arXiv subjects

Arash Habibi Lashkari

Publications and source records attributed to Arash Habibi Lashkari.

5 recordsLinked to original sources

arXiv2026

Future-Proofing Cloud Security Against Quantum Attacks: Risk, Transition, and Mitigation Strategies

Quantum Computing (QC) threatens the cryptographic foundations of Cloud Computing (CC), exposing distributed infrastructures to novel attack vectors. This survey provides comprehensive analysis of quantum-safe cloud security, examining vulnerabilities, transition strategies, and layer-specific countermeasures across nine architectural layers (application, data, runtime, middleware, OS, virtualization, server, storage, networking). We employ STRIDE-based risk assessment aligned with NIST SP 800-30 to evaluate quantum threats through three transition phases: pre-transition (classical cryptography vulnerabilities), hybrid (migration risks), and post-transition (PQC implementation weaknesses including side-channel attacks). Our security framework integrates hybrid cryptographic strategies (algorithmic combiners, dual/composite certificates, protocol-level migration), cryptographic agility, and risk-prioritized mitigation tailored to cloud environments. We benchmark NIST-standardized PQC algorithms for performance and deployment suitability, assess side-channel and implementation vulnerabilities, and analyze quantum-safe strategies from leading CSPs (AWS, Azure, GCP). The survey delivers layer-specific threat taxonomies, likelihood-impact risk matrices, and CSP-informed deployment roadmaps for cloud architects, policymakers, and researchers. We identify six critical research directions: standardization and interoperability, hardware acceleration and performance optimization, AI-enhanced security and threat mitigation, integration with emerging cloud technologies, systemic preparedness and workforce development, and migration frameworks with crypto-agility.

cs.CR↗

arXiv2025

Packed Malware Detection Using Grayscale Binary-to-Image Representations

Detecting packed executables is a critical step in malware analysis, as packing obscures the original code and complicates static inspection. This study evaluates both classical feature-based methods and deep learning approaches that transform binary executables into visual representations, specifically, grayscale byte plots, and employ convolutional neural networks (CNNs) for automated classification of packed and non-packed binaries. A diverse dataset of benign and malicious Portable Executable (PE) files, packed using various commercial and open-source packers, was curated to capture a broad spectrum of packing transformations and obfuscation techniques. Classical models using handcrafted Gabor jet features achieved intense discrimination at moderate computational cost. In contrast, CNNs based on VGG16 and DenseNet121 significantly outperformed them, achieving high detection performance with well-balanced precision, recall, and F1-scores. DenseNet121 demonstrated slightly higher precision and lower false positive rates, whereas VGG16 achieved marginally higher recall, indicating complementary strengths for practical deployment. Evaluation against unknown packers confirmed robust generalization, demonstrating that grayscale byte-plot representations combined with deep learning provide a useful and reliable approach for early detection of packed malware, enhancing malware analysis pipelines and supporting automated antivirus inspection.

cs.CR↗

arXiv2011

A Survey on Web-based AR Applications

Due to the increase of interest in Augmented Reality (AR), the potential uses of AR are increasing also. It can benefit the user in various fields such as education, business, medicine, and other. Augmented Reality supports the real environment with synthetic environment to give more details and meaning to the objects in the real word. AR refers to a situation in which the goal is to supplement a user's perception of the real-world through the addition of virtual objects. This paper is an attempt to make a survey of web-based Augmented Reality applications and make a comparison among them.

cs.MM↗

arXiv2010

A Wide range Survey on Recall Based Graphical User Authentications Algorithms Based on ISO and Attack Patterns

Nowadays, user authentication is one of the important topics in information security. Text based strong password schemes could provide with certain degree of security. However, the fact that strong passwords being difficult to memorize often leads their owners to write them down on papers or even save them in a computer file. Graphical user authentication (GUA) has been proposed as a possible alternative solution to text based authentication, motivated particularly by the fact that humans can remember images better than text. In recent years, many networks, computer systems and Internet based environments try used GUA technique for their users authentication. All of GUA algorithms have two different aspects which are usability and security. Unfortunately, none of graphical algorithms were being able to cover both of these aspects at the same time. This paper presents a wide range survey on the pure and cued recall based algorithms in GUA, based on ISO standards for usability and attack patterns standards for security. After explain usability ISO standards and attack patterns international standards, we try to collect the major attributes of usability and security in GUA. Finally, try to make comparison tables among all recall based algorithms based on usability attributes and attack patterns those we found.

cs.CR↗

arXiv2009

Shoulder Surfing attack in graphical password authentication

Information and computer security is supported largely by passwords which are the principle part of the authentication process. The most common computer authentication method is to use alphanumerical username and password which has significant drawbacks. To overcome the vulnerabilities of traditional methods, visual or graphical password schemes have been developed as possible alternative solutions to text based scheme. A potential drawback of graphical password schemes is that they are more vulnerable to shoulder surfing than conventional alphanumeric text passwords. When users input their passwords in a public place, they may be at risk of attackers stealing their password. An attacker can capture a password by direct observation or by recording the individuals authentication session. This is referred to as shouldersurfing and is a known risk, of special concern when authenticating in public places. In this paper we will present a survey on graphical password schemes from 2005 till 2009 which are proposed to be resistant against shoulder surfing attacks.

cs.CR↗