107 research outputs found
Towards Secure and Verifiable Computation of KNN Queries in Outsourced Environments
The popularity of cloud computing has increased significantly in the last few years due to scalability, cost efficiency, resiliency, and quality of service. Organizations are more interested in outsourcing the database and DBMS functionalities to the cloud owing to the tremendous growth of big data and on-demand access requirements. As the data is outsourced to untrusted parties, security has become a key consideration to achieve the confidentiality and integrity of data. Therefore, data owners must transform and encrypt the data before outsourcing. In this paper, we focus on a Secure and Verifiable Computation for k-Nearest Neighbor (SVC-kNN) problem. The existing verifiable computation approaches for the kNN problem delegate the verification task solely to a single semi-trusted party. We show that these approaches are unreliable in terms of security, as the verification server could be either dishonest or compromised. To address these issues, we propose a novel solution to the SVC-kNN problem that utilizes the random-splitting approach in conjunction with the homomorphic properties under a two-cloud model. Specifically, the clouds generate and send verification proofs to end-users, allowing them to verify the computation results efficiently. Our solution is highly efficient from the data owner and query issuers’ perspective as it significantly reduces the encryption cost and pre-processing time. Furthermore, we show the correctness of our solution using Proof by Induction methodology to prove the Euclidean Distance Verification. Finally, with a thorough analysis and the empirical results on a real data set, we demonstrate the efficiency and effectiveness of our protocol
A Secure and Verifiable Computation for k-Nearest Neighbor Queries in Cloud
The popularity of cloud computing has increased significantly in the last few years due to scalability, cost efficiency, resiliency, and quality of service. Organizations are more interested in outsourcing the database and DBMS functionalities to the cloud owing to the tremendous growth of big data and on-demand access requirements. As the data is outsourced to untrusted parties, security has become a key consideration to achieve the confidentiality and integrity of data. Therefore, data owners must transform and encrypt the data before outsourcing. In this paper, we focus on a Secure and Verifiable Computation for k-Nearest Neighbor (SVC-kNN) problem. The existing verifiable computation approaches for the kNN problem delegate the verification task solely to a single semi-trusted party. We show that these approaches are unreliable in terms of security, as the verification server could be either dishonest or compromised. To address these issues, we propose a novel solution to the SVC-kNN problem that utilizes the random-splitting approach in conjunction with the homomorphic properties under a two-cloud model. Specifically, the clouds generate and send verification proofs to end-users, allowing them to verify the computation results efficiently. Our solution is highly efficient from the data owner and query issuers’ perspective as it significantly reduces the encryption cost and pre-processing time. Furthermore, we demonstrated the correctness of our solution using Proof by Induction methodology to prove the Euclidean Distance Verification
Security of Information in Cloud Computing: A Systematic Review
Data storage in cloud have become a great concern today. Many encryption and decryption methods have already been proposed to secure cloud data but everything comes with its pros and cons, this paper provides a critical overview of these cryptography techniques, issues and solutions regarding its security and availability
Optimum parameter machine learning classification and prediction of Internet of Things (IoT) malwares using static malware analysis techniques
Application of machine learning in the field of malware analysis is not a new concept, there have been lots of researches done on the classification of malware in android and windows environments. However, when it comes to malware analysis in the internet of things (IoT), it still requires work to be done. IoT was not designed to keeping security/privacy under consideration. Therefore, this area is full of research challenges. This study seeks to evaluate important machine learning classifiers like Support Vector Machines, Neural Network, Random Forest, Decision Trees, Naive Bayes, Bayesian Network, etc. and proposes a framework to utilize static feature extraction and selection processes highlight issues like over-fitting and generalization of classifiers to get an optimized algorithm with better performance. For background study, we used systematic literature review to find out research gaps in IoT, presented malware as a big challenge for IoT and the reasons for applying malware analysis targeting IoT devices and finally perform classification on malware dataset. The classification process used was applied on three different datasets containing file header, program header and section headers as features. Preliminary results show the accuracy of over 90% on file header, program header, and section headers. The scope of this document just discusses these results as initial results and still require some issues to be addressed which may effect on the performance measures
Machine Learning-Enabled IoT Security: Open Issues and Challenges Under Advanced Persistent Threats
Despite its technological benefits, Internet of Things (IoT) has cyber
weaknesses due to the vulnerabilities in the wireless medium. Machine learning
(ML)-based methods are widely used against cyber threats in IoT networks with
promising performance. Advanced persistent threat (APT) is prominent for
cybercriminals to compromise networks, and it is crucial to long-term and
harmful characteristics. However, it is difficult to apply ML-based approaches
to identify APT attacks to obtain a promising detection performance due to an
extremely small percentage among normal traffic. There are limited surveys to
fully investigate APT attacks in IoT networks due to the lack of public
datasets with all types of APT attacks. It is worth to bridge the
state-of-the-art in network attack detection with APT attack detection in a
comprehensive review article. This survey article reviews the security
challenges in IoT networks and presents the well-known attacks, APT attacks,
and threat models in IoT systems. Meanwhile, signature-based, anomaly-based,
and hybrid intrusion detection systems are summarized for IoT networks. The
article highlights statistical insights regarding frequently applied ML-based
methods against network intrusion alongside the number of attacks types
detected. Finally, open issues and challenges for common network intrusion and
APT attacks are presented for future research.Comment: ACM Computing Surveys, 2022, 35 pages, 10 Figures, 8 Table
A Practical Framework for Storing and Searching Encrypted Data on Cloud Storage
Security has become a significant concern with the increased popularity of
cloud storage services. It comes with the vulnerability of being accessed by
third parties. Security is one of the major hurdles in the cloud server for the
user when the user data that reside in local storage is outsourced to the
cloud. It has given rise to security concerns involved in data confidentiality
even after the deletion of data from cloud storage. Though, it raises a serious
problem when the encrypted data needs to be shared with more people than the
data owner initially designated. However, searching on encrypted data is a
fundamental issue in cloud storage. The method of searching over encrypted data
represents a significant challenge in the cloud.
Searchable encryption allows a cloud server to conduct a search over
encrypted data on behalf of the data users without learning the underlying
plaintexts. While many academic SE schemes show provable security, they usually
expose some query information, making them less practical, weak in usability,
and challenging to deploy. Also, sharing encrypted data with other authorized
users must provide each document's secret key. However, this way has many
limitations due to the difficulty of key management and distribution.
We have designed the system using the existing cryptographic approaches,
ensuring the search on encrypted data over the cloud. The primary focus of our
proposed model is to ensure user privacy and security through a less
computationally intensive, user-friendly system with a trusted third party
entity. To demonstrate our proposed model, we have implemented a web
application called CryptoSearch as an overlay system on top of a well-known
cloud storage domain. It exhibits secure search on encrypted data with no
compromise to the user-friendliness and the scheme's functional performance in
real-world applications.Comment: 146 Pages, Master's Thesis, 6 Chapters, 96 Figures, 11 Table
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