Biblio

Filters: Keyword is Clustering algorithms  [Clear All Filters]
2019-06-10
Hussain, K., Hussain, S. J., Jhanjhi, N., Humayun, M..  2019.  SYN Flood Attack Detection based on Bayes Estimator (SFADBE) For MANET. 2019 International Conference on Computer and Information Sciences (ICCIS). :1–4.

SYN flood attack is a very serious cause for disturbing the normal traffic in MANET. SYN flood attack takes advantage of the congestion caused by populating a specific route with unwanted traffic that results in the denial of services. In this paper, we proposed an Adaptive Detection Mechanism using Artificial Intelligence technique named as SYN Flood Attack Detection Based on Bayes Estimator (SFADBE) for Mobile ad hoc Network (MANET). In SFADBE, every node will gather the current information of the available channel and the secure and congested free (Best Path) channel for the traffic is selected. Due to constant congestion, the availability of the data path can be the cause of SYN Flood attack. By using this AI technique, we experienced the SYN Flood detection probability more than the others did. Simulation results show that our proposed SFADBE algorithm is low cost and robust as compared to the other existing approaches.

2019-04-05
Nan, Z., Zhai, L., Zhai, L., Liu, H..  2018.  Botnet Homology Method Based on Symbolic Approximation Algorithm of Communication Characteristic Curve. 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). :1-6.
The IRC botnet is the earliest and most significant botnet group that has a significant impact. Its characteristic is to control multiple zombies hosts through the IRC protocol and constructing command control channels. Relevant research analyzes the large amount of network traffic generated by command interaction between the botnet client and the C&C server. Packet capture traffic monitoring on the network is currently a more effective detection method, but this information does not reflect the essential characteristics of the IRC botnet. The increase in the amount of erroneous judgments has often occurred. To identify whether the botnet control server is a homogenous botnet, dynamic network communication characteristic curves are extracted. For unequal time series, dynamic time warping distance clustering is used to identify the homologous botnets by category, and in order to improve detection. Speed, experiments will use SAX to reduce the dimension of the extracted curve, reducing the time cost without reducing the accuracy.
2019-03-06
Leung, C. K., Hoi, C. S. H., Pazdor, A. G. M., Wodi, B. H., Cuzzocrea, A..  2018.  Privacy-Preserving Frequent Pattern Mining from Big Uncertain Data. 2018 IEEE International Conference on Big Data (Big Data). :5101-5110.
As we are living in the era of big data, high volumes of wide varieties of data which may be of different veracity (e.g., precise data, imprecise and uncertain data) are easily generated or collected at a high velocity in many real-life applications. Embedded in these big data is valuable knowledge and useful information, which can be discovered by big data science solutions. As a popular data science task, frequent pattern mining aims to discover implicit, previously unknown and potentially useful information and valuable knowledge in terms of sets of frequently co-occurring merchandise items and/or events. Many of the existing frequent pattern mining algorithms use a transaction-centric mining approach to find frequent patterns from precise data. However, there are situations in which an item-centric mining approach is more appropriate, and there are also situations in which data are imprecise and uncertain. Hence, in this paper, we present an item-centric algorithm for mining frequent patterns from big uncertain data. In recent years, big data have been gaining the attention from the research community as driven by relevant technological innovations (e.g., clouds) and novel paradigms (e.g., social networks). As big data are typically published online to support knowledge management and fruition processes, these big data are usually handled by multiple owners with possible secure multi-part computation issues. Thus, privacy and security of big data has become a fundamental problem in this research context. In this paper, we present, not only an item-centric algorithm for mining frequent patterns from big uncertain data, but also a privacy-preserving algorithm. In other words, we present- in this paper-a privacy-preserving item-centric algorithm for mining frequent patterns from big uncertain data. Results of our analytical and empirical evaluation show the effectiveness of our algorithm in mining frequent patterns from big uncertain data in a privacy-preserving manner.
2019-01-16
Choudhary, S., Kesswani, N..  2018.  Detection and Prevention of Routing Attacks in Internet of Things. 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :1537–1540.

