Journal of Network Security Computer Networks

MAIR-Suite: An Automated Multi-Vector Malware Analysis, Threat Classification, and Incident Response Framework

Navya S T, Surya — Fri, 23 Oct 2026 00:00:00 +0000

Malware investigation often requires evidence from several sources, including file characteristics, signature matches, runtime behavior, and volatile memory, yet these findings are commonly produced by separate tools and are difficult to correlate. This paper proposes MAIR-Suite, an automated multi-vector malware analysis, threat classification, and incident response framework that integrates static analysis, YARA scanning, controlled dynamic behavior analysis, and Volatility 3-based memory forensics in a single workflow. Findings are normalized and correlated as indicators of compromise, including hashes, URLs, IP addresses, process information, and memory artifacts, and are then passed to a threat-scoring stage and a proposed Random Forest classifier that categorizes samples as BENIGN, SUSPICIOUS, or MALICIOUS. Observed activities are mapped to MITRE ATT&CK techniques to provide a standardized interpretation of behavior, and structured HTML and PDF incident reports are generated for investigator review. The paper presents the problem statement, literature review, methodology, system architecture, and evaluation plan for the proposed design. As the work describes a proposed system rather than a completed implementation, no experimental accuracy or performance results are claimed.

A study on Accelerating Secure IoT Development With Embedded Threat Modeling

Kazi Kutubuddin Sayyad Liyakat, Heena T Shaikh — Mon, 21 Sep 2026 00:00:00 +0000

The rapid proliferation of Internet of Things (IoT) deployments has shifted the security burden from centralized gateways to the far-flung edge nodes that execute mission-critical code. While a wealth of IoT-specific Integrated Development Environments (IDEs) now promise streamlined device provisioning, data model generation, and over-the-air updates, they rarely expose the security ramifications of the code they help craft. This paper presents a novel, security-by-design IDE prototype, SecureNode Studio, that embeds node-centric threat modeling, static analysis, and runtime hardening directly into the developer workflow. By coupling a domain-specific language (DSL) for secure communication primitives with an extensible rule engine that draws on the latest CVE feeds, the IDE autonomously flags insecure patterns (e.g., hard-coded credentials, unchecked buffer handling, improper TLS termination) as the programmer types. A series of controlled experiments with 48 developers across three industry sectors reveals a 42 % reduction in introduced vulnerabilities and a 27 % improvement in time to patch when compared with a conventional IoT IDE. Moreover, the IDE’s “security sandbox” emulates the constrained resources of typical sensor nodes, allowing developers to observe the performance impact of hardening measures (e.g., memory-safe allocators, cryptographic offload) before deployment. The contribution of this work is threefold: (1) a concrete design pattern for integrating security feedback loops into IoT development tools; (2) an open‑source implementation that can be retrofitted onto existing IDEs via plug-ins; and (3) empirical evidence that such integration materially raises the security posture of edge node software without sacrificing developer productivity.

Adaptive Privacy-Compliant XAI Dashboards for Role-Aware Forensic Decision Support

Debalina Sinha Jana, Kaushik Sinha — Mon, 05 Oct 2026 00:00:00 +0000

The article proposes a privacy-compliant Explainable Artificial Intelligence (XAI) dashboard for forensic decision support, addressing the critical need for transparent yet secure AI-driven analysis in sensitive legal contexts. The proposed system dynamically adapts explanations to user roles while enforcing differential privacy and workflow compliance, bridging the gap between interpretability and regulatory constraints. At its core, an Adaptive Differential Privacy (ADP) Engine injects noise into feature attributions based on sensitivity, ensuring GDPR-aligned data protection without compromising explanatory utility. Moreover, a Role-Based Explanation Renderer (RBER) tailors outputs to distinct stakeholders, such as junior analysts receiving anonymized insights or legal auditors obtaining natural language summaries with jurisdictional precedents. A Compliance-Aware Workflow Orchestrator further governs data flow through RBAC policies and chain-of-custody rules, enabling auditable transitions across forensic stages. The architecture integrates with conventional AI pipelines, intercepting raw explanations from SHAP or LIME modules and transforming them into role-aware visualizations. Implemented with Rényi differential privacy for tighter bounds and Kafka-based event streaming for auditability, the system advances state-of-the-art XAI dashboards by unifying privacy, explainability, and workflow rigor. Experiments on forensic datasets demonstrate its efficacy in balancing interpretability with compliance, offering a scalable template for high-stakes decision support systems.

Digital Twins and the Future of Connected Home Appliances

Hitesh Mohapatra — Thu, 15 Oct 2026 00:00:00 +0000

The work centers on the application and importance of digital twins in smart home appliances for monitoring devices used daily in a person’s day-to-day life. This provides an easy way to keep up with the fast-paced world by limiting monitoring and one-on-one interaction with all the devices and electricity bills, including their timely replacement and servicing. With digital twins, a person can access all the information and details about connected appliances at their fingertips in one place, saving time and effort. It highlights the integration of IoT and digital twins, which aids in real-time monitoring with the help of sensors. With the help of cybersecurity and blockchain algorithms, data integrity and security have been ensured to prevent the leakage of essential data. Apart from all the positive sides and advantages, some gaps have also been identified, and accordingly, solutions have been proposed. The integration of digital twin technology in smart home appliances represents a promising avenue for enhancing efficiency, functionality, and user experience. Through elucidating the potential benefits and addressing pertinent issues, this paper aims to contribute to the advancement of smart home technologies and facilitate informed decision-making for stakeholders in the digital twin ecosystem.