| This is a preprint: it has been accepted for publication in the Conference: 2020 6th IEEE International | |
| Conference on Network Softwarization (NetSoft) . | |
| • DOI: 10.1109/NetSoft48620.2020.9165337 | |
| Detection of Insider Threats using Artificial | |
| Intelligence and Visualisation | |
| Vasileios Koutsouvelis∗, Stavros Shiaeles†, Bogdan Ghita‡, Gueltoum Bendiab‡ | |
| ∗Open University of Cyprus, 33 Yiannou Kranidioti Ave., Latsia, Nicosia, Cyprus | |
| [email protected] | |
| †Network and Security Research Group (NSRG), School of Computing, | |
| University of Portsmouth, Winston Churchill Avenue, Portsmouth, PO1 2UP, Hampshire, UK | |
| [email protected] | |
| ‡ Centre for Security, Communications and Networks Research (CSCAN), | |
| School of Computing and Mathematics, Plymouth University, Drake Circus, Plymouth PL4 8AA, UK | |
| [email protected], [email protected] | |
| Abstract —Insider threats are one of the most damaging risk | |
| factors for the IT systems and infrastructure of a company or an | |
| organization; identification of insider threats has prompted the | |
| interest of the world academic research community, with several | |
| solutions having been proposed to alleviate their potential impact. | |
| For th e implementation of the experimental stage described in this | |
| study, the Convolutional Neural Network (from now on CNN) | |
| algorithm was used and implemented via the Google TensorFlow | |
| program, which was trained to identify potential threats from | |
| images produced by the available dataset. From the examination | |
| of the images that were produced and with the help of Machine | |
| Learning, the question whether the activity of each user is | |
| classified as “malicious” or not for the Information System was | |
| answere d. | |
| Index Terms —Threats, visualization, security, artificial intelli - | |
| gence, machine learning | |
| I. INTRODUCTION | |
| Computers nowadays are used in every human activity and | |
| all kinds of operations, from residential to commercial and from | |
| basic service provisioning to research. Beyond their benefit, | |
| the risks and cases of deliberate or accidental de struction, | |
| tampering or unauthorised use of data and computer resources, | |
| in general, are increasing. The consequences of possible | |
| tampering, leakage, or misuse of data can lead not only to | |
| significant damage and costs but also risks for the protection of | |
| the human rights of individuals [1]. In the current security | |
| monitoring landscape, artificial neural networks play an | |
| extremely important role in the prevention and handling of | |
| internal threats [2] and alleviating the risks associated with | |
| information syste ms infrastructures. While a typical infras - | |
| tructure would have a level of protection against an external | |
| attack/threat, an internal threat refers to a person who may have | |
| privileged access to classified, sensitive, or proprietary data and | |
| uses this advant age to remove information from an organization | |
| and transfer it to unauthorised external users. Such attackers | |
| may include the users of the company, who can bypass the | |
| control procedures for access to classified data, and the users | |
| who gain access to user accounts with more rights in relation to these, which they already have. The purpose of | |
| this survey was to answer the question of whether Artificial | |
| Intelligence can be successfully used to detect malicious | |
| activity. The answer to this question went through three (3) | |
| steps: (a) collecting, processing, and classifying the data of the | |
| users tested; (b) visualizing the extracted data; (c) categorize | |
| the behaviour as malicious or normal. | |
| II. RELATED WORK | |
| The detection of malicious activity with the help of Artificial | |
| Intelligence has been a matter of concern to scholars, who have | |
| occasionally dealt with this issue, using different approaches. | |
| For instance, X. Ren, L. Wang, [3] presented a hybrid insider | |
| threat detection system, consisting of data processing, entity | |
| portrait, rule matching as well as iterative attention. Based | |
| on the results of the experiments, the authors claim that the | |
| propose d system provides a higher detection rate and better | |
| timeliness since it incorporates multiple complementary | |
| detection techniques and components. In [4], Sajjanhar, Atul et | |
| al proposed an image -based feature representation of the daily | |
| resource usage pattern of users in the organization. authors | |
| reported an overall accuracy of 99% when compared with | |
| other techniques. In another recent study [5], Kim, Junhong et | |
| al introduced an insider threat detection framework based on | |
| user behaviour modelling and anomaly detection algorithms | |
| and they support those experimental results indicate that the | |
| proposed framework can work well for imbalanced datasets in | |
| which there are only a few insider threats and where no domain | |
| experts’ knowledge is provided. In the same context, Hu, Teng | |
| et al [6] proposed a continuous identity authentication method | |