Internet of things (IoT) is the smart network which connects smart objects over the Internet. The Internet is untrusted and unreliable network and thus IoT network is vulnerable to different kind of attacks. Conventional encryption and authentication techniques sometimes fail on IoT based network and intrusion may succeed to destroy the network. So, it is necessary to design intrusion detection system for such network. In our paper, we detect routing attacks such as sinkhole and selective forwarding. We have also tried to prevent our network from these attacks. We designed detection and prevention algorithm, i.e., KMA (Key Match Algorithm) and CBA (Cluster- Based Algorithm) in MatLab simulation environment. We gave two intrusion detection mechanisms and compared their results as well. True positive intrusion detection rate for our work is between 50% to 80% with KMA and 76% to 96% with CBA algorithm.

2019-05-01
Douzi, S., Benchaji, I., ElOuahidi, B..  2018.  Hybrid Approach for Intrusion Detection Using Fuzzy Association Rules. 2018 2nd Cyber Security in Networking Conference (CSNet). :1-3.

Rapid development of internet and network technologies has led to considerable increase in number of attacks. Intrusion detection system is one of the important ways to achieve high security in computer networks. However, it have curse of dimensionality which tends to increase time complexity and decrease resource utilization. To improve the ability of detecting anomaly intrusions, a combined algorithm is proposed based on Weighted Fuzzy C-Mean Clustering Algorithm (WFCM) and Fuzzy logic. Decision making is performed in two stages. In the first stage, WFCM algorithm is applied to reduce the input data space. The reduced dataset is then fed to Fuzzy Logic scheme to build the fuzzy sets, membership function and the rules that decide whether an instance represents an anomaly or not.

2019-08-05
Xia, S., Li, N., Xiaofeng, T., Fang, C..  2018.  Multiple Attributes Based Spoofing Detection Using an Improved Clustering Algorithm in Mobile Edge Network. 2018 1st IEEE International Conference on Hot Information-Centric Networking (HotICN). :242–243.

Information centric network (ICN) based Mobile Edge Computing (MEC) network has drawn growing attentions in recent years. The distributed network architecture brings new security problems, especially the identity security problem. Because of the cloud platform deployed on the edge of the MEC network, multiple channel attributes can be easily obtained and processed. Thus this paper proposes a multiple channel attributes based spoofing detection mechanism. To further reduce the complexity, we also propose an improved clustering algorithm. The simulation results indicate that the proposed spoofing detection method can provide near-optimal performance with extremely low complexity.

2019-06-10
Farooq, H. M., Otaibi, N. M..  2018.  Optimal Machine Learning Algorithms for Cyber Threat Detection. 2018 UKSim-AMSS 20th International Conference on Computer Modelling and Simulation (UKSim). :32-37.

With the exponential hike in cyber threats, organizations are now striving for better data mining techniques in order to analyze security logs received from their IT infrastructures to ensure effective and automated cyber threat detection. Machine Learning (ML) based analytics for security machine data is the next emerging trend in cyber security, aimed at mining security data to uncover advanced targeted cyber threats actors and minimizing the operational overheads of maintaining static correlation rules. However, selection of optimal machine learning algorithm for security log analytics still remains an impeding factor against the success of data science in cyber security due to the risk of large number of false-positive detections, especially in the case of large-scale or global Security Operations Center (SOC) environments. This fact brings a dire need for an efficient machine learning based cyber threat detection model, capable of minimizing the false detection rates. In this paper, we are proposing optimal machine learning algorithms with their implementation framework based on analytical and empirical evaluations of gathered results, while using various prediction, classification and forecasting algorithms.

2019-03-15
Bian, R., Xue, M., Wang, J..  2018.  Building Trusted Golden Models-Free Hardware Trojan Detection Framework Against Untrustworthy Testing Parties Using a Novel Clustering Ensemble Technique. 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :1458-1463.

As a result of the globalization of integrated circuits (ICs) design and fabrication process, ICs are becoming vulnerable to hardware Trojans. Most of the existing hardware Trojan detection works suppose that the testing stage is trustworthy. However, testing parties may conspire with malicious attackers to modify the results of hardware Trojan detection. In this paper, we propose a trusted and robust hardware Trojan detection framework against untrustworthy testing parties exploiting a novel clustering ensemble method. The proposed technique can expose the malicious modifications on Trojan detection results introduced by untrustworthy testing parties. Compared with the state-of-the-art detection methods, the proposed technique does not require fabricated golden chips or simulated golden models. The experiment results on ISCAS89 benchmark circuits show that the proposed technique can resist modifications robustly and detect hardware Trojans with decent accuracy (up to 91%).

