A Model for Data Leakage Detection. Risk Detection and Mitigation Raisoni Institute of Engg. And Technology rpjadhav gmail. Rajesh Kumar. What is Data leakage detection? What is it's use, how can Moreover, there might exist some redundancy nodes in the graph generated in CoBAn. Xiaohong Huang et al. In this paper, we employ a hybrid approaches which combines graph and vector representations. When representing the confidential textual content and its context of each cluster, the graph of each cluster which includes only confidential and contextual nodes is created.
Redundancy Information Reduction When dealing with text-related tasks, redundancy information is generally useless and even worse, it might decrease the efficiency of task execution.
The principle of PCA is to transform multiple attributes into a few primary attributes, which can reflect the information of original data effectively.
However, the complexity of PCA is generally high and there might be part of original information loss. LSI represents textual data with latent topics that consists of specific terms, but in most cases, the influence of specific terms are ignored. In this paper, the reduction method from rough set theory, as shown in Section 3 , is employed and partly recomposed to meet requirements. Data Leakage Prevention With the number of leakage incidents and the cost they inflict continues to increase, the threat of data leakage posed to companies and organizations has become more and more serious [ 39 — 41 ].
Considering the enormity of data leakage prevention, various models and approaches have been developed to address the problem of data leakage prevention. Tripwire is a more recent prototype system proposed by Joe DeBlasio et al. However, Tripwire is more suitable for forensics rather than confidential data leakage prevention. In , Wenjia Xu et al. Nevertheless, the method focus on data encryption rather than data detection.
Since smart devices based on ARM processor become an attractive target of cyberattacks, Jinhua Cui et al. But it pays less attention to the scenarios of intentional or accidental data leakage from insider. According to the work of Ding Wang et al. In addition, Ding Wang et al. The training phrase can be further divided into three steps, clustering step, graph building step, and pruning step. During the training phrase, the training documents are first classified into different clusters, then each cluster is represented by graph, and finally the nodes of each graph are pruned in terms of their importance.
During the detection phrase, documents are matched to the graphs of clusters respectively and the confidential scores are calculated. A document is considered as confidential only if its confidential score exceeds a predefined threshold. Clustering Documents with DBSCAN In the first step, we apply stemming and stop-words removal to all documents in training set, and transform the processed documents into vectors of weighted terms.
After applying DBSCAN with cosine measure to the vectors, which represent the training documents, each resulting cluster represents an independent topic of training documents and there might exist both confidential and nonconfidential documents. As shown in Figure 1. In this paper, we have carried out the work on business information sharing data which contains some sensitive information to investigate the security challenges of data in the field of business communication.
The greatest challenge that is associated here is to prevent the integrity of the data while sharing the data from organization to the third party, where there exist huge chances of data loss, leakages or alteration. This paper highlights the concepts of data leakage, the techniques to detect the data leakage and the process of protecting the leaked data based on encrypted form.
References  M. Alazab and R. A mixed approach combining result-. And such as birth date and postal code, often combine formulating the Mean shift, mode seeking, and clustering - Pattern Analysis and In this paper, the mean shift algorithm is generalized in Gaussian shape, which, without loss of generality, centers at Again, when the kernel is not 'flat, no merger.
Joint Face Detection and Alignment using Multi-task The cascade face detector proposed by Viola and Jones  The 4th A latent In this paper we consider the prob A major innovation of the Dalal-Triggs detector was the Anomaly Detection using Graph Databases and Machine By adapting well- known kill chain mechanisms and a combine of a time series In this paper, we propose an accurate edge detector using richer To answer Sensor placement for leak detection and location in The main contribution of this paper consists in combining a clustering IEEE Trans.
In this paper, we propose a system for hiding data related to a user's Secret sec; Information security has been researched to considerable Although an Within the realm of such threats, among the most difficult to detect and prevent involve covert The biggest DLP challenge lies in protecting the large amounts of sensitive data which exist in unstructured form e. Therefore, DLP solution providers are continuously improving their data discovery methods using approaches such as fingerprinting and natural- language processing.
Initialize a variable V Fig. And notwithstanding it had handy over sensitive information, it may watermark every object in order that it may trace its origins with absolute certainty. However, in several cases it should so work with agents which will not be percent sure, Associate in Nursingd it's going to not be sure if a leaked object came from an agent or from another supply, Fig.
This model is comparatively straightforward, however it captures the essential trade-offs. The distributing objects judiciously will build a major distinction in distinctive guilty agents, particularly in cases wherever there's massive overlap within the information that agents should receive.
A preliminary discussion of such a model is obtainable in another open drawback is that the extension of the allocation ways in order that it will handle agent requests Fig.
Schechter, R. Dhamija, A. Ozment, and I. Fischer, program. This model is comparatively Security and Compliance - interview with Jayshree Ullal, straightforward, however it believes that it captures the senior VP of Cisco essential trade-offs. It are shown that distributing objects judiciously will  "Dark Reading: Automating Breach Detection For The build a major distinction in distinctive guilty agents, Way Security Professionals Think".
Watermarks are often terribly helpful in of a system. The main advantage of graph-based model is that it can not only capture the contents and structure of a document but also represent the terms together with their context. The differences between the variants are related to term-based techniques. The biggest DLP challenge lies in protecting the large amounts of sensitive data which exist in unstructured form e. Clark, S. Step 2.
McIverand C. Start with an arbitrary document that has not been visited and find all the documents in its -neighborhood. Because combining scores from different methods is not We first build language models for the confidential and non-confidential documents of the same cluster, which are denoted by confidential vector model and nonconfidential vector model.
Unless is greater than , the non-confidential documents of the expanding cluster are included to recalculate the scores of context terms in original cluster. Rani, "Data leakage prevention system with time stamp," International Conference on Information Communication and Embedded Systems , Chennai, , pp
Apparently, a term is more likely to be considered as confidential if it appears in the similar contexts in other confidential documents. Note that not all clusters need to be expanded. But it pays less attention to the scenarios of intentional or accidental data leakage from insider. The remainder of this paper is organized as follows. To the best of our knowledge, graph-based model is seldom employed in text-related tasks. Related Work In this section, we review clustering of textual documents, attribute reduction method, and graph representation of textual documents, respectively.
In general, graph-based model is usually employed in the domain of information retrieval such as PageRank [ 29 ] and HITS [ 30 ]. As shown in Figure 1. The probabilities of a confidential term together with its context appearing in confidential documents and nonconfidential documents, which are denoted by and , are calculated separately. As shown in Figure 2 , for each cluster, a set of confidential terms and a set of its context terms are obtained after the training phase, and confidential terms and its context terms are represented as confidential nodes and context nodes respectively. Note that not all clusters need to be expanded.
The main advantage of graph-based model is that it can not only capture the contents and structure of a document but also represent the terms together with their context. In this paper we consider the prob The 4th Biondi, A. The support vector clustering algorithm created by Hava Siegelmann and Vladimir Vapnik applies the statistics of support vectors, developed in the support vector machines algorithm, to categorize unlabeled data, and is one of the most widely used clustering algorithms in industrial applications.
On the basis of VSM model and TF-IDF method, existing textual documents clustering algorithms can be divided into five main categories: partitioning methods, hierarchical methods, density-based methods, grid-based methods, and model-based methods.