Despite the rapid adoption of Large Language Models (LLMs) for automatic code generation, their output often exhibits syntax errors, security vulnerabilities, and functional inconsistencies. To address these issues, we present CodeEnhancer, a two-stage framework that tightly integrates LLMs with static application security testing (SAST) tools and targeted fine-tuning. The goal is to produce more secure and functionally correct Python code. In the first stage, our iterative validation pipeline couples LLM-generated code with tools such as Pylint and Bandit. These tools automatically identify and remediate issues through structured feedback loops. When applied to the GPT-4o model, this process eliminated 82.8% of the initial vulnerabilities and resolved all the detected functional correctness issues when tested on the LLMSecEval dataset. In the second stage, we fine-tune the LLMs using two types of secure code examples: expert-written samples and code refined by our framework. Comparative experiments demonstrate that the framework-tuned model outperforms the baseline and expert-tuned models. The framework-tuned model generates only 18.4% vulnerable code snippets on the LLMSecEval dataset, whereas the baseline and expert-tuned models produce 43.6% and 54.7% vulnerable code snippets, respectively. The framework-tuned model reduces final vulnerability rates to 6.7% on LLMSecEval and 3.5% on the SecurityEval dataset. Our results highlight the synergistic effect of integrating static analysis with feedback-informed fine-tuning. They also reveal limitations in current evaluation metrics and dataset representativeness. These findings suggest a scalable, robust approach to achieving more secure, trustworthy, and practical AI-assisted code generation. • Combines language models with SAST Tools to enhance Syntax, security and functional correctness Python code. • First approach to address syntax, security, and functional correctness in LLM-generated code. • Automated feedback and learning process helps LLMs generate more secure, correct code. • Fine-tuning on framework-refined code leads to better security than training on expert-written code. • Scalable approach enables robust and trustworthy AI-assisted code generation and refinement with minimal manual effort.

This paper presents a testbed for the ECHONET Lite Web API (ELWA), a protocol supporting cloud-to-cloud interoperability for IoT systems. The key innovation of the testbed lies in its ability to integrate IoT devices using any protocol or communication pattern through the introduction of an MQTT-based protocol binding and a token-based access management mechanism. This architecture enables a two-sided marketplace model, with the testbed server acting as a mediator between IoT device providers and service developers, realizing the testbed-as-a-service paradigm for the ELWA ecosystem. The proposed system has been implemented and deployed in a real-world environment with a variety of devices. Performance evaluations demonstrate the testbed’s scalability, reliably handling up to 3,000 concurrent requests per second, with minimal overhead introduced by protocol binding and access control mechanisms.

Multi-server wireless multihop networks (MWMNs) are critical for modern communication systems, enabling efficient data transmission between devices and servers. However, the complexity of determining optimal server selection and multihop path planning in such networks often results in high interference, high network latency, low network capacity, and reduced network performance. To address these challenges, this paper proposes a two-stage network optimization scheme for MWMNs, using Broad Learning System and Q-learning, called BLSQ. First, the Broad Learning System (BLS) is employed to allocate servers to devices based on their location and computational requirements. Second, a Q-learning algorithm is introduced to optimize multihop path selection, aiming to maximize network capacity while minimizing interference. The proposed approach is evaluated based on different path selection methods in extensive simulations. Results demonstrate that our method significantly reduces network interference, increases network capacity, and achieves lower transmission time, providing a possible approach for optimizing wireless in MWMNs.

Information sharing is a key practice in cybersecurity for coping with the ever-changing cyberattacks that are targeting computer systems. Thus, when cyber incidents happen, cyber threat intelligence (CTI) reports are prepared and shared among cybersecurity practitioners to help them get up-to-date information about those incidents. However, reading and analyzing the report text to comprehend the included information is a cumbersome process. Although techniques based on deep learning were proposed to speed up report analysis in order to obtain the enclosed essential information, such as attack path, training data insufficiency makes these methods inefficient in practical circumstances. This paper presents RAF-AG, a report analysis framework for attack path generation. To analyze CTI reports, RAF-AG utilizes the sentence dependency tree for entity and relation extraction, and a weak supervision approach for entity labeling. This is followed by graph building and graph alignment for generating the attack paths. Our approach resolves the data insufficiency problem in the cybersecurity domain by lowering the need for expert involvement. We evaluated RAF-AG by comparing the generated attack paths with those produced by AttacKG, a state-of-the-art automatic report analysis framework. RAF-AG was able to identify cyberattack steps by matching their appearance order inside the report, and link them with techniques from the MITRE ATT&CK knowledge base with an improved F1 score compared to AttacKG (0.708 versus 0.393).