| based on mouse dynamic behaviour and deep learning to solve | |
| the insider threat attack detection problem and they claim that | |
| the experimental results showed that the proposed method could | |
| identify the user’s identities in seconds and has a lower false | |
| accep t rate (FAR) and false reject rate (FRR). | |
| Tuor, Kaplan and Hutchinson [7] referred to a system of | |
| profound knowledge for filtering log files’ data and analysing | |
| them. According to the writers, an internal threat behaviour varies widely, so the researcher does not attempt to formulate | |
| the pattern of behaviour which is a threat. Instead, new variants | |
| of Neural Networks (DNN) and Recurring Neural Networks | |
| (RNNs) are trained to recognize the activity that is typical | |
| for each user on a network. At the same time, these Neural | |
| Networks assess whether the behaviour of the user is normal or | |
| suspicious. The authors note that detecting malicious cases is | |
| particularly difficult because attackers often try to imitate the | |
| typical behaviour of a normal user. In another study, Sanzgiri | |
| and Dasgupta [8] presented the techniques that have been | |
| developed to detect internal threats referring to the researchers | |
| and their techniques. In particular, the objective of this paper is | |
| to present a categorization of the techniques used by | |
| researchers (Hu, Giordano, Kandias, Maybury, Greitzer, | |
| Eldardiry) to deal with insider threats. Finally, the researchers | |
| remarked that one of the main reasons why it is still difficult | |
| to detect attacks by internal users is the lack of sufficient | |
| real available data in order to build and test models and | |
| mechanisms for detecting internal threats. | |
| Breier and Branisova [9] proposed a threat detection method | |
| based on data mining techniques for analysing system log | |
| files (log analysis). Their approach was based on Apache | |
| Hadoop, which allows the processing of large volumes of data | |
| in a parallel way. The method detects new types of viola - | |
| tions without further human intervention, while the overall | |
| error reaches a value below 10%. Legg, Buckley, Goldsmith | |
| and Creese (2015) [10] proposed Corporate Insider Threat | |
| Detection (CITD), a corporate threat detection system which | |
| incor porates technical and behavioural activities for the evalu - | |
| ation of threats caused by individuals. In particular, the system | |
| recognised the users and the role-based profiles and measured | |
| how they deviate from their observed behaviour in order to | |
| estimate the potential threat that a set of user activities can | |
| cause. Some other studies have used approaches [11] based on | |
| graphs to find malicious cases in structural data models that | |
| represent an internal threat activity that is looking for activities | |
| that display similarities to normal data transactions but are | |
| structurally different from them. Others [12] have suggested | |
| approaches that combine Structural Anomaly Detection - SA. | |
| Despite the work to date, the challenge to holistically | |
| observe and analyse user and application behaviour remains a | |
| current one, due to its volume and complexity. The benefits | |
| of AI-based solution are obvious when faced with the large | |
| amounts of data collected, but interpreting the data and results | |
| demands further research. | |
| III. PROPOSED APPROACH | |
| As identified in the previous section, existing research | |
| demonstrated the efficiency of machine learning approaches, | |
| but also exposed their limitations in segregating the complexity | |
| of user behaviour into normal and malicious. To account | |
| for this complexity, we apply the CNN algorithm on user | |
| interaction data, as reflected through the log files collected from | |
| individual users. Unlike other studies focusing on domain | |
| knowledge to detect malicious behaviours through the use of | |
| specific structural anomaly detection [13], our proposed approach focuses exclusively on the behaviour of each user | |
| of the Information Technology System. In addition to the | |
| standard approach of log data collection [10, 14], the method | |
| also considers the organizational role for each user when estab - | |
| lishing the behaviour profile, which improved significantly the | |
| accuracy of the method. For example, the “malicious” activity | |
| of a user, who holds an IT Admin position in the organization, | |
| may justify - to a certain extent - this activity. | |
| In the context of internal threats, the method aims to | |
| discriminate between legitimate and malicious behaviour by | |
| investigating the differences in the associated visualisation | |
| maps. For each user, the approach creates an image that | |
| depicted his/her activity and behaviour, as emerged from their | |
| interaction with various information systems. While the result - | |
| ing images may appear visuall y different, they were processed | |
| through a machine learning algorithm in order to automatically | |
| recognize which subset of the users appear to exhibit malicious | |
