Journal ArticleDOI
Security Threats to Hadoop: Data Leakage Attacks and Investigation
Reads0
Chats0
TLDR
Some possible data leakage attacks in Hadoop are presented and an investigation framework is proposed and tested based on some simulated cases.Abstract:
As one of the most popular platforms for processing big data, Hadoop has low costs, convenience, and fast speed. However, it is also a significant target of data leakage attacks, as a growing number of businesses and individuals store and process their private data in it. How to investigate data leakage attacks in Hadoop is an important but long-neglected issue. This article first presents some possible data leakage attacks in Hadoop. Then an investigation framework is proposed and tested based on some simulated cases.read more
Citations
More filters
Journal ArticleDOI
Attribute based honey encryption algorithm for securing big data: Hadoop distributed file system perspective.
Gayatri Kapil,Alka Agrawal,Abdulaziz Attaallah,Abdullah Algarni,Rajeev Kumar,Raees Ahmad Khan +5 more
TL;DR: Attribute Based Encryption with the honey encryption on Hadoop, i.e., Attribute Based Honey Encryption (ABHE) is integrated and shows considerable improvement in performance during the encryption-decryption of files.
Journal ArticleDOI
MapReduce: an infrastructure review and research insights
TL;DR: This paper surveys researches conducted on the MapReduce framework in the context of its open-source implementation, Hadoop, in order to summarize and report the wide topic area at the infrastructure level.
Journal Article
Data Wrangling and Data Leakage in Machine Learning for Healthcare
TL;DR: Nowadays, healthcare and life sciences overall have produced massive amounts of real-time data by enterprise resource planning (ERP) which turns into varied and challenging to avert data leakage.
Journal ArticleDOI
Your Model Trains on My Data? Protecting Intellectual Property of Training Data via Membership Fingerprint Authentication
TL;DR: MeFA is a novel framework for detecting training data IP embezzlement via Membership Fingerprint Authentication, which is able to determine whether a suspect ML model is trained on the to be protected target data or not and can also serve as a post-protection to verify the ownership of ML models, without modifying the training process of the model.
Proceedings ArticleDOI
Hadoop Distributed File System Security -A Review
S. Suganya,S. Selvamuthukumaran +1 more
TL;DR: A review of algorithms or methodologies suggested for the storage of large volume of unstructured, real time data and streams at a high velocity in Hadoop.
References
More filters
Journal ArticleDOI
A lightweight live memory forensic approach based on hardware virtualization
TL;DR: A lightweight live memory forensic framework based on hardware virtualization that can build a virtualization environment on-the-fly and acquire and analyze evidence at the hypervisor level is proposed and two novel forensic methods are proposed to verify the effectiveness of the framework.
Proceedings ArticleDOI
Progger: An Efficient, Tamper-Evident Kernel-Space Logger for Cloud Data Provenance Tracking
Ryan K. L. Ko,Mark A. Will +1 more
TL;DR: Progger (Provenance Logger), a kernel-space logger which potentially empowers all cloud stakeholders to trace their data, is presented, which provides high assurance of data security and data activity audit.
Book ChapterDOI
Secure Hadoop with Encrypted HDFS
Seon Young Park,Youngseok Lee +1 more
TL;DR: From experiments with a small Hadoop testbed, it is shown that the representative MapReduce job on encrypted HDFS generates affordable computation overhead less than 7%.
Journal ArticleDOI
Data correlation-based analysis methods for automatic memory forensic
Xiao Fu,Xiaojiang Du,Bin Luo +2 more
TL;DR: This paper presents an automatic memory analysis methodology based on data correlation that can discover the relationships among processes, files, users, Dynamic-link library DLLs, and network connections and reorganize these independent memory evidences and disclose their meanings in a high semantic level.