Biblio

In the information era, knowledge is becoming increasingly significant for all industries, especially criminal investigation that deeply relies on intelligence and strategies. Therefore, there is an urgent need for effective management and utilization of criminal investigation knowledge. As an important branch of knowledge engineering, the expert system can simulate the thinking pattern of an expert, proposing strategies and solutions based on the knowledge stored in the knowledge base. A crucial step in building the expert system is to construct the knowledge base, which determines the function and capability of the expert system. This paper establishes a practical knowledge base for criminal investigation, combining the technologies of cloud computing with traditional method of manual entry to acquire and process knowledge. The knowledge base covers data information and expert knowledge with detailed classification of rules and cases, providing answers through comparison and reasoning. The knowledge becomes more accurate and reliable after repeated inspection and verification by human experts.

Currently, many organizations are moving to new digital management systems, which is accompanied not only by the introduction of new approaches based on the use of information technology, but also by a change in the organizational and management environment. Risk management is a process necessary to maintain the competitive advantage of an organization, but it can also become involved in the course of digitalization itself, which means that risk management also needs to change to meet modern conditions and ensure the effectiveness of the organization. This article discusses the risk management process in the digital environment. The main approach to the organization of this process is outlined, taking into account the use of information tools, together with the stages of this process, which directly affect the efficiency of the company. The risks that are specific to a digital organization are taken into account. Modern requirements for risk management for organizations are studied, ways of their implementation are outlined. The result is a risk management process that functions in a digital organization.

Techniques applied in response to detrimental digital incidents vary in many respects according to their attributes. Models of techniques exist in current research but are typically restricted to some subset with regards to the discipline of the incident. An enormous collection of techniques is actually available for use. There is no single model representing all these techniques. There is no current categorisation of digital forensics reactive techniques that classify techniques according to the attribute of function and nor is there an attempt to classify techniques in a means that goes beyond a subset. In this paper, an ontology that depicts digital forensic reactive techniques classified by function is presented. The ontology itself contains additional information for each technique useful for merging into a cognate system where the relationship between techniques and other facets of the digital investigative process can be defined. A number of existing techniques were collected and described according to their function - a verb. The function then guided the placement and classification of the techniques in the ontology according to the ontology development process. The ontology contributes to a knowledge base for digital forensics - essentially useful as a resource for the various people operating in the field of digital forensics. The benefit of this that the information can be queried, assumptions can be made explicit, and there is a one-stop-shop for digital forensics reactive techniques with their place in the investigation detailed.

Advanced Persistent Threat (APT) is one of the cyber threats that continuously attack specific targets exfiltrate information or destroy the system [1]. Because the attackers use various tools and methods according to the target, it is difficult to describe APT attack in a single pattern. Therefore, APT attacks are difficult to defend against with general countermeasures. In these days, systems consist of various components and related stakeholders, which makes it difficult to consider all the security concerns. In this paper, we propose an ontology knowledge base and its design process to recommend security requirements based on APT attack cases and system domain knowledge. The proposed knowledge base is divided into three parts; APT ontology, general security knowledge ontology, and domain-specific knowledge ontology. Each ontology can help to understand the security concerns in their knowledge. While integrating three ontologies into the problem domain ontology, the appropriate security requirements can be derived with the security requirements recommendation process. The proposed knowledge base and process can help to derive the security requirements while considering both real attacks and systems.

Surveillance is the monitoring of the behavior, activities or other changing information whereas security means the state of being protected from harmful activities. Nowadays proper surveillance security is considered as a challenging issue in the world and security has become a major concern from real life to virtual life. Tech-giants are implementing new solutions & techniques for better security assessment. This paper illustrates the design and implementation of a Belief Rule Based Expert System (BRBES) to overcome the uncertainty problems during bank security assessment. The proposed expert system has been developed based on generic Belief Rule Based (BRB) inference methodology using Evidential Reasoning algorithm (RIMER). Real-time security data has been taken from several banks of Bangladesh in conjunction with the expert's opinion to construct the knowledge base. This expert system provides more reliable and effective result under uncertainties which is better than any other traditional expert's prediction. Real life case studies were used for the validation of this system. Also, the outcome is compared with the real-life security system. Furthermore, the architectural design, implementation and utilization of an expert system to assess bank security under uncertainty are also discussed in this paper.

The information security of urban rail transit system faces great challenges. As a subsystem of the subway, BAS is short for Building Automation System, which is used to monitor and manage subway equipment and environment, also facing the same problem. Based on the characteristics of BAS, this paper designed a targeted intrusion detection expert system. This paper focuses on the design of knowledge base and the inference engine of intrusion detection system based on expert system. This study laid the foundation for the research on information security of the entire rail transit system.

We investigate a method of building a closed domain intelligent conversational agent using deep neural networks. A conversational agent is a dialog system intended to converse with a human, with a coherent structure. Our conversational agent uses a retrieval based model that identifies the intent of the input user query and maps it to a knowledge base to return appropriate results. Human conversations are based on context, but existing conversational agents are context insensitive. To overcome this limitation, our system uses a simple stack based context identification and storage system. The conversational agent generates responses according to the current context of conversation. allowing more human-like conversations.

