Journal of Network and Systems Management最新文献

The Internet of Things (IoT) is omnipresent, exposing a large number of devices that often lack security controls to the public Internet. In the modern world, many everyday processes depend on these devices, and their service outage could lead to catastrophic consequences. There are many Deep Packet Inspection (DPI) based intrusion detection systems (IDS). However, their linear computational complexity induced by the event-driven nature poses a power-demanding obstacle in resource-constrained IoT environments. In this paper, we shift away from the traditional IDS as we introduce a novel and lightweight framework, relying on a time-driven algorithm to detect Distributed Denial of Service (DDoS) attacks by employing Machine Learning (ML) algorithms leveraging the newly engineered features containing system and network utilization information. These features are periodically generated, and there are only ten of them, resulting in a low and constant algorithmic complexity. Moreover, we leverage IoT-specific patterns to detect malicious traffic as we argue that each Denial of Service (DoS) attack leaves a unique fingerprint in the proposed set of features. We construct a dataset by launching some of the most prevalent DoS attacks against an IoT device, and we demonstrate the effectiveness of our approach with high accuracy. The results show that standalone IoT devices can detect and classify DoS and, therefore, arguably, DDoS attacks against them at a low computational cost with a deterministic delay.

Competing service providers in the cloud environment ensure services are delivered under the promised security requirements. It is crucial for mobile services where user’s movement results in the service’s migration between edge servers or clouds in the Continuum. Maintaining service sovereignty before, during, and after the migration is a real challenge, especially when the service provider has committed to ensuring its quality following the Service Level Agreement. In this paper, we present the main challenges mobile service providers face in a cloud environment to guarantee the required level of security and digital sovereignty as described in the Security Service Level Agreement, with emphasis on challenges resulting from the service migration between the old and new locations. We present the security and sovereignty context intended for migration and the steps of the migration algorithm. We also analyze three specific service migration cases for three vertical industries with different service quality requirements.

Integrating Software-Defined Networking (SDN) with the Internet of Things (IoT) simplifies the management of IoT devices; however, it introduces security challenges. Adversaries may manipulate forwarding rules to redirect communication, compromising user security. Additionally, the centralized nature of SDN-enabled IoT networks poses a single point of failure during master controller failure. To address these issues, we present SDBlock-IoT, a distributed SDN architecture based on blockchain technology. This ensures increased resiliency in the event of master controller failure. Our proposed model considers response time and resource utilization of equal controllers, ensuring the most suitable controller assumes the role of master controller. We enhance the integrity of OpenFlow forwarding rules through the Smart Agent and SC, which validate whether a flow is registered on the blockchain or not. The Smart Agent verifies forwarding rules for every new flow request. We conducted experiments on hardware SDN switches using a Ryu OpenFlow controller and a private blockchain, demonstrating the effectiveness of our approach. Evaluation results indicate that SDBlock-IoT outperforms existing solutions in terms of flow verification time, controller recovery time, CPU utilization, and transaction costs.

Proprietary communication technologies for time-critical communication in industrial environments are being gradually replaced by Time-sensitive Networking (TSN)-enabled Ethernet. Furthermore, attempts have been made to bring TSN features into wireless networks so that the flexibility of wireless networks can be utilized, and the end-to-end timings for Time-Triggered (TT) streams can be guaranteed. Given a mixed wired-wireless network, the scheduling problem should be solved for a set of TT stream requests. In this paper, we formulate the no-wait scheduling problem for mixed wired-wireless networks as a Mixed Integer Linear Programming (MILP) model with the objective of minimizing the flowspan. We also propose a relaxation of the original MILP in the form of a 2-stage MILP formulation. Next, a scalable approach based on the greedy heuristic is proposed to solve the problem for realistic-size networks. Evaluation results show that the greedy heuristic is suitable for realistic problem sizes where the MILP-based approach is found to be practically infeasible. Furthermore, the impact of wireless requests on the performance of the greedy heuristic is reported.

In Agrifood scenarios, where farmers need to ensure that their produce is safely produced, transported, and stored, they rely on a network of IoT devices to monitor conditions such as temperature and humidity throughout the supply chain. However, managing this large-scale IoT environment poses significant challenges, including transparency, traceability, data tampering, and accountability. Blockchain is portrayed as a technology capable of solving the problems of transparency, traceability, data tampering, and accountability, which are key issues in the AgriFood supply chain. Nonetheless, there are challenges related to managing a large-scale IoT environment using the current security, authentication, and access control solutions. To address these issues, we introduce an architecture in which IoT devices record data and store them in the participant’s cloud after validation by endorsing peers following an attribute-based access control (ABAC) policy. This policy allows IoT device owners to specify the physical quantities, value ranges, time periods, and types of data that each device is permitted to measure and transmit. Authorized users can access this data under the ABAC policy contract. Our solution demonstrates efficiency, with 50% of IoT data write requests completed in less than 0.14 s using solo ordering service and 2.5 s with raft ordering service. Data retrieval shows an average latency between 0.34 and 0.57 s and a throughput ranging from 124.8 to 9.9 Transactions Per Second (TPS) for data sizes between 8 and 512 kilobytes. This architecture not only enhances the management of IoT environments in the AgriFood supply chain but also ensures data privacy and security.