2019-03-22
Duan, J., Zeng, Z., Oprea, A., Vasudevan, S..  2018.  Automated Generation and Selection of Interpretable Features for Enterprise Security. 2018 IEEE International Conference on Big Data (Big Data). :1258-1265.

We present an effective machine learning method for malicious activity detection in enterprise security logs. Our method involves feature engineering, or generating new features by applying operators on features of the raw data. We generate DNF formulas from raw features, extract Boolean functions from them, and leverage Fourier analysis to generate new parity features and rank them based on their highest Fourier coefficients. We demonstrate on real enterprise data sets that the engineered features enhance the performance of a wide range of classifiers and clustering algorithms. As compared to classification of raw data features, the engineered features achieve up to 50.6% improvement in malicious recall, while sacrificing no more than 0.47% in accuracy. We also observe better isolation of malicious clusters, when performing clustering on engineered features. In general, a small number of engineered features achieve higher performance than raw data features according to our metrics of interest. Our feature engineering method also retains interpretability, an important consideration in cyber security applications.

2019-02-25
Ali, S. S., Maqsood, J..  2018.  .Net library for SMS spam detection using machine learning: A cross platform solution. 2018 15th International Bhurban Conference on Applied Sciences and Technology (IBCAST). :470–476.

Short Message Service is now-days the most used way of communication in the electronic world. While many researches exist on the email spam detection, we haven't had the insight knowledge about the spam done within the SMS's. This might be because the frequency of spam in these short messages is quite low than the emails. This paper presents different ways of analyzing spam for SMS and a new pre-processing way to get the actual dataset of spam messages. This dataset was then used on different algorithm techniques to find the best working algorithm in terms of both accuracy and recall. Random Forest algorithm was then implemented in a real world application library written in C\# for cross platform .Net development. This library is capable of using a prebuild model for classifying a new dataset for spam and ham.

2017-12-20
Wang, Y., Huang, Y., Zheng, W., Zhou, Z., Liu, D., Lu, M..  2017.  Combining convolutional neural network and self-adaptive algorithm to defeat synthetic multi-digit text-based CAPTCHA. 2017 IEEE International Conference on Industrial Technology (ICIT). :980–985.
We always use CAPTCHA(Completely Automated Public Turing test to Tell Computers and Humans Apart) to prevent automated bot for data entry. Although there are various kinds of CAPTCHAs, text-based scheme is still applied most widely, because it is one of the most convenient and user-friendly way for daily user [1]. The fact is that segmentations of different types of CAPTCHAs are not always the same, which means one of CAPTCHA's bottleneck is the segmentation. Once we could accurately split the character, the problem could be solved much easier. Unfortunately, the best way to divide them is still case by case, which is to say there is no universal way to achieve it. In this paper, we present a novel algorithm to achieve state-of-the-art performance, what was more, we also constructed a new convolutional neural network as an add-on recognition part to stabilize our state-of-the-art performance of the whole CAPTCHA system. The CAPTCHA datasets we are using is from the State Administration for Industry& Commerce of the People's Republic of China. In this datasets, there are totally 33 entrances of CAPTCHAs. In this experiments, we assume that each of the entrance is known. Results are provided showing how our algorithms work well towards these CAPTCHAs.
Ren, H., Jiang, F., Wang, H..  2017.  Resource allocation based on clustering algorithm for hybrid device-to-device networks. 2017 9th International Conference on Wireless Communications and Signal Processing (WCSP). :1–6.
In order to improve the spectrum utilization rate of Device-to-Device (D2D) communication, we study the hybrid resource allocation problem, which allows both the resource reuse and resource dedicated mode to work simultaneously. Meanwhile, multiple D2D devices are permitted to share uplink cellular resources with some designated cellular user equipment (CUE). Combined with the transmission requirement of different users, the optimized resource allocation problem is built which is a NP-hard problem. A heuristic greedy throughput maximization (HGTM) based on clustering algorithm is then proposed to solve the above problem. Numerical results demonstrate that the proposed HGTM outperforms existing algorithms in the sum throughput, CUEs SINR performance and the number of accessed D2D deceives.
Kim, M., Cho, H..  2017.  Secure Data Collection in Spatially Clustered Wireless Sensor Networks. 2017 25th International Conference on Systems Engineering (ICSEng). :268–276.
A wireless sensor network (WSN) can provide a low cost and flexible solution to sensing and monitoring for large distributed applications. To save energy and prolong the network lifetime, the WSN is often partitioned into a set of spatial clusters. Each cluster includes sensor nodes with similar sensing data, and only a few sensor nodes (samplers) report their sensing data to a base node. Then the base node may predict the missed data of non-samplers using the spatial correlation between sensor nodes. The problem is that the WSN is vulnerable to internal security threat such as node compromise. If the samplers are compromised and report incorrect data intentionally, then the WSN should be contaminated rapidly due to the process of data prediction at the base node. In this paper, we propose three algorithms to detect compromised samplers for secure data collection in the WSN. The proposed algorithms leverage the unique property of spatial clustering to alleviate the overhead of compromised node detection. Experiment results indicate that the proposed algorithms can identify compromised samplers with a high accuracy and low energy consumption when as many as 50% sensor nodes are misbehaving.
2018-01-16
Kumar, P. S., Parthiban, L., Jegatheeswari, V..  2017.  Auditing of Data Integrity over Dynamic Data in Cloud. 2017 Second International Conference on Recent Trends and Challenges in Computational Models (ICRTCCM). :43–48.