Power systems domain has generally been very conservative in terms of conducting digital forensic investigations, especially so since the advent of smart grids. This lack of research due to a multitude of challenges has resulted in absence of knowledge base and resources to facilitate such an investigation. Digitalization in the form of smart grids is upon us but in case of cyber-attacks, attribution to such attacks is challenging and difficult if not impossible. In this research, we have identified digital forensic artifacts resulting from a cyber-attack on Wide Area Monitoring, Protection and Control (WAMPAC) systems, which will help an investigator attribute an attack using the identified evidences. The research also shows the usage of sandboxing for digital forensics along with hardware-in-the-loop (HIL) setup. This is first of its kind effort to identify and acquire all the digital forensic evidences for WAMPAC systems which will ultimately help in building a body of knowledge and taxonomy for power system forensics.

This article describes a framework for semantic annotation of texts that are submitted for forensic analysis, based on Frame Semantics, and a knowledge base of Forensic Frames - FrameFOR. We demonstrate through experimental evaluations that the application of the Semantic Role Labeling (SRL) techniques and Natural Language Processing (NLP) in digital forensic increases the performance of the forensic experts in terms of agility, precision and recall.

This electronic document is a “live” template and already defines the components of your paper [title, text, heads, etc.] in its style sheet. The paper considers the possibility and necessity of using in modern control and training systems with a natural language interface methods and mechanisms, characteristic for knowledge processing systems. This symbiosis assumes the introduction of specialized inference machines into the testing systems. For the effective operation of such an intelligent interpreter, it is necessary to “translate” the user's answers into one of the known forms of the knowledge representation, for example, into the expressions (rules) of the first-order predicate calculus. A lexical processor, performing morphological, syntactic and semantic analysis, solves this task. To simplify further work with the rules, the Skolem-transformation is used, which allows to get rid of quantifiers and to present semantic structures in the form of sequents (clauses, disjuncts). The basic principles of operation of the inference machine are described, which is the main component of the developed intellectual subsystem. To improve the performance of the machine, one of the fastest methods was chosen - a parallel method of deductive inference based on the division of clauses. The parallelism inherent in the method, and the use of the dataflow architecture, allow parallel computations in the output machine to be implemented without additional effort on the part of the programmer. All this makes it possible to reduce the time for comparing the sequences stored in the knowledge base by several times as compared to traditional inference mechanisms that implement various versions of the principle of resolutions. Formulas and features of the technique of numerical estimation of the user's answers are given. In general, the development of the human-computer dialogue capabilities in test systems- through the development of a specialized module for processing knowledge, will increase the intelligence of such systems and allow us to directly consider the semantics of sentences, more accurately determine the relevance of the user's response to standard knowledge and, ultimately, get rid of the skeptical attitude of many managers to machine testing systems.

The Tor Network, a hidden part of the Internet, is becoming an ideal hosting ground for illegal activities and services, including large drug markets, financial frauds, espionage, child sexual abuse. Researchers and law enforcement rely on manual investigations, which are both time-consuming and ultimately inefficient. The first part of this paper explores illicit and criminal content identified by prominent researchers in the dark web. We previously developed a web crawler that automatically searched websites on the internet based on pre-defined keywords and followed the hyperlinks in order to create a map of the network. This crawler has demonstrated previous success in locating and extracting data on child exploitation images, videos, keywords and linkages on the public internet. However, as Tor functions differently at the TCP level, and uses socket connections, further technical challenges are faced when crawling Tor. Some of the other inherent challenges for advanced Tor crawling include scalability, content selection tradeoffs, and social obligation. We discuss these challenges and the measures taken to meet them. Our modified web crawler for Tor, termed the “Dark Crawler” has been able to access Tor while simultaneously accessing the public internet. We present initial findings regarding what extremist and terrorist contents are present in Tor and how this content is connected to each other in a mapped network that facilitates dark web crimes. Our results so far indicate the most popular websites in the dark web are acting as catalysts for dark web expansion by providing necessary knowledgebase, support and services to build Tor hidden services and onion websites.

Wikidata is the new, large-scale knowledge base of the Wikimedia Foundation. Its knowledge is increasingly used within Wikipedia itself and various other kinds of information systems, imposing high demands on its integrity. Wikidata can be edited by anyone and, unfortunately, it frequently gets vandalized, exposing all information systems using it to the risk of spreading vandalized and falsified information. In this paper, we present a new machine learning-based approach to detect vandalism in Wikidata. We propose a set of 47 features that exploit both content and context information, and we report on 4 classifiers of increasing effectiveness tailored to this learning task. Our approach is evaluated on the recently published Wikidata Vandalism Corpus WDVC-2015 and it achieves an area under curve value of the receiver operating characteristic, ROC-AUC, of 0.991. It significantly outperforms the state of the art represented by the rule-based Wikidata Abuse Filter (0.865 ROC-AUC) and a prototypical vandalism detector recently introduced by Wikimedia within the Objective Revision Evaluation Service (0.859 ROC-AUC).