Smart contracts are decentralized applications that hold a pivotal role in blockchain-based systems. Smart contracts are composed of error-prone programming languages, so it is affected by many vulnerabilities (e.g., time dependence, outdated version, etc.), which can result in a substantial economic loss within the blockchain ecosystem. Therefore, many vulnerability detection tools are designed to detect the vulnerabilities in smart contracts such as Slither, Mythrill and so forth. However, these tools require high processing time and cannot achieve good accuracy with complex smart contracts nowadays. Consequently, many studies have shifted towards using Deep Learning (DL) techniques, which consider bytecode to determine vulnerabilities in smart contracts. However, these mechanisms reveal three main limitations. First, these mechanisms focus on multi-class problems, assuming that a given smart contract contains only a single vulnerability while the smart contract can contain more than one vulnerability. Second, these approaches encounter ineffective word embedding with large input sequences. Third, the learning model in these mechanisms is forced to classify into one of pre-defined labels even when it cannot make decisions accurately, leading to misclassifications. Therefore, in this paper, we propose a multi-label vulnerability classification mechanism using a language model. To deal with the ineffective word embedding, the proposed mechanism not only takes into account the implicit features derived from the language models (e.g., SecBERT, etc.) but also auxiliary features extracted from other word embedding techniques (e.g., TF-IDF, etc.). Besides, a trustworthy neural network model is proposed to reduce the misclassification rate of vulnerability classification. In detail, an additional neuron is added to the output of the model to indicate whether the model is able to make decisions accurately or not. The experimental results illustrate that the trustworthy model outperforms benchmarks (e.g., binary relevance, label powerset, classifier chain, etc.), achieving up to approximately 98% f1-score while requiring low execution time with 26 ms.

In this work, we investigate Reconfigurable Intelligent Surface (RIS)-aided Full-Duplex (FD)-Simultaneous Wireless Information Power Transfer (SWIPT)-Cooperative non-Orthogonal Multiple Access (C-NOMA) consisting of two paired devices. The device with better channel conditions ((D_1)) is designated to act as a FD relay to assist the device with poor channel conditions ((D_2)). We assume that (D_1) does not use its own battery energy to cooperate but harvests energy by utilizing SWIPT. A practical non-linear Energy Harvesting (EH) model is considered. We first approximate the harvested power as a Gamma Random Variable (RV) via the Moment Matching (MM) technique. This allows us to derive analytical expressions for Outage Probability (OP) and ergodic rate (ER) that are simple to compute yet accurate for a wide range of system parameters, such as EH coefficients and residual Self-Interference (SI) levels, being extensively validated by numerical simulations. The OP and ER expressions reveal how important it is to mitigate the SI in the FD relay mode since, for reasonable values of residual SI coefficient, its detrimental effect on the system performance, is extremely noticeable. Also, numerical results reveal that increasing the number of RIS elements can benefit the cooperative system much more than the non-cooperative one.

Software-defined networks (SDN) rely on flow tables to forward packets from different flows with different policies. To speed up packet forwarding, the rules in the flow table should reside in the forwarding plane as much as possible to reduce the chances of consulting the SDN controller, which is a slow process. The rules are usually cached in the forwarding plane with a Ternary Content Addressable Memory (TCAM) device. However, a TCAM has limited capacity, because it is expensive and power-hungry. As a result, wise caching of a subset of flow rules in TCAM is needed. In this paper, we address two related issues that affect caching efficiency: rules to be cached and rules to be replaced. For the first issue, caching an active rule hit by a flow may need to cache inactive rules due to rule dependency. We propose a two-stage caching architecture called CRAFT, which reduces inactive rules in cache by cutting down long dependent chains and by partitioning rules with massive dependent rules into non-overlapping sub-rules. For the second issue, unawareness of the flow traffic characteristics may evict heavy hitters instead of mice flows. We propose RRTC to address this issue, which is a rule replacement policy taking the real-time network traffic characteristics into consideration. By recognizing the heavy hitters and protecting their matching rules in TCAM, RRTC performs better than least recently used(LRU) policy in terms of cache hit ratio. Simulation results show that our combined rule caching and replacement framework outperforms previous work considerably.

This work describes an approach to enhance container orchestration platforms with an autonomous and dynamic rescheduling system that aims at improving application service time by co-locating highly interdependent containers for network delay reduction. Unreasonable container consolidation may however lead to host CPU saturation, in turn impairing the service time. The multiobjective approach proposed in this work aims to improve application service-time by minimizing both inter-server network traffic and CPU throttling on overloaded servers. To this extent, the Simulated Annealing combinatorial optimization heuristic is used and compared on its relative performance towards the optimal solution obtained by Mathematical Programming. Additionally, the impact of the proposed system is validated on a Kubernetes cluster hosting three concurrent applications, and this under varying load scenarios. The proposed rescheduling system systematically i) improves the application service-time (up to 27.2% from our experiments) and ii) surpasses the improvement reached by the Kubernetes descheduler.

Large Language Models (LLM) continue to demonstrate their utility in a variety of emergent capabilities in different fields. An area that could benefit from effective language understanding in cybersecurity is the analysis of log files. This work explores LLMs with different architectures (BERT, RoBERTa, DistilRoBERTa, GPT-2, and GPT-Neo) that are benchmarked for their capacity to better analyze application and system log files for security. Specifically, 60 fine-tuned language models for log analysis are deployed and benchmarked. The resulting models demonstrate that they can be used to perform log analysis effectively with fine-tuning being particularly important for appropriate domain adaptation to specific log types. The best-performing fine-tuned sequence classification model (DistilRoBERTa) outperforms the current state-of-the-art; with an average F1-Score of 0.998 across six datasets from both web application and system log sources. To achieve this, we propose and implement a new experimentation pipeline (LLM4Sec) which leverages LLMs for log analysis experimentation, evaluation, and analysis.