Gujarat Forensic Sciences University

The Tails operating system, renowned for its emphasis on privacy and anonymity, has become a preferred choice for individuals seeking to safeguard their online activities. Tails OS design centres around providing a secure environment that leaves minimal traces, thereby attracting privacy-conscious users. However, this very secure design also entices cyber criminals operating in the digital landscape, to use Tails to perpetrate illicit activities, creating new challenges for digital forensics practitioners in their pursuit of extracting evidence. This paper conducts an exhaustive forensics of Tails operating system, aiming to uncover the digital remnants left behind during its utilisation. By analysing the RAM, network, disk, and virtualisation, employing a range of activities and forensic tools, many artefacts are unearthed that provide insight into user interactions within the Tails environment. This research contributes to our understanding of the interplay between privacy preservation and digital evidence recovery, shedding light on the complexities of investigating a privacy-focused operating system like Tails.

Knowledge representation and reasoning is a field of ‘Artificial Intelligence’ that encodes knowledge, beliefs, actions, feelings, goals, desires, preferences, and all other mental states in the machine. An ontology is prominently used to represent knowledge and offers the richest machine-interpretable (rather than just machine-processable) and explicit semantics. Ontology does not only provide sharable and reusable knowledge, but it also provides a common understanding of the knowledge; as a result, the interoperability and interconnectedness of the model make it priceless for addressing the issues of querying data. Ontology work with concepts and relations that are very close to the working of the human brain. Ontological engineering provides the methods and methodologies for the development of ontology. Nowadays, ontologies are used in almost every field, and a lot of much research is being done on this topic. The paper aims to elaborate on the need of ontology (from data to knowledge), how does for ontology (from data to knowledge), how semantics come from logic, the ontological engineering field, history from hypertext to linked data, and further possible research directions of the ontology. This paper benefit reader who wishes to embark on ontology-based research and application development.

Ocimum species is having massive variations in their chemical composition that includes day time, terrestrial and cyclical variation according to the season etc. In this study, variation in overall essential oil constituents among matured, intermediate and young leaf samples of O. sanctum was studied. The leaf samples according to their age- matured, intermediate and young leaves were collected from three different plants at 11 a.m. each. Extract of collected leaf samples were then scrutinized using gas chromatographic technique having flame ionization detector (GC-FID). Result reveals that the noteworthy variance was observed in overall essential oil constituents of the leaf samples according to their age. The Average chromatographic fingerprint data of triplicate run for different leaf samples were then treated to multivariate methods such as hierarchical cluster analysis and principal component analysis. In this study, instead of considering one or two biomarkers, we have exasperated to obtain as many constituents in chloroform extracts of chosen leaf samples. Scree plot reveals that nine components were responsible for the whole variation. Score plot and dendogram efficiently discriminated all the three leaf types according to their age in different clusters. The outcome as an inference was, O. sanctum leaves according to their age exhibit variation in its chemical composition and developed tool can be used for routine quality control.

Crop disease in the plant is a significant issue in the agriculture sector, and it is currently very difficult to detect these illnesses in crop leaves. The foundation of the global economy is agriculture. India ranks second in the production of tomatoes worldwide. The tomato crop is affected by various diseases which lead to a reduction in product quality and quantity. The advancement in computer vision and deep learning opens up the door for predicting diseases that appear in the crops. The aim of this paper is classification among 10 different categories of tomato plant leaves using the proposed novel TomConv model which deploys an improved Convolutional Neural Network (CNN). For this purpose, the publicly available dataset called PlantVillage comprising of more than 16000 images of tomato leaves, both diseased and healthy was used for the experimentation purpose. The proposed model is the simplest model among all the available state-of-the-art models. The tomato leaf images were preprocessed for reducing the size in 150 × 150 dimension. The model constitutes four layered CNN followed by a max pooling layer. The model splits the corpus into training and validation datasets in 80:20 ratio, is trained under 105 epochs for tomato leaf images, and achieved an accuracy of 98.19%. The proposed model is compared with existing models under different parameters such as no. of classes, no. of layers, and accuracy. The results are promising as they outperform all the available state-of-the-art models.

In recent cyber attacks, credential theft has emerged as one of the primary vectors of gaining entry into the system. Once attacker(s) have a foothold in the system, they use various techniques including token manipulation to elevate the privileges and access protected resources. This makes authentication and token based authorization a critical component for a secure and resilient cyber system. In this paper we discuss the design considerations for such a secure and resilient authentication and authorization framework capable of self-adapting based on the risk scores and trust profiles. We compare this design with the existing standards such as OAuth 2.0, OpenID Connect and SAML 2.0. We then study popular threat models such as STRIDE and PASTA and summarize the resilience of the proposed architecture against common and relevant threat vectors. We call this framework as Resilient Risk based Adaptive Authentication and Authorization (RAD-AA). The proposed framework excessively increases the cost for an adversary to launch and sustain any cyber attack and provides much-needed strength to critical infrastructure. We also discuss the machine learning (ML) approach for the adaptive engine to accurately classify transactions and arrive at risk scores.