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Particularly in the last decade, Internet usage has been growing rapidly. However, as the Internet becomes a part of the day to day activities, cybercrime is also on the rise. Cybercrime will cost nearly $6 trillion per annum by 2021 as per the cybersecurity ventures report in 2020. For illegal activities, cybercriminals utilize any network computing devices as a primary means of communication with a victims’ devices, so attackers get profit in terms of finance, publicity and others by exploiting the vulnerabilities over the system. Cybercrimes are steadily increasing daily. Evaluating cybercrime attacks and providing protective measures by manual methods using existing technical approaches and also investigations has often failed to control cybercrime attacks. Existing literature in the area of cybercrime offenses suffers from a lack of a computation methods to predict cybercrime, especially on unstructured data. Therefore, this study proposes a flexible computational tool using machine learning techniques to analyze cybercrimes rate at a state wise in a country that helps to classify cybercrimes. Security analytics with the association of data analytic approaches help us for analyzing and classifying offenses from India-based integrated data that may be either structured or unstructured. The main strength of this work is testing analysis reports, which classify the offenses accurately with 99 percent accuracy.

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Ship detection plays a crucial role in marine security in remote sensing imagery. This paper discusses about a deep learning approach to detect the ships from satellite imagery. The model developed in this work achieves integrity by the inclusion of hashing. This model employs a supervised image classification technique to classify images, followed by object detection using You Only Look Once version 3 (YOLOv3) to extract features from deep CNN. Semantic segmentation and image segmentation is done to identify object category of each pixel using class labels. Then, the concept of hashing using SHA-256 is applied in conjunction with the ship count and location of bounding box in satellite image. The proposed model is tested on a Kaggle Ships dataset, which consists of 231,722 images. A total of 70% of this data is used for training, and the 30% is used for testing. To add security to images with detected ships, the model is enhanced by hashing using SHA-256 algorithm. Using SHA-256, which is a one-way hash, the data are split up into blocks of 64 bytes. The input data to the hash function are both the ship count and bounding box location. The proposed model achieves integrity by using SHA-256. This model allows secure transmission of highly confidential images that are tamper-proof.

Mobile ad hoc network (MANET) is a miscellany of versatile nodes that communicate without any fixed physical framework. MANETs gained popularity due to various notable features like dynamic topology, rapid setup, multihop data transmission, and so on. These prominent features make MANETs suitable for many real-time applications like environmental monitoring, disaster management, and covert and combat operations. Moreover, MANETs can also be integrated with emerging technologies like cloud computing, IoT, and machine learning algorithms to achieve the vision of Industry 4.0. All MANET-based sensitive real-time applications require secure and reliable data transmission that must meet the required QoS. In MANET, achieving secure and energy-efficient data transmission is a challenging task. To accomplish such challenging objectives, it is necessary to design a secure routing protocol that enhances the MANET’s QoS. In this paper, we proposed a trust-based multipath routing protocol called TBSMR to enhance the MANET’s overall performance. The main strength of the proposed protocol is that it considers multiple factors like congestion control, packet loss reduction, malicious node detection, and secure data transmission to intensify the MANET’s QoS. The performance of the proposed protocol is analyzed through the simulation in NS2. Our simulation results justify that the proposed routing protocol exhibits superior performance than the existing approaches.