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Public sentiment is regarded as an important measure for event detection, information security, policy making etc. Analyzing public sentiments relies more and more on large amount of multimodal contents, in contrast to the traditional text-based and image-based sentiment analysis. However, most previous works directly extract feature from image as the additional information for text modality and then...
Android app is often used at multiple devices of one user. Sometimes, an app recognizes another device to be the same device which it has known, and bypasses its authentication process. As a result, an attacker can get the same privilege as the original device owner has for the app. In this paper, we show how to get the privilege of the device owner in Android app and how to defend against the attack...
In this paper we present results of a research on automatic extremist text detection. For this purpose an experimental dataset in the Russian language was created. According to the Russian legislation we cannot make it publicly available. We compared various classification methods (multinomial naive Bayes, logistic regression, linear SVM, random forest, and gradient boosting) and evaluated the contribution...
The one-off public key scheme gets more and more attention for its security features. However, there are two problems in the current schemes proposed, one is that the security threat of user's privacy disclosure for plaintext transmission, the other one is that attackers can infer the identities of the communication participants via signature correctness verification. For the problem mentioned above,...
Over the years social network data has been mined to predict individuals' traits such as intelligence and sexual orientation. While mining social network data can provide many beneficial services to the user such as personalized experiences, it can also harm the user when used in making critical decisions such as employment. In this work, we investigate the reliability of applying data mining techniques...
Attack graph technique is a common tool for the evaluation of network security. However, attack graphs are generally too large and complex to be understood and interpreted by security administrators. This paper proposes an analysis framework for security attack graphs for a given IT infrastructure system. First, in order to facilitate the discovery of interconnectivities among vulnerabilities in a...
This paper focuses on one type of Covert Storage Channel (CSC) that uses the 6-bit TCP flag header in TCP/IP network packets to transmit secret messages between accomplices. We use relative entropy to characterize the irregularity of network flows in comparison to normal traffic. A normal profile is created by the frequency distribution of TCP flags in regular traffic packets. In detection, the TCP...
This paper takes an unsupervised learning approach for monitoring edge activity within an enterprise computer network. Using NetFlow records, features are gathered across the active connections (edges) in 15-minute time windows. Then, edges are grouped into clusters using the k-means algorithm. This process is repeated over contiguous windows. A series of informative indicators are derived by examining...
Traffic classification plays an important and basic role in network management and cyberspace security. With the widespread use of encryption techniques in network applications, encrypted traffic has recently become a great challenge for the traditional traffic classification methods. In this paper we proposed an end-to-end encrypted traffic classification method with one-dimensional convolution neural...
Terror operations are carried out by a team of terrorists with their interaction and cooperation. Different tie strengths, such as acquaintances, friends, and family members, influence the construction and reconstruction of the operation team. Understanding the interaction patterns of tie strength might be useful not only for advancing our understanding of terror operations, but ultimately for providing...
Identifying the physical person behind an SNS account has become a critical issue in investigations of SNS-involved crime cases. It is a challenging task because information provided by users on an SNS platform could be false, conflicting, missing and deceptive. One way to gain an accurate profile of a user is to link up all their multiple accounts created on different social platforms, which is referred...
Cyber-attacks are constantly increasing and can prove difficult to mitigate, even with proper cybersecurity controls. Currently, cyber threat intelligence (CTI) efforts focus on internal threat feeds such as antivirus and system logs. While this approach is valuable, it is reactive in nature as it relies on activity which has already occurred. CTI experts have argued that an actionable CTI program...
In order to effectively evaluate the impact on network situation under DDoS attacks, this paper proposes a hierarchical network threat situation assessment method based on D-S evidence theory for DDoS. It is divided into the basic data acquisition layer, the metric indexes extraction layer, the device threat assessment layer and the threat situation assessment layer. Firstly, we calculate indexes...
This paper presents a system that uses machine learning to recognize military vehicles in social media images. To do so, the system draws on recent advances in applying deep neural networks to computer vision tasks, while also making extensive use of openly available libraries, models and data. Training a vehicle recognition system over three classes, the paper reports on two experiments that use...
This paper explores the recently published works on iris template protection namely Indexing-First-One hashing. Despite the Indexing-First-One hashing offers high recognition performance with resistant against several major privacy and security attacks, it does not resolve the rotation inconsistent issues existed in conventional iris template due to head tilt/ rotation during user's eyes images acquisition...
With the development of social media technology, users often register accounts, post messages and create friend links on several different platforms. Performing user identity mapping on multi-platform based on the behavior patterns of users is considerable for network supervision and personalization service. The existing methods focus on utilizing either text information or structure information alone...
Data analytics is being increasingly used in cyber-security problems, and found to be useful in cases where data volumes and heterogeneity make it cumbersome for manual assessment by security experts. In practical cyber-security scenarios involving data-driven analytics, obtaining data with annotations (i.e. ground-truth labels) is a challenging and known limiting factor for many supervised security...
Public sentiments affecting health outcomes are increasingly modulated by social media. Existing literature mainly focus on investigating how network structure affects the contagion of health sentiments. However, most of these studies neglect that the interaction topology change in time. In fact, the change of inter-individual connections over time is associated with individual attributes. The mechanism...
Social media has become an important platform for people to express opinions, share information and communicate with others. Detecting and tracking topics from social media can help people grasp essential information and facilitate many security-related applications. As social media texts are usually short, traditional topic evolution models built based on LDA or HDP often suffer from the data sparsity...
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