Cloud computing is a new computing paradigm which encourages remote data storage. This facility shoots up the necessity of secure data auditing mechanism over outsourced data. Several mechanisms are proposed in the literature for supporting dynamic data. However, most of the existing schemes lack the security feature, which can withstand collusion attacks between the cloud server and the abrogated users. This paper presents a technique to overthrow the collusion attacks and the data auditing mechanism is achieved by means of vector commitment and backward unlinkable verifier local revocation group signature. The proposed work supports multiple users to deal with the remote cloud data. The performance of the proposed work is analysed and compared with the existing techniques and the experimental results are observed to be satisfactory in terms of computational and time complexity.

Bhaya, W., EbadyManaa, M..  2017.  DDoS attack detection approach using an efficient cluster analysis in large data scale. 2017 Annual Conference on New Trends in Information Communications Technology Applications (NTICT). :168–173.

Distributed Denial of Service (DDoS) attack is a congestion-based attack that makes both the network and host-based resources unavailable for legitimate users, sending flooding attack packets to the victim's resources. The non-existence of predefined rules to correctly identify the genuine network flow made the task of DDoS attack detection very difficult. In this paper, a combination of unsupervised data mining techniques as intrusion detection system are introduced. The entropy concept in term of windowing the incoming packets is applied with data mining technique using Clustering Using Representative (CURE) as cluster analysis to detect the DDoS attack in network flow. The data is mainly collected from DARPA2000, CAIDA2007 and CAIDA2008 datasets. The proposed approach has been evaluated and compared with several existing approaches in terms of accuracy, false alarm rate, detection rate, F. measure and Phi coefficient. Results indicates the superiority of the proposed approach with four out five detected phases, more than 99% accuracy rate 96.29% detection rate, around 0% false alarm rate 97.98% F-measure, and 97.98% Phi coefficient.

Nikolskaya, K. Y., Ivanov, S. A., Golodov, V. A., Sinkov, A. S..  2017.  Development of a mathematical model of the control beginning of DDoS-attacks and malicious traffic. 2017 International Conference "Quality Management,Transport and Information Security, Information Technologies" (IT QM IS). :84–86.

A technique and algorithms for early detection of the started attack and subsequent blocking of malicious traffic are proposed. The primary separation of mixed traffic into trustworthy and malicious traffic was carried out using cluster analysis. Classification of newly arrived requests was done using different classifiers with the help of received training samples and developed success criteria.

2018-04-04
Rupasinghe, R. A. A., Padmasiri, D. A., Senanayake, S. G. M. P., Godaliyadda, G. M. R. I., Ekanayake, M. P. B., Wijayakulasooriya, J. V..  2017.  Dynamic clustering for event detection and anomaly identification in video surveillance. 2017 IEEE International Conference on Industrial and Information Systems (ICIIS). :1–6.

