AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments

This report considers the application of Articial Intelligence (AI) techniques to the problem of misuse detection and misuse localisation within telecommunications environments. A broad survey of techniques is provided, that covers inter alia rule based systems, model-based systems, case based reasoning, pattern matching, clustering and feature extraction, articial neural networks, genetic algorithms, arti cial immune systems, agent based systems, data mining and a variety of hybrid approaches. The report then considers the central issue of event correlation, that is at the heart of many misuse detection and localisation systems. The notion of being able to infer misuse by the correlation of individual temporally distributed events within a multiple data stream environment is explored, and a range of techniques, covering model based approaches, `programmed' AI and machine learning paradigms. It is found that, in general, correlation is best achieved via rule based approaches, but that these suffer from a number of drawbacks, such as the difculty of developing and maintaining an appropriate knowledge base, and the lack of ability to generalise from known misuses to new unseen misuses. Two distinct approaches are evident. One attempts to encode knowledge of known misuses, typically within rules, and use this to screen events. This approach cannot generally detect misuses for which it has not been programmed, i.e. it is prone to issuing false negatives. The other attempts to `learn' the features of event patterns that constitute normal behaviour, and, by observing patterns that do not match expected behaviour, detect when a misuse has occurred. This approach is prone to issuing false positives, i.e. inferring misuse from innocent patterns of behaviour that the system was not trained to recognise. Contemporary approaches are seen to favour hybridisation, often combining detection or localisation mechanisms for both abnormal and normal behaviour, the former to capture known cases of misuse, the latter to capture unknown cases. In some systems, these mechanisms even work together to update each other to increase detection rates and lower false positive rates. It is concluded that hybridisation offers the most promising future direction, but that a rule or state based component is likely to remain, being the most natural approach to the correlation of complex events. The challenge, then, is to mitigate the weaknesses of canonical programmed systems such that learning, generalisation and adaptation are more readily facilitated

A Fraud-Detection Fuzzy Logic Based System for the Sudanese Financial Sector

Financial fraud considered as a global issue that faces the financial sector and economy; as a result, many financial institutions loose hundreds of millions of dollars annually due to fraud. In Sudan, there are difficulties of getting real data from banks and the unavailability of systems which explain the reasons of suspicious transaction. Hence, there is a need for transparent techniques which can automatically detect fraud with high accuracy and identify its causes and common patterns. Some of the Artificial Intelligence (AI) techniques provide good predictive models, nevertheless they are considered as black-box models which are not easy to understand and analyze. In this paper, we developed a novel intelligent type-2 Fuzzy Logic Systems (FLSs) which can detect fraud in debit cards using real world dataset extracted from financial institutions in Sudan. FLSs provide white-box transparent models which employ linguistic labels and IF-Then rules which could be easily analyzed, interpreted and augmented by the fraud experts. The proposed type-2 FLS system learnt its fuzzy sets parameters from data using Fuzzy C-means (FCM) clustering as well as learning the FLS rules from data. The proposed system has the potential to result in highly accurate automatic fraud-detection for the Sudanese financial institutions and banking sectors

The Feasibility of Using Behavioural Profiling Technique for Mitigating Insider Threats: Review

Insider threat has become a serious issue to the many organizations. Various companies are increasingly deploying many information technologies to prevent unauthorized access to getting inside their system. Biometrics approaches have some techniques that contribute towards controlling the point of entry. However, these methods mainly are not able to continuously validate the users reliability. In contrast behavioral profiling is one of the biometrics technologies but it focusing on the activities of the users during using the system and comparing that with a previous history. This paper presents a comprehensive analysis, literature review and limitations on behavioral profiling approach and to what extent that can be used for mitigating insider misuse

Application of Data Mining in Telecommunication Industry

Data Mining is a logical procedure intended to investigate data (normally a lot of data - commonly business or market related - otherwise called "enormous data") looking for predictable examples as well as methodical connections amongst factors, and after that to approve the discoveries by applying the recognized examples to new subsets of data. The telecommunications industry inside the division of data and correspondence technology is comprised of all Telecommunications/telephone companies and web access suppliers and assumes the urgent part in the development of versatile interchanges and the data society. Customary telephone calls keep on being the industry's greatest income generator, yet because of advances in arrange technology, Telecom today is less about voice and progressively about content (informing, email) and pictures (e.g. video gushing). Fast web access for PC based data applications, for example, broadband data administrations and intelligent stimulation , is unavoidable. Digital Subscriber Line (DSL) is the primary broadband telecom technology. The quickest development originates from (esteem included) administrations conveyed over portable systems

Dropout Prediction: A Systematic Literature Review

Dropout predicting is challenging analysis process which requires appropriate approaches to address the dropout. Existing approaches are applied in different areas such as education, telecommunications, retail, social networks, and banking services. The goal is to identify customers in the risk of dropout to support retention strategies. This research developed a systematic literature review to evaluate the development of existing studies to predict dropout using machine learning, following the guidelines recommended by Kitchenham and Peterson. The systematic review followed three phases planning, conducting, and reporting. The selection of the most relevant articles was based on the use of Active Systematic Review tool using artificial intelligence algorithms. The criteria identified 28 articles and several research lines where identified. Dropout is a transversal problem for several sectors of economic activity, where it can be taken countermeasures before it happens if detected early

Advanced analytical methods for fraud detection: a systematic literature review

The developments of the digital era demand new ways of producing goods and rendering services. This fast-paced evolution in the companies implies a new approach from the auditors, who must keep up with the constant transformation. With the dynamic dimensions of data, it is important to seize the opportunity to add value to the companies. The need to apply more robust methods to detect fraud is evident. In this thesis the use of advanced analytical methods for fraud detection will be investigated, through the analysis of the existent literature on this topic. Both a systematic review of the literature and a bibliometric approach will be applied to the most appropriate database to measure the scientific production and current trends. This study intends to contribute to the academic research that have been conducted, in order to centralize the existing information on this topic

Exploring Text Mining and Analytics for Applications in Public Security: An in-depth dive into a systematic literature review

Text mining and related analytics emerge as a technological approach to support human activities in extracting useful knowledge through texts in several formats. From a managerial point of view, it can help organizations in planning and decision-making processes, providing information that was not previously evident through textual materials produced internally or even externally. In this context, within the public/governmental scope, public security agencies are great beneficiaries of the tools associated with text mining, in several aspects, from applications in the criminal area to the collection of people's opinions and sentiments about the actions taken to promote their welfare. This article reports details of a systematic literature review focused on identifying the main areas of text mining application in public security, the most recurrent technological tools, and future research directions. The searches covered four major article bases (Scopus, Web of Science, IEEE Xplore, and ACM Digital Library), selecting 194 materials published between 2014 and the first half of 2021, among journals, conferences, and book chapters. There were several findings concerning the targets of the literature review, as presented in the results of this article

Fraud Detection In Mobile Communications Networks Using User Profiling And Classification Techniques

Fraud detection is an important application, since network operators lose a relevant portion of their revenue to fraud. The intentions of mobile phone users cannot be well observed except through the call data. The call data is used in describing behavioural patterns of users. Neural networks and probabilistic models are employed in learning these usage patterns from call data by detecting changes in established usage patterns or to recognize typical usage patterns of fraud. The methods are shown to be effective in detecting fraudulent behaviour by empirically testing the methods with data from real mobile communications networks.Keywords: Call data, fraud detection, neural networks, probabilistic models, user profilin