Information Leakage Detection Research Articles

METHOD OF DETECTION OF INFORMATION LEAKAGE BY REJECTING TRAFFIC FROM THE INFORMATION COMMUNICATION NETWORK

In the work, an analysis of the methods of detecting the leakage of language information was carried out. The analysis showed the absence of a single scientific methodical apparatus or automated software complexes to ensure the operational implementation of traffic analysis. Therefore, the work is devoted to information leakage detection based on the deviation of traffic from the information communication network. An improved method of providing operational traffic analysis and informing about a suspicious situation is proposed. A situation that requires further detailed traffic analysis by automated software complexes or relevant specialists. The developed method allows informing, in real-time, the responsible specialists about a possible leak of information, which is based on the analysis of the deviation of the nature of the traffic from the elements of the information speech network. Deviations, the nature of the traffic from the elements of the network parameters are measured relative to the usual traffic of the telephone or voice network relative to these parameters. A comparative analysis of normal traffic with real-time traffic is carried out. This method further improves the methodology. The improvement was carried out due to practical recommendations regarding constant coefficients, and calculations. These coefficients for the improved method were chosen by calculation and empirically, which allows for a significantly reduced response of the traffic estimation system. This system uses the developed methodology to detect possible leakage of language information.

A Gate-Level Information Leakage Detection Framework of Sequential Circuit Using Z3

Hardware intellectual property (IP) cores from untrusted vendors are widely used, raising security concerns for system designers. Although formal methods provide powerful solutions for detecting malicious behaviors in hardware, the participation of manual work prevents the methods from reaching practical applications. For example, Information Flow Tracking (IFT) represents a powerful approach to preventing leakage of sensitive information. However, existing IFT solutions either introduce hardware overheads or lack practical automatic working procedures, especially for hardware sequential logic. To alleviate these challenges, we propose a framework that fully automates information leakage detection at the gate level of hardware. This framework introduces Z3, an SMT solver, to automatically check the violation of confidentiality. On the other hand, an automatic tool is developed to remove the manual workload further. In this tool, the gate level hardware is converted to the formal model firstly, and the integrity of the model is assessed. Along with the model converting step, the property for leakage detection is generated as well. The proposed solution is tested on 25 gate-level netlist benchmarks, where sequential designs are included to validate the effectiveness. As a result, Trojans leaking information from circuit outputs can be automatically detected. The measured time consumption of the entire working procedure validates the efficiency of the proposed approach.

Information Leakage Detection and Risk Assessment of Intelligent Mobile Devices

(1) Background: Smart mobile devices provide conveniences to people’s life, work, and entertainment all the time. The basis of these conveniences is the data exchange across the entire cyberspace, and privacy data leakage has become the focus of attention. (2) Methods: First, we used the method of directed information flow to conduct an API test for all applications in the application market, then obtained the application data transmission. Second, by using tablet computers, smart phones, and bracelets as the research objects, and taking the scores of senior users on the selected indicators as the original data, we used the fusion information entropy and Markov chain algorithm skillfully to build a data leakage risk assessment mode to obtain the steady-state probability values of different risk categories of each device, and then obtained the entropy values of three devices. (3) Results: Tablet computers have the largest entropy in the risk of data leakage, followed by bracelets and mobile phones. (4) Conclusions: This paper compares the risk situation of each risk category of each device, and puts forward simple avoidance opinions, which might lay a theoretical foundation for subsequent research on privacy protection strategies, image steganography, and device security improvements.

Review of Works Content Analyzer for Information Leakage Detection and Prevention in Android Smart Devices

The advent of android operating systems introduced tools to keep track of users’ information activities and prevent information leakagewhich bridged the trust between application developers and consumers. A review of related literature shows that several phenomenahad been developed to prevent malicious applications from stealing personal sensitive information from smart phones but there is stillthe need for efficient solutions. This study presents a literature review of works on content Analyzers for information leakage detectionand prevention on android-based devices. The review will help to combine different concept to minimize false positives that will in turnlead to increase in code coverage towards detecting the maximum number of data leaks.

