Multi-class and Multi-label classication of Darkweb Data

abstract: In this research, I try to solve multi-class multi-label classication problem, where the goal is to automatically assign one or more labels(tags) to discussion topics seen in deepweb. I observed natural hierarchy in our dataset, and I used dierent techniques to ensure hierarchical integrity constraint on the predicted tag list. To solve `class imbalance' and `scarcity of labeled data' problems, I developed semisupervised model based on elastic search(ES) document relevance score. I evaluate our models using standard K-fold cross-validation method. Ensuring hierarchical integrity constraints improved F1 score by 11.9% over standard supervised learning, while our ES based semi-supervised learning model out-performed other models in terms of precision(78.4%) score while maintaining comparable recall(21%) score.Dissertation/ThesisMasters Thesis Computer Science 201

Complex Protection System of Metadata-based Distributed Information Systems

A description of architecture and approaches to the implementation of a protection system of metadatabased adaptable information systems is suggested. Various protection means are examined. The system described is a multilevel complex based on a multiagent system combining IDS functional abilities with structure and logics protection means

Detecting Hacker Threats: Performance of Word and Sentence Embedding Models in Identifying Hacker Communications

Abstract—Cyber security is striving to find new forms of protection against hacker attacks. An emerging approach nowadays is the investigation of security-related messages exchanged on deep/dark web and even surface web channels. This approach can be supported by the use of supervised machine learning models and text mining techniques. In our work, we compare a variety of machine learning algorithms, text representations and dimension reduction approaches for the detection accuracies of software-vulnerability-related communications. Given the imbalanced nature of the three public datasets used, we investigate appropriate sampling approaches to boost detection accuracies of our models. In addition, we examine how feature reduction techniques such as Document Frequency Reduction, Chi-square and Singular Value Decomposition (SVD) can be used to reduce the number of features of the model without impacting the detection performance. We conclude that: (1) a Support Vector Machine (SVM) algorithm used with traditional Bag of Words achieved highest accuracies (2) The increase of the minority class with Random Oversampling technique improves the detection performance of the model by 5% on average, and (3) The number of features of the model can be reduced by up to 10% without affecting the detection performance. Also, we have provided the labelled dataset used in this work for further research. These findings can be used to support Cyber Security Threat Intelligence (CTI) with respect to the use of text mining techniques for detecting security-related communication

The Metaverse and Web 3.0: Revolutionising Consumption and Communication for the Future

The metaverse is a new frontier in consumption. It is a digital place where people can buy and consume anything they want, whenever they want. It is an oasis of freedom and choice, and it has the potential to change the way we live and work. The future of the metaverse is placed where data and technology merge to create an experience that’s both unique and engaging. With information overload becoming a weekly reality, it is crucial for businesses to understand how their consumers are engaging with their offerings. This chapter synthesised the current research and practice to answer the following questions: How is the metaverse changing the way we consume and communicate? And how is Web 3.0 empowering and transforming the metaverse? Moreover, what are the threats Web 3.0 is bringing to our privacy on the internet

Assessing and augmenting SCADA cyber security: a survey of techniques

SCADA systems monitor and control critical infrastructures of national importance such as power generation and distribution, water supply, transportation networks, and manufacturing facilities. The pervasiveness, miniaturisations and declining costs of internet connectivity have transformed these systems from strictly isolated to highly interconnected networks. The connectivity provides immense benefits such as reliability, scalability and remote connectivity, but at the same time exposes an otherwise isolated and secure system, to global cyber security threats. This inevitable transformation to highly connected systems thus necessitates effective security safeguards to be in place as any compromise or downtime of SCADA systems can have severe economic, safety and security ramifications. One way to ensure vital asset protection is to adopt a viewpoint similar to an attacker to determine weaknesses and loopholes in defences. Such mind sets help to identify and fix potential breaches before their exploitation. This paper surveys tools and techniques to uncover SCADA system vulnerabilities. A comprehensive review of the selected approaches is provided along with their applicability