block has a Secure Hash Algorithm version 3 of 512-bit hash-based tree. Our approach uses graph theory-based graph neural networks in Smart Contracts to track users’ data (GNNSC). Finally, a blockchain-based evidence graph allows for evidence analysis. The experiments were run in a cloud environment with Python and network simulator-3.30 (for software-defined network). We achieved good results in terms of evidence response time, cloud evidence insertion time, cloud evidence verification time, computational overhead, hash calculation time, key generation times, and entire overall change rate of indication using our newly deliberated forensic construction using blockchain (FAuB).

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Forensic in cloud computing is an advancement of evolutionary modern forensic science that protects against cyber criminals. Single centralize point compilation and storage of data, however, overcome the authenticity of digital evidence. In order to address this serious issue, this article suggests a evolutionary modern algorithm automated forensic platform leveraging infrastructure as a cloud service (IaaS) based on Blockchain concept. This proposed forensic structural design, evidence collection of evidence and stored on a blockchain which is circulated around several peer blocks. Secure Block Verification Mechanism (SBVM) is proposed to Safeguarding the device from unauthorised users. Using the cuckoo search optimization algorithm for strengthening of the cloud environment, secret keys are optimally generated. On the bases of level of confidentiality, all data is stored and encrypted at cloud authentication server. Confidentiality-based Algebraically Homomorphic Cryptosystems learning is presented with a fast-forwarding algorithm for encryption. A block in the SDN controller is created for every data and information is stored in the cloud service provider and the history is recorded as metadata data about data. A hash based tree is constructed in each block by Secure Hash Algorithm version − 3 of 512 bits. By implementing graph theory-based graph neural networks in Smart Contracts, our framework enables users to track their data (GNNSC). Finally, the construction of a evidence graph using blockchain data enables evidence analysis. Experiments was carried out in a Python programming and blockchain integrated cloud environment with network simulator-3.30 (for Software Defined Network). As part of result our newly designed forensic architecture using blochchain (FAuB) good results in terms of evidence response time, insertion times of cloud evidence, verification time of evidence, computational overhead of evidence, hashes calculation time, keys generations times of evidence, evidence encryption time, evidence decryptions time, and total overall change rate of evidence, according to a comprehensive comparative study.

In this paper we use the Kohonen neural network based Self Organizing Map (SOM) algorithm for Urdu Character Recognition. Kohenen NN have more efficient in terms of performance as compare to other approaches. Classification is used to recognize hand written Urdu character. The number of possible unknown character is reducing by pre-classification with respect to subset of the total character set. So the proposed algorithm is attempt to group similar character .Members of pre-classified group are further analyzed using a statistical classifier for final recognition. A recognition rate of around 79.9% was achieved for the first choice and more than 98.5% for the top three choices. The result of this paper shows that the proposed Kohonen SOM algorithm yields promising output and feasible with other existing techniques.