Network social media information leakage detection based on link state awareness

Network social media information leakage detection based on link state awareness

Network social media information leakage detection based on link state awareness

Network social media information leakage detection based on link state awareness

Method for Evaluating Digital Video Electromagnetic Information Leakage from Video Cable

Electromagnetic emissions from electrical information equipment may contain useful information and lead to information leakage. In order to detect the electromagnetic information leakage of digital video cable, a rapid testing method for computer video information leakage detection without reconstructing the displayed image is proposed. In this method, the electromagnetic radiation spectrum of typical image transmitted by HDMI cable is simulated. Then the electromagnetic radiation spectrum of the computer when transmitting the same image as the typical image is obtained by conduction and radiation EMC test methods. Finally, the correlation between the spectrum of simulation and the test spectrum is analyzed, and the correlation coefficient is used to judge whether the computer has produced the electromagnetic leakage of video information. The analysis and test results show that when the correlation coefficient is greater than 0.5, the electromagnetic leakage of computer video information can be judged.

Content Analyzer for Information Leakage Detedction and Prevention in Android Smart Devices: A Conceptual Approach.

The advent of android operating system introduced tools to keep track of users’ information activities and prevent information leakage which bridged the trust between application developers and consumers. Literature shows that several phenomena had been developed to prevent malicious applications from stealing personal sensitive information from smart phones but there is still the need for efficient solutions. This study proposes a conceptual approach for the development of a contentAnalyzer for information leakage detection and prevention on android-based devices. The concept will help to minimize false positives that will in turn lead to increase in code coverage towards detecting the maximum number of data leaks. The proposed concept combines both static and dynamic analysis, and if implemented will improve checking through the codes in the file activities and vulnerabilities that could be a problem. Keywords: Android, ContentAnalyzer, Static Analysis, Dynamic Analysis, Information leakage, Information leakage detection, Information leakage Prevention.

A method for detecting image information leakage risk from electromagnetic emission of computer monitors

The unintentional electromagnetic (EM) emission of computer monitors may cause the leakage of image information displayed on the monitor. Detection of EM information leakage risk is significant for the information security of the monitor. The traditional detection method is to verify EM information leakage by reconstructing an image from EM emission. The detection method based on image reconstruction has limitations: adequate signal sampling rate, accurate synchronization signal, and dependence on operational experience. In this paper, we analyze the principle of image information leakage and propose an innovative detection method based on Convolutional Neural Network (CNN). This method can identify the image information in EM emission to verify the EM information leakage risk of the monitor. It overcomes the limitations of the traditional method with machine learning. This is a new attempt in the field of EM information leakage detection. Experimental results show that it is more adaptable and reliable in complex detection environment.

Information leakage detection and protection of leaked information by using the MAC-IP binding technique

The Digital world is advancing in terms of technological development day by day, resulting in an instantaneous rise in Data. This massive amount of Data has introduced the thought of Big Data, which has attracted both the business and IT sectors leaving the scope for huge opportunities. In turn, securing this massive data has become a challenging issue in the field of Information and Communication Technology. 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 article an attempt is also made to identify the user’s intention or behavior during the navigation of data. 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.

An improved data pre-processing method for classification and insider information leakage detection

Data pre-processing, a step performed prior to data processing, converts data into a form that is easy to analyse. In this study, we propose a method for the pre-processing and integration of data collected from various sources to detect insider information leakage; further, we evaluate the performance of data pre-processing by performing classification and detection experiments with collected normal and abnormal log data. An insider information leakage attack scenario was created, and the attack data for this scenario were generated in order to collect the corresponding log data. This preprocessing stage improved the efficiency of information leakage analysis and detection, as demonstrated by the results of our experiments that shown the performance with accuracies of 0.9991 and 0.9997, respectively, in source classification. In addition, we found that securing the attack scenario and actual attack data is a very important factor in insider information leakage detection owing to the small amount of attack data.

An improved data pre-processing method for classification and insider information leakage detection

Data pre-processing, a step performed prior to data processing, converts data into a form that is easy to analyse. In this study, we propose a method for the pre-processing and integration of data collected from various sources to detect insider information leakage; further, we evaluate the performance of data pre-processing by performing classification and detection experiments with collected normal and abnormal log data. An insider information leakage attack scenario was created, and the attack data for this scenario were generated in order to collect the corresponding log data. This preprocessing stage improved the efficiency of information leakage analysis and detection, as demonstrated by the results of our experiments that shown the performance with accuracies of 0.9991 and 0.9997, respectively, in source classification. In addition, we found that securing the attack scenario and actual attack data is a very important factor in insider information leakage detection owing to the small amount of attack data.