| behaviour (and therefore posing a threat for the respective | |
| information systems) and which are legitimate/benign ones. | |
| Two stages are critical for the success of the method: the | |
| input data and the processing method. Given the proposed | |
| approach is aiming to provide a holistic view of the user | |
| interactions, the most appropriate method was considered to | |
| be converting log events into a visual representation. A full | |
| overview of the process is provided in the following section, | |
| including the attempts to highlight the most relevant features | |
| through the chosen visualisation. The second critical s tage, | |
| information processing, was biased by the choice of input data. | |
| Research undertaken in recent years demonstrated that | |
| Convolutional Neural Networks are indeed extremely efficient | |
| at image analysis, as shown by [15]. However, they have also | |
| proved the ir efficiency in the security area. Given their ability | |
| to deal with complex relationship, CNNs have been applied | |
| from basic security problems, as in [16] which introduced a | |
| CNN -based generic detection engine, to [17] for analysis of | |
| encrypted content. | |
| The approach presented in this paper follows from the mal - | |
| ware classification method from Wang et al. [18] of converting | |
| the data analysis task into an image recognition one. Unlike | |
| [18] and [19], which look at either malware or network activity | |
| to detect attacks, this paper aims to extend the analysis into | |
| the logging footprint to detect malicious vs normal behaviour. | |
| As highlighted in previous studies, the challenge is to ensure | |
| that sufficient input data is available for the method to be | |
| successful, and the transformation applied makes it compatible | |
| with the image analysis task. | |
| A. Methodology | |
| The input data was the Insider Threat Test Dataset from | |
| CERT, which is part of the Institute of Software Engineering | |
| (SEI). The file included log files, CSV type, which recorded | |
| an activity covering eighteen (18) months, collected between | |
| 01.01.2010 and 31.05.2011. Through these files and after | |
| analysis and pro cessing, it was attempted to present an image | |
| of the Information System and to analyse the behaviour of users | |
| identified as malicious. For each user, the inputs included login records, files/documents used or opened, emails sent and | |
| received, web browsing history, devices used, and user role | |
| within the organization. | |
| The steps we followed to complete the process and draw | |
| our conclusions were 1) data sharing and creation of files | |
| based on the data of the user under consideration, 2) importing | |
| the data files we created in the ”D3.js” library, selecting an | |
| appropriate image creation plan, examining the application | |
| library’s patterns and creating images of the user i n question | |
| that included his/her activity during each day, 3) creating | |
| images, 4) implementing and training the CNN algorithm in | |
| Tensorflow program and examining user behaviour, which we | |
| have described as ”normal” or ”malicious”; and 5) drawing | |
| conclusions . | |
| 1) First Stage: In the first stage of pre-processing , the raw | |
| input files were parsed to classify the log files from fifteen users | |
| according to the user role (salesman, IT admin, electrical | |
| engineer, mechanical engineer, administrator, production line | |
| worker, computer scientist, and software quality engineer). | |
| First, log files were parsed for each of the 15 users to create | |
| separate profiles consisting of three files (file.csv, email.csv, | |
| http.csv), which included the complete activity. In the second | |
| step, the profile files were compared against a number of rules | |
| that defined threats and malicious behaviour. In the website | |
| category, we chose terms that were associated with social | |
| networks, work search sites, malware, and file sharing. The | |
| parsing scripts also searched for attached files, which were | |
| divided into three size intervals, defining small [50K B -100K | |
| B] medium [100KB -200KB] and large files (over 200KB). | |
| In parallel, we also parsed the p rofile files for terms associ - | |
| ated with malware, such as Keylogger, files that may have been | |
| leaked to the Internet, and files that may have been distributed | |
| over the Internet. The result of the pre-processing was a set of | |
| three files per user, which had the following structure: date, | |
| user (username), host (from which the action was carried out), | |
| keyword (defining the type of threat), threat index (e ach | |
| specific threat was assigned a numerical category) which files | |
| contained the respective threats. The activity was classified | |
| using a separate record field which defined a specific type of | |
| [illegal] activity. At a subsequent stage, the field was | |
| transco ded to a unique colour for visualization of the images | |
| associated with the user activity map. The collected content | |
| for the users was then aggregated into weekly and monthly | |
