Delay-Tolerant Networking (DTN) enables communication across intermittently connected environments such as space systems, remote IoT infrastructures, and disaster-response networks. However, DTNs remain highly vulnerable to routing-based blackhole attacks, where malicious nodes attract and drop bundles, severely degrading end-to-end reliability. This paper presents a dual-mode secure DTN architecture that integrates Software-Defined Networking (SDN) with Machine Learning (ML) and Federated Learning (FL) for both reactive and pre-emptive intrusion detection. Reactive detection employs ML and FL classifiers on DTN-aware indicators such as bundle delivery ratio, hop anomalies, and delay variance to identify active attacks. Pre-emptive detection uses authenticated canary probes with hash-based message authentication code (HMAC) tags and exponentially weighted moving average (EWMA) timeouts to forecast forwarding inconsistencies before delivery collapse. Our DTN-SHIELD framework unifies SDN’s global control-plane visibility with FL’s privacy-preserving distributed learning, enabling the controller and edge nodes to jointly anticipate routing anomalies and deliver pre-emptive blackhole detection even under the partial visibility conditions inherent to DTNs. Experimental evaluation on a hybrid SDN–DTN testbed shows that in reactive mode, a centralized Gated Recurrent Unit (GRU) achieves 92% accuracy, while the federated variant maintains 93%. In pre-emptive mode, a centralized Random Forest (RF) attains 97% accuracy, whereas the federated Multi-Layer Perceptron (MLP) configuration reaches 87% under non-IID data distribution. Results demonstrate that combining SDN programmability with reactive analytics and pre-emptive FL forecasting provides a scalable, privacy-preserving, and resilient defense against blackhole intrusions in next-generation DTN deployments. Beyond the immediate DTN setting, this integration of programmable control and federated intelligence lays the groundwork for secure telemetry exchange and distributed anomaly detection across emerging delay-tolerant and future interplanetary Internet architectures.

The proliferation of Internet of Things (IoT) devices across multiple domains has heralded an era of unprecedented connectivity and data exchange. Fog computing enhances edge-network processing, enabling real-time data analysis and prompt responses. However, ensuring secure and trustworthy communication among these devices remains a paramount concern. In fog-enabled IoT environments, securing communication among users, IoT devices, gateways, and fog nodes is of paramount importance to prevent unauthorized access and ensure data integrity and confidentiality. Additionally, users can control and deliver instructions to IoT devices remotely. Hence, we propose a blockchain-assisted authentication protocol tailored specifically for fog-enabled IoT environments to verify the user’s identity prior to accessing the IoT devices. The proposed protocol leverages cutting-edge crypto primitives like elliptic curve cryptography, hash functions, and blockchain to establish secure communication between users and IoT devices. Furthermore, we evaluate the proposed scheme through formal (Scyther) and informal analysis, demonstrating its efficacy in mitigating well-known attacks. On the other hand, the proposed protocol exhibits robustness against relevant protocols in terms of communication and computational aspects, as well as reliability for real-world fog-enabled IoT applications.

Integrating advanced communication technologies has significantly enhanced power distribution efficiency, reliability, and sustainability in the evolving landscape of smart microgrids. However, this integration introduces substantial security challenges, particularly concerning authentication and critical agreement processes, which are essential for maintaining the integrity and confidentiality of smart grid communications. This paper presents a novel authentication and key agreement protocol specifically designed for the smart grid environment by incorporating advanced cryptographic techniques such as ECC, physical unclonable functions, and blockchain. Our protocol ensures mutual authentication between devices, robust key management, and resistance to prevalent security threats. We conduct a comprehensive security analysis and performance evaluation, demonstrating that our protocol enhances security and maintains the efficiency necessary for practical deployment in smart grids. The results indicate significant improvements in security and performance metrics compared to existing solutions, establishing our protocol as a viable and effective option for securing smart grid communications. • Blockchain-based key management ensures scalable, resilient data exchange. • Novel ECC- and PUF-driven protocol enables secure smart microgrid authentication. • Protocol achieves mutual authentication, session secrecy, and forward secrecy. • Resistant to replay, man-in-the-middle, cloning, and physical attacks. • Supports lightweight operations suitable for real-time smart energy systems.

Network slicing plays a crucial role in supporting Fifth Generation (5G) mobile network, which is designed to efficiently accommodate a diverse range of services with varying service level requirements. In this work, our efforts are largely aimed at exposing security flaws in 5G network slicing from a Distributed Denial of Service (DDoS) attack perspective. Time consuming authentication process during the inter-slice handover procedure is exploited to launch a DDoS attack. To address this issue, we offer novel attack detection and localization algorithms. We have compared results for various combinations of average waiting time and average switching rate to detect the attack and localize compromised user equipments. As per experimentation results, our approach resulted in an accuracy of 91% for detecting an attack and 96% for identifying compromised users.

