Complex network tools to enable identification of a criminal community

Retrieving criminal ties and mining evidence from an organised crime incident, for example money laundering, has been a difficult task for crime investigators due to the involvement of different groups of people and their complex relationships. Extracting the criminal association from enormous amount of raw data and representing them explicitly is tedious and time consuming. A study of the complex networks literature reveals that graph-based detection methods have not, as yet, been used for money laundering detection. In this research, I explore the use of complex network analysis to identify the money laundering criminals’ communication associations, that is, the important people who communicate between known criminals and the reliance of the known criminals on the other individuals in a communication path. For this purpose, I use the publicly available Enron email database that happens to contain the communications of 10 criminals who were convicted of a money laundering crime. I show that my new shortest paths network search algorithm (SPNSA) combining shortest paths and network centrality measures is better able to isolate and identify criminals’ connections when compared with existing community detection algorithms and k-neighbourhood detection. The SPNSA is validated using three different investigative scenarios and in each scenario, the criminal network graphs formed are small and sparse hence suitable for further investigation. My research starts with isolating emails with ‘BCC’ recipients with a minimum of two recipients bcc-ed. ‘BCC’ recipients are inherently secretive and the email connections imply a trust relationship between sender and ‘BCC’ recipients. There are no studies on the usage of only those emails that have ‘BCC’ recipients to form a trust network, which leads me to analyse the ‘BCC’ email group separately. SPNSA is able to identify the group of criminals and their active intermediaries in this ‘BCC’ trust network. Corroborating this information with published information about the crimes that led to the collapse of Enron yields the discovery of persons of interest that were hidden between criminals, and could have contributed to the money laundering activity. For validation, larger email datasets that comprise of all ‘BCC’ and ‘TO/CC’ email transactions are used. On comparison with existing community detection algorithms, SPNSA is found to perform much better with regards to isolating the sub-networks that contain criminals. I have adapted the betweenness centrality measure to develop a reliance measure. This measure calculates the reliance of a criminal on an intermediate node and ranks the importance level of each intermediate node based on this reliability value. Both SPNSA and the reliance measure could be used as primary investigation tools to investigate connections between criminals in a complex network

A proposed conceptual success model of citizen-centric digital government in Malaysia

The emergence of Digital Government throughout the world is reflecting how governments are trying to find innovative digital solutions towards empowering social, economic and political advantage. Effective service delivery to citizens through Information Communication Technology application such as integrated citizen service information systems is a prerequisite to achieve citizen-centric digital government. Measuring success of such systems is a growing concern. However, very few studies have attempted to find success factors using Information Systems theoretical approach in the context of digital government, particularly in Malaysia. Therefore, this study is designed to bridge the gap by identifying such factors and propose a conceptual model. This study addresses success factors from system and personal traits’ perspectives, behavioral intention, satisfaction, trust and citizen empowerment as determinants of digital government success.Keywords: Digital government; e-government; trust; digital services; information system

Malaysian politicians’ connection pattern on twitter using sna: a case of Najib Razak

Najib Razak is one of the most prominent politicians in Malaysia whose popularity has risen worldwide over the years due to his political sharp-witted strategy and various political scandals. He is also identified as one of the most followed Malaysian politicians on social media, especially Twitter. Hence, this study aims to apply Social Network Analysis (SNA) to further examine the interactions between Twitter users and the relationship formed with Najib Razak. A complete network of Najib Razak's Twitter account is used to study the connection pattern, influence, and groups developed between account users in the network. Netlytic is used to extract the data on Twitter, and based on the extracted dataset, it is discovered that 1004 nodes that represent Twitter users, follows and mentions the @najibrazak Twitter account. The dataset was further analyzed using R to explore the interaction and the connection patterns were visualized using Gephi. Based on the findings, the connectivity, centrality and clustering of the top 10 most influential Twitter users that contribute to the discussion and mention of Najib Razak on Twitter were determined. The previous work using Najib Razak's twitter account focused on finding the relations between public and politicians by analyzing the issues discussed through language processing at topical and lexical level. Unlike the previous achievement, the results from this proposed SNA technique can be further analyzed to gather greater insights on the hidden relationship built between politicians to strengthen their position and distinguish their possible future followers for further investigations

Identifying a criminal's network of trust

The identification of influential nodes in complex network can be very challenging. If the network has a community structure, centrality measures may fail to identify the complete set of influential nodes, as the hubs and other central nodes of the network may lie inside only one community. Here we define a bipartite clustering coefficient that, by taking differently structured clusters into account, can find important nodes across communities