This work introduces concepts and algorithms along with a case study validating them, to enhance the event detection, pattern recognition and anomaly identification results in real life video surveillance. The motivation for the work underlies in the observation that human behavioral patterns in general continuously evolve and adapt with time, rather than being static. First, limitations in existing work with respect to this phenomena are identified. Accordingly, the notion and algorithms of Dynamic Clustering are introduced in order to overcome these drawbacks. Correspondingly, we propose the concept of maintaining two separate sets of data in parallel, namely the Normal Plane and the Anomaly Plane, to successfully achieve the task of learning continuously. The practicability of the proposed algorithms in a real life scenario is demonstrated through a case study. From the analysis presented in this work, it is evident that a more comprehensive analysis, closely following human perception can be accomplished by incorporating the proposed notions and algorithms in a video surveillance event.

2018-02-21
Macharla, D. R., Tejaskanda, S..  2017.  An enhanced three-layer clustering approach and security framework for battlefeld surveillance. 2017 International conference on Microelectronic Devices, Circuits and Systems (ICMDCS). :1–6.

Hierarchical based formation is one of the approaches widely used to minimize the energy consumption in which node with higher residual energy routes the data gathered. Several hierarchical works were proposed in the literature with two and three layered architectures. In the work presented in this paper, we propose an enhanced architecture for three layered hierarchical clustering based approach, which is referred to as enhanced three-layer hierarchical clustering approach (EHCA). The EHCA is based on an enhanced feature of the grid node in terms of its mobility. Further, in our proposed EHCA, we introduce distributed clustering technique for lower level head selection and incorporate security mechanism to detect the presence of any malicious node. We show by simulation results that our proposed EHCA reduces the energy consumption significantly and thus improves the lifetime of the network. Also, we highlight the appropriateness of the proposed EHCA for battlefield surveillance applications.

2018-04-02
Innokentievich, T. P., Vasilevich, M. V..  2017.  The Evaluation of the Cryptographic Strength of Asymmetric Encryption Algorithms. 2017 Second Russia and Pacific Conference on Computer Technology and Applications (RPC). :180–183.

We propose a method for comparative analysis of evaluation of the cryptographic strength of the asymmetric encryption algorithms RSA and the existing GOST R 34.10-2001. Describes the fundamental design ratios, this method is based on computing capacity used for decoding and the forecast for the development of computer technology.

2018-06-20
Patil, S. U..  2017.  Gray hole attack detection in MANETs. 2017 2nd International Conference for Convergence in Technology (I2CT). :20–26.

Networking system does not liable on static infrastructure that interconnects various nodes in identical broadcast range dynamically called as Mobile Ad-hoc Network. A Network requires adaptive connectivity due to this data transmission rate increased. In this paper, we designed developed a dynamic cluster head selection to detect gray hole attack in MANETs on the origin of battery power. MANETs has dynamic nodes so we delivered novel way to choose cluster head by self-stabilizing election algorithm followed by MD5 algorithm for security purposes. The Dynamic cluster based intrusion revealing system to detect gray hole attack in MANET. This Architecture enhanced performance in terms of Packet delivery ratio and throughput due to dynamic cluster based IDS, associating results of existing system with proposed system, throughput of network increased, end to end delay and routing overhead less compared with existing system due to gray hole nodes in the MANET. The future work can be prolonged by using security algorithm AES and MD6 and also by including additional node to create large network by comparing multiple routing protocol in MANETs.

2018-06-11
Yang, C., Li, Z., Qu, W., Liu, Z., Qi, H..  2017.  Grid-Based Indexing and Search Algorithms for Large-Scale and High-Dimensional Data. 2017 14th International Symposium on Pervasive Systems, Algorithms and Networks 2017 11th International Conference on Frontier of Computer Science and Technology 2017 Third International Symposium of Creative Computing (ISPAN-FCST-ISCC). :46–51.

The rapid development of Internet has resulted in massive information overloading recently. These information is usually represented by high-dimensional feature vectors in many related applications such as recognition, classification and retrieval. These applications usually need efficient indexing and search methods for such large-scale and high-dimensional database, which typically is a challenging task. Some efforts have been made and solved this problem to some extent. However, most of them are implemented in a single machine, which is not suitable to handle large-scale database.In this paper, we present a novel data index structure and nearest neighbor search algorithm implemented on Apache Spark. We impose a grid on the database and index data by non-empty grid cells. This grid-based index structure is simple and easy to be implemented in parallel. Moreover, we propose to build a scalable KNN graph on the grids, which increase the efficiency of this index structure by a low cost in parallel implementation. Finally, experiments are conducted in both public databases and synthetic databases, showing that the proposed methods achieve overall high performance in both efficiency and accuracy.

