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Adaptive Database Intrusion Detection based on Michigan-style Deep Learning Classifier System
http://doi.org/10.5626/JOK.2023.50.10.891
In a role-based access control (RBAC) environment, database intrusion detection can be achieved by designing a role classifier for query transactions and determining it as an intrusion when the predicted role differs from the actually performed role. The current query-role classifier design methods utilize deep learning models, but it was difficult to simultaneously achieve high accuracy and incomplete adaptability for changing patterns. To solve this problem, this study proposes a Michigan-style Deep Learning Classifier System (MDLCS). This method applies a divide-and-conquer strategy that divides the input space into patterns and assigns an optimal classifier, combining the evolutionary computation principle of a Michigan-style learning classifier system with a deep learning classifier to adapt and improve detection performance for real-time changing patterns.The proposed MDLCS method provides strong adaptability and robustness compared to existing intrusion detection methods such as anomaly detection, signature-based detection and behavior-based detection. MDLCS was evaluated in a commercial database following the TPC-E schema and achieved a 26.81%p improved detection performance compared to existing methods under real environmental conditions in which new patterns sequentially emerge.
Privacy Protection Method based on Multi-Object Authentication in Intelligent CCTV Environment
http://doi.org/10.5626/JOK.2019.46.2.154
In the intelligent CCTV surveillance environment, personal identity is confirmed based on face recognition. However, the recognition rate of the current face recognition technology is still faulty. In particular, face recognition may not work correctly due to various causes such as CCTV shot quality, weather, personal pose and facial expression, hairstyle, lighting condition, and so on. In this case, there is a great risk of exposing object`s privacy information in the video surveillance environment due to erroneous object judgment. The proposed method can increase the recognition rate of objects based on the CCTV-RFID hybrid authentication method, and thus protect the privacy of the image object.
Case Study for Collecting Policy Evaluation Factors upon Request when Creating XACML Policy
http://doi.org/10.5626/JOK.2018.45.9.975
As the Internet of Things environment continues to expand, access control issues continue to emerge. OneM2M, one of the standards of the IOT platform, allows access control using XACML. In the arena of access control, conflicts must be solved. Because of this, various solutions are being investigated in order to solve these problems. Currently, however, the policy editor must solve policy conflicts by themselves. So, the policy editor needs to be able to effectively collect information about policies and conditions that affect their policy evaluation decisions in order to resolve policy conflicts. In this paper, we analyze policy and express policy evaluation methods in terms of Truth Table. In addition, we present a tree-based policy evaluation factor collection method through a case study on a policy evaluation factor collection method according to requests using Truth Table.
An Effective Technique for Protecting Application Data using Security Enhanced (SE) Android in Rooted Android Phones
This paper analyzes security threats in Security Enhanced (SE) Android and proposes a new technique to efficiently protect application data including private information on rooted Android phones. On an unrooted device, application data can be accessed by the application itself according to the access control models. However, on a rooted device, a root-privileged shell can disable part or all of the access control model enforcement procedures. Therefore, a root-privileged shell can directly access sensitive data of other applications, and a malicious application can leak the data of other applications outside the device. To address this problem, the proposed technique allows only some specific processes to access to the data of other applications including private information by modifying the existing SEAndroid Linux Security Module (LSM) Hook function. Also, a new domain type of process is added to the target system to enforce stronger security rules. In addition, the proposed technique separates the directory type of a newly installed application and the directory type of previously installed applications. Experimental results show that the proposed technique can effectively protect the data of each application and incur performance overhead up to or less than 2 seconds.
Mandatory Access Control for Android Application Security
June-sung Na, Do-Yun Kim, Wooguil Pak, Young-June Choi
In this paper, we investigate the security issues of the Android platform which dominates the global market of smart mobile devices. The current permission model for Android security is not powerful and has two problems. One is the coarse-grained relationship between permissions and methods which require them. The other is that mobile users do not have rights to control the permissions of the application. To solve these problems, we propose MacDroid which can control the platform’s resources for accessing installed applications. Users can control the application’s behavior via MacDroid’s policy. We have divided the permission set into method units. The results of the performance test using a pure Android platform show that our proposed scheme can improve security within a short time.
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