Volume 14 (2022)
Volume 13 (2021)
Volume 12 (2020)
Volume 11 (2019)
Volume 10 (2018)
Volume 9 (2017)
Volume 8 (2016)
Volume 7 (2015)
Volume 6 (2014)
Volume 5 (2013)
Volume 4 (2012)
Volume 3 (2011)
Volume 2 (2010)
Volume 1 (2009)


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The ISC International Journal of Information Security (ISeCureis a peer reviewed scholarly publication by Iranian Society of Cryptology. ISeCure is published biannually in print and online with full texts of articles made available for free on the website of the journal under ISeCure open access policy. ISeCure is devoted to publishing theoretical scholarship on a variety of topics related to information security. The intended audience of the journal is any person with an interest in information security from an academic perspective such as engineers, mathematicians and computer scientists. A partial list of topics for review by the journal can be found in the Aims and Scope section. Manuscript types for submission are research papers, review papers, case reports, short communications and letters to the editor.  More information about the policies of the journal can be found on the About Journal and mostly in the Publication Ethics pages. To start a new submission, please first read the Guide for Authors page for detailed information about manuscript format, style and other requirements. Manuscript submission, refereeing and publishing are completely free of charge. New manuscripts should be submitted online by the corresponding author through the website after registration. Articles published in ISeCure Journal are indexed in the Emerging Sources Citation Index (ESCI) database of Web of Science/ISI and Scopus.

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Review Article
Cloud Computing Threats, Vulnerabilities and Countermeasures: A State-of-the-Art

Suryateja Satya Pericherla

Volume 15, Issue 1 , January 2023


  Cloud computing created a revolution in the way IT organizations and IT teams manage their internal digital resources and workloads. One major drawback or limitation of cloud computing, among others, is security. Cloud computing is plagued by a plethora of threats and vulnerabilities, with new ones being ...  Read More

Research Article
A Machine Learning Approach for Detecting and Categorizing Sensitive Methods in Android Malware

Hayyan Salman Hasan; Hasan Muhammad Deeb; Behrouz Tork Ladani

Volume 15, Issue 1 , January 2023


  Sensitive methods are those that are commonly used by Android malware to perform malicious behavior. These methods may be either evasion or malicious payload methods. Although there are several approaches to handle these methods for performing effective dynamic malware analysis, but generally most of ...  Read More

Research Article
Hierarchical Deterministic Wallets for Secure Steganography in Blockchain

Omid Torki; Maede Ashouri-Talouki; Mojtaba Mahdavi

Volume 15, Issue 1 , January 2023


  Steganography is a solution for covert communication and blockchain is a p2p network for data transmission, so the benefits of blockchain can be used in steganography. In this paper, we discuss the advantages of blockchain in steganography, which include the ability to embed hidden data without manual ...  Read More

Research Article
Quantum Cryptanalysis of Symmetric Primitives by Improving Relaxed Variants of Simon’s Algorithm

Ali Khosravi; Taraneh Eghlidos

Volume 15, Issue 1 , January 2023


  The main goal of Simon’s Algorithm is to find the period of periodic functions. However, if the target function does not satisfy Simon's promise completely or if the number of superposition queries of the adversary is limited, Simon's algorithm cannot compute the actual period, unambiguously. These ...  Read More

Research Article
Highly Efficient and Revocable CP-ABE with Outsourcing Decryption for IoT

Sina Abdollahi; Javad Mohajeri; Mahmoud Salmasizadeh

Volume 15, Issue 1 , January 2023


  Ciphertext-policy attribute-based encryption(CP-ABE) is considered a promising solution for secure data sharing in the cloud environment. Although very well expressiveness in ABE constructions can be achieved using a linear secret sharing scheme(LSSS), there is a significant drawback in such constructions. ...  Read More

Research Article
4n × 4n Diffusion Layers Based on Multiple 4 × 4 MDS Matrices

Mahdi Sajadieh; Arash Mirzaei

Volume 15, Issue 1 , January 2023


  In terms of security, MDS matrices are one of the best choices for diffusion layer of block ciphers. However, as these matrices grow in size, their software implementation becomes a challenge. In this paper, to benefit from the properties of MDS matrices and avoid the mentioned challenge, we use 4*4 ...  Read More

Review Article
A New Social Multi-Secret Sharing Scheme using Birkhoff Interpolation and Chinese Remainder Theorem

Mohammad Ebrahim Ebrahimi Kiasari; Nasrollah Pakniat; Abdolrasoul Mirghadri; Mojtaba Nazari

Volume 15, Issue 1 , January 2023


  Secret sharing (SS) schemes allow the sharing of a secret among a set of trustees in such a way that only some qualified subsets of them can recover the secret. Ordinary SS schemes assume that the trust to each trustee is fixed over time. However, this is not the case in many real scenarios. Social secret ...  Read More

Research Article
Lightweight Cryptographic S-Boxes Based on Efficient Hardware Structures for Block Ciphers

Bahram Rashidi

Volume 15, Issue 1 , January 2023


  In this paper, we present four low-cost substitution boxes (S-boxes) including two 4-bit S-boxes called S1 and S2 and two 8-bit S-boxes called SB1 and SB2, which are suitable for the development of lightweight block ciphers. The 8-bit SB1 S-box is constructed based on four 4-bit S-boxes, multiplication ...  Read More

Research Article
An Auto-Encoder based Membership Inference Attack against Generative Adversarial Network

Maryam Azadmanesh; Behrouz Shahgholi Ghahfarokhi; Maede Ashouri-Talouki

Articles in Press, Accepted Manuscript, Available Online from 04 September 2022


  Using generative models to produce unlimited synthetic samples is a popular replacement for database sharing. Generative Adversarial Network (GAN) is a popular class of generative models which generates synthetic data samples very similar to real training datasets. However, GAN models do not necessarily ...  Read More

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