With the advancement in cyber area, frequent use of internet and technologies leads to cyber attacks. Digital forensic is opted for acquiring electronic information and investigation of malicious evidence found in system or on network in such a manner that makes it admissible in court. It is also used to recover lost information in a system. The recovered information is used to prosecute a criminal. Number of crimes committed against an internet and malware attacks over the digital devices have increased. Memory analysis has become a critical capability in digital forensics because it provides insight into the system state that should not be represented by traditional media analysis. In this paper, we study the details of cyber forensics and also provide the vital information regarding distinctive tools operate in digital forensic process. It includes forensic analysis of encrypted drives, disk analysis, analysis toolkit, volatile memory analysis, captures and analyzes packets on network.
Flood is a recurrent and crucial natural phenomenon affecting almost the entire planet. It is a critical problem that causes crop destruction, destruction to the population, loss of infrastructure, and demolition of several public utilities. An effective way to deal with this is to alert the community from incoming inundation and provide ample time to evacuate and protect property. In this article, we suggest an IoT-based energy efficient flood prediction and forecasting system. IoT sensor nodes are constrained in terms of battery and memory, so the fog layer uses an energy-saving approach based on data heterogeneity to preserve the system's power consumption. cloud storage is used for efficient storage. The environmental conditions such as temperature, humidity, rainfall, and water body parameters, i.e. water flow and water level, are being investigated for India's Kerala region to calibrate the flood phases. PCA (Principal Component Analysis) approach is used at the fog layer for attribute dimensionality reduction. To forecast the flood, the ANN (Artificial Neural Network) algorithm is used, and the simulation technique of Holt Winter is used to forecast the future flood. Data is obtained from the Indian government meteorological database and experimental assessment is carried out. The findings showed the feasibility of the proposed architecture.
Background
Latent fingerprints are the unintentional impressions that are left at crime scenes, which are considered to be highly significant in forensic analysis and authenticity verification. It is an extremely crucial tool used by law enforcement and forensic agencies for the conviction of criminals. However, due to the accidental nature of these impressions, the quality of prints uplifted is generally inferior.
Main body
In order to improve the overall fingerprint recognition performance, there is an insistent need to design novel methods to improve the reliability and robustness of the existing techniques. Therefore, a systematic review is presented to study the existing methods for latent fingerprint acquisition, enhancement, reconstruction, and matching, along with various benchmark datasets available for research purposes.
Conclusion
The paper highlights multiple challenges and research gaps using comparative analysis of existing enhancement, reconstruction and matching approaches in order to augment the research in this direction that has become imperative in this digital era.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.