| activity for long-term analysis. | |
| 2) Second Stage: In the second stage of the experiment, | |
| the numerical input was processed through the Java D3.js | |
| library to visualize and produce images that depict the activity | |
| of each user. Concerning D3.js is an open -source JavaScript | |
| library, used for document and data handling, which is based | |
| on web templates. It is used through upgraded web browsers, | |
| combining powerful data visualization methods and elements. | |
| Large datasets can be easily linked to SVG objects using simple | |
| D3.js functions to create rich text and graphics di agrams. With | |
| a minim al resource burden on a computer system, D3.js is | |
| extremely fast, supporting large data sets and dynamic | |
| interactions through static or moving images. As mentioned above, the illegal activity was colour -coded for | |
| visual processing as follows: blue for social and professional | |
| interaction, such as job search and social networking sites, red | |
| for sharing site file -sharing websites, pink for email threats, | |
| green for file threats, yellow for 50-100 KB email attachments, | |
| orange for 100-200 KB email attachments, and brown for | |
| > 200KB email attachments. Using this coding, the user data | |
| was converted to an hourly resolution heatmap, summarising | |
| week -long and month -long data; this allowed a harmonised | |
| view of the data for the two timeframes. This process aimed | |
| to convert the numerical data into a visual representation and | |
| characterization of the activity to determine whether it is | |
| malicious or normal. | |
| The result is a two-dimension heatmap, colour -coded as | |
| described above, with the days of the month on the vertical | |
| and the hours of the day horizontally. The dimensions of the | |
| design area were determined according to the size of the grid | |
| that was set. The result was the creation of a space consisting | |
| of hourly activity squares, each of which was coloured with | |
| the dominant activity identified during that respective timeslot. | |
| Following the generation of the activity -based heatmap, the | |
| full dataset included a total of 1199 images; these were | |
| manually labelled as normal or malicious. The selection was | |
| based on specific criteria, indicative of the density of the | |
| activity at specific time intervals, its colours, and the time | |
| of day. Following manual analysis, 769 images were labelled | |
| as containing malicious activity and 430 images were labelled | |
| as normal. Indicatively, Figure 1 presents four such images. | |
| Fig. 1. Display of user’ normal and malicious activity weekly and monthly | |
| Fig. 2. Schematic layout of the CNN algorithm implemented | |
| 3) Third Stage: In the third stage of the experiment, | |
| Google’s Tensorflow program was used to design a six -layer | |
| CNN that would recognize whether the image was classified | |
| as normal or malicious. CNN is a class of forward -facing | |
| artificial neural networks and has been successfully applied | |
| to the analysis and recognition of visual images, videos, and | |
| natural language processing systems. It also uses relatively | |
| little processing compared to other image sorting algorithms. | |
| This means that the network easily learns filters made using | |
| traditiona l algorithms. It is also known as an invariant artificial | |
| neural network, based on the weight’s architecture. Finally, in | |
| the previous step one thousand one hundred ninety -nine (1199) | |
| images were produced, of which - as reported - four hundred | |
| and thirty (430) were assessed as containing normal activity. | |
| For this reason, a corresponding number of malicious images | |
| were selected. Finally, eight hundred and sixty (860) images | |
| were used for this stage, of which eight hundred forty (840) | |
| were the training data images as follows: | |
| 1) Training Data : 80% of training images were used. | |
| 2) Validation Data : 20% of training images were used for | |
| validation. | |
| Figure 2 shows the resulting implementation. | |
| B. Evalu ation | |
| In order to ensure a balanced dataset, the 769 malicious | |
| activity images were reduced through random selection to 430 | |
| images, matching the number of normal activity images. The | |
| resulting dataset had 860 samples, including an equal number | |
| of normal and malicious activity samples; the dataset was split | |
| as follows: 20 images were set aside for validation and the | |
| remaining 840 images were split 80% training (672 images) | |
| and 20% testi ng (168 images). The validation images were also | |
| selected in a balanced fashion, with an equal number | |
| (10) of normal and malicious activity samples. The forecasting | |
| of the results was successful, with a proportion that reached | |
| 100%; the CNN algorithm was traced graphically to | |
| illustrate training accuracy, validation accuracy, and cost. | |
| Below are presented the three training exercises (training | |
| accuracy, validation accuracy, cost) and the implementation graph of the | |
| CNN algorithm. | |
| According to the graphs in Figure 3, training accuracy starts | |