Advanced recurrent neural network with tensorflow for heart disease prediction

Heart disease has become one of the most critical disease that cause highest mortality rate. Deep learning is a subfield of machine learning that is based on learning multiple levels of representation and abstraction. In this paper we aim to present our proposed model on the heart disease prediction. This model aims to perform an advanced Recurrent Neural Network (RNN) model of deep learning to increase the accuracy of the existing model of predictions, which should be more than 98.23%. This paper discusses about the deep learning methods, draw comparison of performance among the existing systems and propose an enhanced RNN model to provide a better in terms of accuracy and feasibility. The presence of multiple Gated Recurrent Unit (GRU) have improvised the RNN model performance with 98.4% of accuracy. The Cleveland data for this study are obtained from UCI Repository. The further research and advancement possibilities are also mentioned in the paper

Using shortest path to discover criminal community

Extracting communities using existing community detection algorithms yields dense sub-networks that are difficult to analyse. Extracting a smaller sample that embodies the relationships of a list of suspects is an important part of the beginning of an investigation. In this paper, we present the efficacy of our shortest paths network search algorithm (SPNSA) that begins with an 'algorithm feed', a small subset of nodes of particular interest, and builds an investigative sub-network. The algorithm feed may consist of known criminals or suspects, or persons of influence. This sets our approach apart from existing community detection algorithms. We apply the SPNSA on the Enron Dataset of e-mail communications starting with those convicted of money laundering in relation to the collapse of Enron as the algorithm feed. The algorithm produces sparse and small sub-networks that could feasibly identify a list of persons and relationships to be further investigated. In contrast, we show that identifying sub-networks of interest using either existing community detection algorithms or a k-Neighbourhood approach produces subnetworks of much larger size and complexity. When the 18 top managers of Enron were used as the algorithm feed, the resulting sub-network identified 4 convicted criminals that were not managers and so not part of the algorithm feed. We directly validate the SPNSA by removing one of the convicted criminals from the algorithm feed and rerunning the algorithm; in 5 out of 9 cases the left out criminal occurred in the resulting sub-network

Systematic literature review of information security compliance behaviour theories

The paper aims to identify behavioural theories that influence information security policies compliance behaviour. A systematic review of empirical studies from eleven online databases (ACM digital library, Emerald Insight, IEEE Xplore digital library, Springer link, Science direct, Scopus, Web of Science, Oxford academic journals, SAGE journals, Taylor & Francis and Wiley online library) are conducted. This review identified 29 studies met its criterion for inclusion. The investigated theories were extracted and analysed. Total of 19 theories have been identified and studied concerning to security policy compliance behaviour. The result indicated that the most established theories in information security compliance behaviour studies are the Theory of Planned Behavior and Protection Motivation theory. Meanwhile, General Deterrence Theory, Neutralization theory, Social Bond Theory / Social Control Theory are used moderately in this research area. Less explored theories are namely Self Determination Theory, Knowledge, Attitude, and Behavior, Social Cognitive Theory, Involvement Theory, Health belief model, Theory of Interpersonal Behavior, Extended Parallel Processing Model, Organisational Control Theory, Psychological Reactance Theory, Norm Activation Theory, Organizational Behaviour Theory, Cognitive Evaluation Theory and Extended Job Demands-Resources. The results from this review may guide the development and evaluation of theories promoting information security compliance behaviours. This will further contribute in the development of an integrated theory of information security compliance behaviour

Proposed computer forensic approach for cloud computing environment

The security perimeter in computing has changed from a fixed boundary to an elastic boundary that is constantly changing and also the threats are evolving, making the Incident handler more difficult to analyze the information system based attacks. Therefore, the purposes of this study are introducing a new approach in identifying computer forensic attacks using Infrastructure as a Service (IaaS) in a cloud computing environment. First will identify and classify the different types of attacks on cloud infrastructure. Next, based on the attacks we are going to suggest an appropriate approach that can be utilized to collect as much data possible to perform a detailed investing of the incidents or attacks. Furthermore, the proposed approach will be tested in a virtual environment in order to check its effectiveness. Finally, refinement will be performed based on the results obtained and will be bench marked against the existing computer forensic approaches. Thus, this study contributes to better provide many data sources that can be used by the investigators to conduct forensics investigation in the infrastructure layer of the cloud computing. The findings will benefit the organizations which deploy private cloud services and infrastructure services which include virtual machines. Therefore, this paper discusses in detail the proposed computer forensic approach