Distributed Ledger Technologies (DLT) are based on the Blockchain concept and have been specifically designed for enterprise-level devices with acceptable computing powers and network bandwidth. Direct Acyclic Graph (DAG) ledger(s) is a new form of DLT technology designed for Internet-of- Things (IoT) devices due to the nature of its disadvantages of the computing powers and limited network bandwidth. IOTA is a DAG-based Blockchain implementation for IoT applications that has gained an increased attention in recent years. One of the major concerns that is hindering for its wide adaptation is the security concerns. Many security attack occurrences against the IOTA such as parasite attacks, double spending, and DDoS to disrupt availability resources of the new ledger can become both widespread and disruptive. Existing security studies are ad-hoc and typically address a solution scheme for a specific security threat. In this paper, we present an adaptive Reinforcement-Learning (RL) approach to best classify the monitored resource consumption parameters of the DAG-based nodes or devices for any potential security anomaly detection. The aim is to create high accuracy security threat index that can be used to proactively defend the decentralized IOTA infrastructure and individual nodes against compromises. The performance evaluation results of this solution against DoS attacks are promising. The framework implementation derives a stochastic interpretation and output and the same time it converges deterministically.

Disasters require quick response times, thought-out preparations, overall community, and government support to ensure the prevention of loss of life and reduce possible damages. Hurricane Irma can be recognized as a more popular recent disaster in terms of social media attention and made landfall in the US with significant time to prepare, making it a good model for an evaluation of disaster response. The objective of this research is to establish a pattern regarding sentiment trends over the progression of the storm totality using sentiment analysis and produce a viable set of topic models for its and Latent Dirichlet Allocation (LDA) topic modeling. The results from this study demonstrate that sentiment analysis can measure changes in users's emotions during natural disasters and that simple topic models can be formed from the twitter data. Information like this can be used by authorities to limit the damage and effectively recover from the disaster as well as adjust future response efforts accordingly. This research can be further improved by incorporating sentiment analysis methods for short texts, classifying emoticons and non-textual components such as videos or images, and optimizing data collection and preparation methods.

Cloud data center traffic is experiencing a rapid growth as more and more data-intensive applications are required to process big data in a cloud data center. Although fat-tree networks own rich path multiplicity, and have been widely adopted as network topologies in cloud data center networks to transmit vast bisection bandwidth, they lead to bandwidth-related bottlenecks. To address this issue, in this paper, we propose a traffic-aware virtual cluster reallocation approach via biogeography-based optimization to allocate some reallocated virtual machines (VMs) as compact as possible with those VMs in the same virtual clusters. In order to validate our approach, we build a system model to perform a thorough evaluation of its performance. Experimental results show that our proposed approach outperforms six existed approaches in term of total transmission cost, total processing time, and total network resource consumption.

The large-scale utilization of cloud computing services for hosting industrial/enterprise applications has led to the emergence of cloud service reliability as an important issue for both cloud service providers and users. To enhance cloud service reliability, two types of fault tolerance schemes, reactive and proactive, have been proposed. Existing schemes rarely consider the problem of coordination among multiple virtual machines (VMs) that jointly complete a parallel application. Without VM coordination, the parallel application execution results will be incorrect. To overcome this problem, we first propose an initial virtual cluster allocation algorithm according to the VM characteristics to reduce the total network resource consumption and total energy consumption in the data center. Then, we model CPU temperature to anticipate a deteriorating physical machine (PM). We migrate VMs from a detected deteriorating PM to some optimal PMs. Finally, the selection of the optimal target PMs is modeled as an optimization problem that is solved using an improved particle swarm optimization algorithm. We evaluate our approach against five related approaches in terms of the overall transmission overhead, overall network resource consumption, and total execution time while executing a set of parallel applications. Experimental results demonstrate the efficiency and effectiveness of our approach.

The discipline of Homeland Security is gaining wider traction especially after the horrendous attack on the world trade center, the USA in 2001. Recently national governments are very seriously and sincerely putting a lot of emphasis and efforts on national security aspects that implicitly cover the safety and security of people, infrastructures, and resources. It is overwhelmingly acknowledged that Information and Communication Technology (ICT) is the best fit and the route for effectively scavenging, sensitizing and securing the various mission and life-critical sources and resources of the continents, countries, counties, and cities. In this paper, we would like to insist how the emerging and evolving concept of cloud computing will effectively safeguard and seal the security of nations and their occupants, constituents, and participants. In this paper, we have contributed with a description of homeland security services that can be designed, built and hosted on public clouds. We have designed a flexible framework for the cloud-based service development, deployment, and delivery platform, especially for homeland security. As services are being implemented in the cloud environment, the availability and accessibility get comprehensively easy and ensured for worldwide developers to come out with better, leaner, and adaptive homeland security applications.

In this paper, we present a vulnerability assessment framework that could be used to assess and prevent cyber threats related to wired and wireless networks and computer systems. We have performed vulnerability assessment tests for aviation systems including data loaders and in order to meet aviation industry requirements for wireless network security. Our contributions include detecting cyber vulnerabilities in these aviation systems by using vulnerability assessment and penetration testing tools such as Metasploit Pro and BackTrack and improving security and safety of aircraft. Based on our test results of cyber vulnerabilities, the corresponding solutions will be developed to fix these vulnerabilities. New vulnerability assessment tests will be conducted again until our solutions are secure and safe to use. Some results of our vulnerability assessment tests against our software-hardware products are presented.