| at a value close to 0.450, i .e., 45%, and has an increasing value | |
| to reach a rate close to 100%. Validation accuracy (F igure 3) | |
| starts from a value close to 0.400, i.e., 40%, and has a rising | |
| value to reach a rate close to 90%. While the cost starts from a | |
| value close to 0.700, i.e., 70% and has a decreasing value to | |
| arrive at a value, which is close to 0. | |
| IV. CONCLUSION | |
| The purpose of this study was to investigate the feasibility of | |
| using machine learning techniques to detect malicious activity | |
| by converting activity reports into a visual representation. The | |
| answer to this question has gone through three stages: a) the | |
| collection, processing, and classification of the data of the users | |
| under consideration, b) the visualization of the extracted data, | |
| and c) the use of the CNN algorithm to classify behaviour | |
| into malicious or normal. The algorithm was trained and tested | |
| using a dataset of 860 created images, including both malicious | |
| and normal activity. Our conclusion is that, with the | |
| methodology used, the malicious activity of the users of the | |
| information system was achieved. The forecasting of the | |
| results was successful , with a percentage that reached 100%. | |
| Table I is a concise table showing the results of some internal | |
| recognition methods, both in terms of validation accuracy and | |
| some of their comparisons. | |
| In conclusion, it should be noted that the characterization | |
| of a user’s behaviour as malicious is also dependent on the | |
| Security Policy that the Company or Organization adopts to | |
| protect its Information System. For the analysis, one of the | |
| sets polices was that visiting social networking sites or job | |
| search websites is falls outside normal activity and should | |
| be categorised as malicious. In addition, an important role in | |
| characterising a user’s activity is played by the position held | |
| in the organization and the set policy and associated analysis | |
| should also consider this. | |
| Fig. 3. Graphic representation of Training Accuracy, Validation Accuracy and Cost | |
| TABLE I | |
| COMPARISON OF OUR MET HOD WITH OTHERS | |
| Study Accuracy Comparison | |
| Proposed method Testing Accuracy: 100% Mechanical learning (machine learning) and training of the CNN algorithm for | |
| Training Accuracy:100% | |
| Validation Accuracy:90.6% | |
| Cost: 0.582 the implementation and categorization of the user’s behaviour in normal and | |
| malicious. | |
| W. Eberle et al [11] Testing Accuracy: 95% In this method a graphical theoretical approach was used to detect malicious user | |
| behaviour in an Information System | |
| O. Brdiczka et al [12] Server Eitrigg:82.74% This study proposes an approach combining structural anomaly detection (SA) | |
| Server Cenarion Circle:89.09% from social networks and information networks as well as the psychological | |
| Server Bleeding Hollow:79.84% profiling (PP) of individuals. | |
| W. T. Young et al [13] Indicators:87.4% This study focuses on the awareness of domain knowledge to detect malicious | |
| Anomalies:97.9% behaviour through the use of specific SAs algorithms. | |
| Scenarios:80.6% | |
| J. Breier et al [14] The error rate of the method used was less than 10%. In the method we used, | |
| the error rate approaches zero (0). | |
| P. A. Legg et al [10] The method used was based on collecting log data, by building a profile for each | |
| user and his / her property. In the method we used, the user property was not | |
| evaluated. | |
| X.Ren, L.Wang [3] The method proposes a hybrid intelligent system for insider threat detection that | |
| aims to realize more effective detection of security incidents by incorporating | |
| multiple complementary detection techniques, such as entity portrait, rule match - | |
| ing and iterative attention. In our method, only the user activity was evaluated | |
| A.Sajjanhar et al [4] Accuracy 99% The method proposes an image -based feature representation of the daily resource | |
| usage pattern of users in the organization. Classification models are applied to | |
| the representative images to detect anomalous behaviour of insiders. The images | |
| are classified too malicious and no malicious | |
| J.Kim et al [5] Accuracy > 90% The method proposes insider -threat detection methods based on user behaviour | |
| modelling and anomaly detection algorithms. Experimental results show that the | |
| proposed framework can work reasonably well to detect insiders’ malicious | |
| behaviours . Although the proposed framework was empirically verified, there are | |
| some limitations in the current research, which led the authors to future research | |
| directions | |
| ACKNOWLEDGMENT | |
| This project has received funding from the Euro - | |
| pean Union’s Horizon 2020 research and innovation | |
| programme under grant agreement no. 786698. The work re- | |
| flects only the authors’ view and the Agency is not responsible | |
| for any use that may be made of the information it contains. | |
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