2018-04-04
Gajjar, V., Khandhediya, Y., Gurnani, A..  2017.  Human Detection and Tracking for Video Surveillance: A Cognitive Science Approach. 2017 IEEE International Conference on Computer Vision Workshops (ICCVW). :2805–2809.

With crimes on the rise all around the world, video surveillance is becoming more important day by day. Due to the lack of human resources to monitor this increasing number of cameras manually, new computer vision algorithms to perform lower and higher level tasks are being developed. We have developed a new method incorporating the most acclaimed Histograms of Oriented Gradients, the theory of Visual Saliency and the saliency prediction model Deep Multi-Level Network to detect human beings in video sequences. Furthermore, we implemented the k - Means algorithm to cluster the HOG feature vectors of the positively detected windows and determined the path followed by a person in the video. We achieved a detection precision of 83.11% and a recall of 41.27%. We obtained these results 76.866 times faster than classification on normal images.

2018-04-11
Nandhini, M., Priya, P..  2017.  A Hybrid Routing Algorithm for Secure Environmental Monitoring System in WSN. 2017 International Conference on Communication and Signal Processing (ICCSP). :1061–1065.

Wireless sensor networks are the most prominent set of recently made sensor nodes. They play a numerous role in many applications like environmental monitoring, agriculture, Structural and industrial monitoring, defense applications. In WSN routing is one of the absolutely requisite techniques. It enhance the network lifetime. This can be gives additional priority and system security by using bio inspired algorithm. The combination of bio inspired algorithms and routing algorithms create a way to easy data transmission and improves network lifetime. We present a new metaheuristic hybrid algorithm namely firefly algorithm with Localizability aided localization routing protocol for encircle monitoring in wireless area. This algorithm entirely covers the wireless sensor area by localization process and clumping the sensor nodes with the use of LAL (Localizability Aided Localization) users can minimize the time latency, packet drop and packet loss compared to traditional methods.

2018-01-10
Thaler, S., Menkonvski, V., Petkovic, M..  2017.  Towards a neural language model for signature extraction from forensic logs. 2017 5th International Symposium on Digital Forensic and Security (ISDFS). :1–6.
Signature extraction is a critical preprocessing step in forensic log analysis because it enables sophisticated analysis techniques to be applied to logs. Currently, most signature extraction frameworks either use rule-based approaches or handcrafted algorithms. Rule-based systems are error-prone and require high maintenance effort. Hand-crafted algorithms use heuristics and tend to work well only for specialized use cases. In this paper we present a novel approach to extract signatures from forensic logs that is based on a neural language model. This language model learns to identify mutable and non-mutable parts in a log message. We use this information to extract signatures. Neural language models have shown to work extremely well for learning complex relationships in natural language text. We experimentally demonstrate that our model can detect which parts are mutable with an accuracy of 86.4%. We also show how extracted signatures can be used for clustering log lines.
2017-12-12
Gamachchi, A., Boztas, S..  2017.  Insider Threat Detection Through Attributed Graph Clustering. 2017 IEEE Trustcom/BigDataSE/ICESS. :112–119.

While most organizations continue to invest in traditional network defences, a formidable security challenge has been brewing within their own boundaries. Malicious insiders with privileged access in the guise of a trusted source have carried out many attacks causing far reaching damage to financial stability, national security and brand reputation for both public and private sector organizations. Growing exposure and impact of the whistleblower community and concerns about job security with changing organizational dynamics has further aggravated this situation. The unpredictability of malicious attackers, as well as the complexity of malicious actions, necessitates the careful analysis of network, system and user parameters correlated with insider threat problem. Thus it creates a high dimensional, heterogeneous data analysis problem in isolating suspicious users. This research work proposes an insider threat detection framework, which utilizes the attributed graph clustering techniques and outlier ranking mechanism for enterprise users. Empirical results also confirm the effectiveness of the method by achieving the best area under curve value of 0.7648 for the receiver operating characteristic curve.