Information Security Journal: A Global Perspective aims to inform security professionals about new technologies, useful tools, and relevant research in policy and technology. We also seek to provide insight into current and existing professional development issues.    We welcome submissions that cover the following topics:        Security Access Control Systems and Security Methodology      Security Telecommunications and Network Security      Business Continuity Planning and Disaster Recovery Planning      Security Management Practices      Security Architecture and Models      Law, Investigation, and Ethics in Information Security      Application and Systems Development Security      Cryptography security      Computer Operations Security      Physical Security       We are especially keen to receive manuscripts that explore Cloud Security, Health Care Information Security and Privacy and Cyber Forensics

The Information Security Journal: A Global Perspective aims to inform security professionals about new technologies, useful tools, and relevant research in policy and technology. We also seek to provide insights into current and existing professional development issues. We welcome submissions covering the following topics: Security Access Control Systems and Security Methodology, Security Telecommunications and Network Security, Business Continuity Planning and Disaster Recovery Planning, Security Management Practices, Security Architecture and Models, Law, Investigation, and Ethics in Information Security, Application and Systems Development Security, Cryptography Security, Computer Operations Security, and Physical Security. We are particularly keen to receive manuscripts that explore Cloud Security, Health Care Information Security and Privacy, and Cyber Forensics.

《Information Security Journal》旨在为安全专业人士提供有关新技术、有用工具和相关政策与技术研究的信息。我们还希望深入探讨当前和现有的职业发展问题。我们欢迎涵盖以下主题的投稿：安全访问控制系统与安全方法论、安全电信与网络安全、业务连续性规划与灾难恢复规划、安全管理实践、安全架构与模型、信息安全中的法律、调查与伦理、应用与系统开发安全、密码学安全、计算机操作安全和物理安全。我们特别希望收到探讨云安全、医疗信息安全与隐私以及网络取证的稿件。

Information Security Journal

Inventory management in network infrastructures currently relies on static methods, leading to data consistency issues due to the difficulty of keeping static reports and lists up to date with the dynamic nature of real-world networks. An alternative approach is the use of automatically generated network topology diagrams, which provide a visual representation of the network's structure and organization, depicting the configuration and interconnections of nodes and links. These diagrams serve purposes such as network planning, management, and security management. However, the creation of automatic network topology diagrams remains a challenge. This paper addresses this challenge by proposing a Reinforcement Learning-based approach that utilizes network traffic data to automatically generate network topology diagrams. The preliminary results demonstrate promising outcomes, enabling the automatic generation of efficient network topology diagrams for various purposes, even in the case of large-scale networks. These automatically created diagrams can further facilitate security analysis, planning, and optimization activities. By focusing on enhancing security outcomes and enabling robust network analysis, this research contributes to advancing the field of network management, particularly in the domain of automatic network visualization and comprehensive security analysis.

Ferda Özdemir Sönmez

Chris Hankin

Pasquale Malacaria

Information Security Journal A Global Perspective

Smart topology detection using multi-armed bandit reinforcement learning method

使用多臂 bandit 强化学习方法的智能拓扑检测

Ennaji Elmahfoud

Salah El Hajla

Yassine Maleh

Soufyane Mounir

Karim Ouazzane

Label flipping attacks in hierarchical federated learning for intrusion detection in IoT

分层联邦学习中用于物联网入侵检测的标签翻转攻击

Nowadays, e-mail is becoming more prevalent among individuals. In recent days, it has been declared to be the least expensive and quickest mode of communicating. In recent years, e-mail spam has become a big problem, so the number of e-mail spam has also increased, and they are used for unethical and illegal conduct, scams, and theft. As a result, the main objective of the study is to detect e-mail spam using an adaptive deep-learning model with feature fusion. Initially, the input image and text are collected from benchmark datasets. Further, the raw text undergoes the pre-processing stage, and it is then followed by extracting the features using Bidirectional Encoder Representations from Transformers (BERT). Similarly, the Vision Transformer (ViT) is employed for extracting the features from raw images. Finally, these features are given in Dual Scale Feature Fusion-based Adaptive Convolutional Neural Network (DFF-ACNNet), and it is then fused for performing the classification in Convolutional Neural Network (CNN). Here, the hyper-parameters are tuned by using Modified Squid Game Optimizer (MSGO). Finally, the evaluation is done with various metrics to estimate the performance of the model and the accuracy of the developed system is 98.46, which is higher than the other conventional approaches.

Vikas S. Kadam

Subhash Pingale

Sangappa R. Biradar

Vandana M. Rohokale

Kalyan D. Bamane

Designing a novel framework of email spam detection using an improved heuristic algorithm and dual-scale feature fusion-based adaptive convolution neural network

设计一种基于改进启发式算法和双尺度特征融合的自适应卷积神经网络的电子邮件垃圾邮件检测新框架

Mohamed Haddadi

Abdelhamid Khiat

Hadil Bouaoud

Hadjer Djehiche

SPGDAD: Slow HTTP-Get denial of service attack detection using ontology

SPGDAD：基于本体的慢速HTTP-GET拒绝服务攻击检测

Daniel Ramalho

André Diegues Fernandes

Miguel Mira da Silva

Rúben Pereira

Digital risk: a systematic multivocal literature review

数字风险：一项系统性多视角文献综述

Deepali Bankatsingh Gothawal

S.V. Nagaraj

Hybrid secure routing and monitoring mechanisms in IoT-based wireless sensor networks using Egret-Harris optimization

基于物联网的无线传感器网络中使用白鹭-哈里斯优化的混合安全路由与监控机制

Abbreviation	Inf. Secur. J.: Global Perspect.
Journal Impact	1.54
Quartiles(Global)	COMPUTER SCIENCE, INFORMATION SYSTEMS(Q3)

ISSN	1939-3547, 1939-3555
h-index	32

Publication Information	Publisher: Taylor and Francis Ltd.，Publishing cycle: ，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 2008，Proportion of original research papers: 93.10%，Self Citation Rate:6.20%， Gold OA Rate: 5.50%

Information Security Journal

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Smart topology detection using multi-armed bandit reinforcement learning method

Label flipping attacks in hierarchical federated learning for intrusion detection in IoT

Designing a novel framework of email spam detection using an improved heuristic algorithm and dual-scale feature fusion-based adaptive convolution neural network

SPGDAD: Slow HTTP-Get denial of service attack detection using ontology

Digital risk: a systematic multivocal literature review

Smart topology detection using multi-armed bandit reinforcement learning method

Designing a novel framework of email spam detection using an improved heuristic algorithm and dual-scale feature fusion-based adaptive convolution neural network

Label flipping attacks in hierarchical federated learning for intrusion detection in IoT

Digital risk: a systematic multivocal literature review

Hybrid secure routing and monitoring mechanisms in IoT-based wireless sensor networks using Egret-Harris optimization