Leakage Detection and Risk Assessment on Privacy for Android Applications: LRPdroid

How to identify and manage information leakage of user privacy is a very crucial and sensitive topic for handheld mobile device manufacturers, telecommunication companies, and mobile device users. As the success of a financial fraud usually requires possessing a victim's private information, new types of personal identity theft and private information acquirement attack are developed and deployed along with various Apps in order to steal personal private information from mobile device users. With more than 50% of smartphone market share, Android-based mobile phone vendors and Internet service providers have to face the new challenge on user privacy management. In this paper, we present a user privacy analysis framework for an Android platform called LRPdroid. The goals of LRPdroid are to achieve information leakage detection, user privacy disclosure evaluation, and privacy risk assessment for Apps installed on Android-based mobile devices. With a formally defined user privacy model, LRPdroid can effectively support mobile users to manage their own privacy risks on targeted Apps. In addition, new privacy analysis viewpoints such as user perception and leakage awareness are introduced in LRPdroid. Two general App usage scenarios are evaluated with our system prototype to show the feasibility and practicability of the LRPdroid framework on user privacy management.

New Approach for Detecting Leakage of Internal Information; Using Emotional Recognition Technology

Currently, the leakage of internal information has emerged as one of the most significant security concerns in enterprise computing environments. Especially, damage due to internal information leakage by insiders is more serious than that by outsiders because insiders have considerable knowledge of the system’s identification and password (ID&P/W), the security system, and the main location of sensitive data. Therefore, many security companies are developing internal data leakage prevention techniques such as data leakage protection (DLP), digital right management (DRM), and system access control, etc. However, these techniques cannot effectively block the leakage of internal information by insiders who have a legitimate access authorization. The security system does not easily detect cases which a legitimate insider changes, deletes, and leaks data stored on the server. Therefore, we focused on the insider as the detection target to address this security weakness. In other words, we switched the detection target from objects (internal information) to subjects (insiders). We concentrated on biometrics signals change when an insider conducts abnormal behavior. When insiders attempt to leak internal information, they appear to display abnormal emotional conditions due to tension, agitation, and anxiety, etc. These conditions can be detected by the changes of biometrics signals such as pulse, temperature, and skin conductivity, etc. We carried out experiments in two ways in order to verify the effectiveness of the emotional recognition technology based on biometrics signals. We analyzed the possibility of internal information leakage detection using an emotional recognition technology based on biometrics signals through experiments.

Personal information leakage detection method using the inference-based access control model on the Android platform

The web services used on desktop can be accessed through a smartphone due to the development of smart devices. As the usage of smartphones increases, the importance of personal information security inside the smartphone is emphasized. The openness features of Android platform make a lot easier to develop an application and also deploying malicious codes into application is an easy task for hackers. The security practices are also growing rapidly as the number of malicious code increases exponentially. According to these circumstances, new methods for detecting and protecting the behavior of leaked personal information are needed to manage the personal information within a smartphone.In this paper, we study the permission access category in order to detect the malicious code, which discloses the personal information on Android environment such as equipment and location information, address book and messages, and solve the problem related to Resource access of Random Access Control method in conventional Android file system to detect the new malware or malicious code via the context ontology reasoning of permission access and API resource information which the personal information are leaked through. Then we propose an inference-based access control model, which can be enabled to access the proactive security. There is more improvement accuracy than existing malicious detecting techniques and effectiveness of access control model is verified through the proposal of inference-based access control model.

API간 상호 의존성 및 최단거리 분석을 통한 안드로이드 애플리케이션의 개인정보 유출 탐지 기법

In general, the benign apps transmit privacy information to the external to provide service to users as the malicious app does. In other words, the behavior of benign apps is similar to the one of malicious apps. Thus, the benign app can be easily manipulated for malicious purposes. Therefore, the malicious apps as well as the benign apps should notify the users of the possibility of privacy information leakage before installation to prevent the potential malicious behavior. In this paper, We propose the method to detect leakage of privacy information on the android app by analyzing API inter-dependency and shortest distance. Also, we present LeakDroid which detects leakage of privacy information on Android with the above method. Unlike dynamic approaches, LeakDroid analyzes Android apps on market site. To verify the privacy information leakage detection of LeakDroid, we experimented the well-known 250 malicious apps and the 1700 benign apps collected from Android Third party market. Our evaluation result shows that LeakDroid reached detection rate of 96.4% in the malicious apps and detected 68 true privacy information leakages inside the 1700 benign apps.

Behind BANANA: Design and Implementation of a Tool for Nesting Analysis of Mobile Ambients

We present a survey of the work on control-flow analysis carried on by the Venice Team during the Mefisto project. We study security issues, in particular information leakage detection, in the context of the Mobile Ambient calculus. We describe BANANA, a Java-based tool for ambient nesting analysis, by focussing on analysis accuracy and algorithmic optimizations.