A wireless multihop network (WMN) is set of wirelessly connected nodes without an aid of centralized infrastructure that can forward any packets via intermediate nodes by a multihop fashion. In the WMN, there are still some issues that need to be resolved, like due to any source node may choose an uncertainty path to send their packets through the multihop fashion and this leads to the performance of network capacity can degrade drastically. To solve this problem, in this research, we propose two novel path selection algorithms called SNR-based learning path selection (NLPS) algorithm and SINR-based learning path selection (INLPS) algorithm, which are incorporated with the deep reinforcement learning (DRL) to select the best multihop path from any source node to a destination node with highest end-to-end (E2E) throughput. Besides that, a factor graph (FG) approach and a nested lattice code (NLC) representation are used to reduce the computation time. According to the numerical studies with the NLC is applied, our simulation results reveal that the proposed NLPS and INLPS algorithms can improve the overall average network capacity up to 3.1 times and 10.5 times compared to FG, respectively. However, the overall average computation time are highly increased for NLPS and INLPS, i.e., about 0.627 s and 1.221 s, respectively compared to FG, which is about 0.006 s. In other words, both NLPS and INLPS algorithms can achieve high network capacity and moderate computation time.

Wireless Multihop Network (WMN) is set of wirelessly connected nodes without an aid of centralized infrastructure that can forward any message via relaying nodes by multihop fashion. In WMN, there are still some issues that need to be resolved, like due to the uncertainty of source node choosing a path to send the message and the nature of multihop fashion, the performance of network capacity can degrade drastically. To solve these problems, in this research we propose two novel path selection algorithms called SNR-based learning path selection (NLPS) algorithm and SINR-based learning path selection (INLPS) algorithm, which are incorporated with the deep reinforcement learning (DRL) to select the best multihop path from source node to destination node with highest endto-end throughput. Factor graph (FG) representation is used to reduce the computation time. Our simulation results reveal that both NLPS and INLPS can achieve high network capacity and moderate computation time. Meanwhile, nested lattice code (NLC) is used in compute-and-forward strategy to reduce the time slots. As a result, the network capacity can increase more.

Cybersecurity training is a key endeavour for ensuring that the IT workforce possess the knowledge and practical skills required to counter the ever-increasing cybersecurity threats that our society is faced with. While some related systems, such as Capture The Flag platforms, have been available for almost one decade, platforms that support full-fledged cybersecurity training exercises have only been released as open source in recent years. Given the complexity of such cybersecurity training platforms, the question that arises is how to meaningfully evaluate and compare their capabilities in order to identify the most suitable solution for a given type of organization and/or training activity. In this paper, we introduce a capability assessment methodology for cybersecurity training platforms that focuses on the three key aspects of training: content representation, environment management, and training facilitation. The assessment tool that we developed is used to evaluate two open-source cybersecurity training platforms, CyTrONE and KYPO. We then conduct a comparative analysis of these two platforms based on our first-hand developer experience with them, and discuss the lessons learned from implementing, deploying and using these platforms. The assessment tool and the detailed technical comparative analysis that we conducted are intended as instruments and references for anyone who plans to deploy or develop cybersecurity training platforms.

As the Internet of Things (IoT) becomes more and more pervasive, encompassing many aspects of our daily life, the issue of how much the IoT systems can be trusted is critical. However, the multitude of recent incidents that were caused by or somehow involved such systems, often with dire consequences, makes it obvious that IoT system trustworthiness is not yet attained.

Wireless full-duplex (FD) transmission is one of the key drivers for improving spectrum utilization and network capacity. Many research studies have examined suppressing the residual self-interference and reducing the co-channel interference from other ongoing transmissions to realize the wireless FD transmission. The trade-off between the interference power and capacity gain still leads to a great challenge in designing the practical FD medium access control (MAC) protocol in the multihop wireless networks. This paper proposes a novel FD MAC protocol with a Mixture of Concurrent and Sequential transmission (MCST) scheme to accomplish a higher throughput by managing the transmissions and optimizing the achievable transmission capacity. Numerical simulations reveal that the proposed FD MAC with MCST scheme can achieve higher average saturation throughput with a reasonable level of average overhead ratio compared to the existing HD MAC and FD MAC protocols.

Full-duplex (FD) wireless communication improves the attainable spectral efficiency of the wireless network with the simultaneous transmission and reception over the same frequency channel at a single timeslot. However, the effect of self-interference (SI) and inter-user interference (IUI) become crucial to take into consideration compared to the current half-duplex system. Many researches have been studied to suppress the SI and IUI, to design the medium access control (MAC) protocol, and to propose resource management techniques for realizing the FD system and truly achieving the double transmission capacity in the network. In this paper, we propose a novel FD MAC protocol with a mixture of concurrent and sequential transmission scheme, namely FD-MCST, for maximizing the transmission capacity in multihop wireless networks. Through FD-MCST, the transmitting node can cooperatively share the transmission capacity status for maximizing the network capacity. As a result, numerical simulations reveal that the proposed FD-MCST can accomplish a higher achievable network capacity of up to 1.7 times and nearly twice the achievable throughput with a reasonable amount of achievable transmission overhead about 7.7%, compared to the existing FD MAC protocols.

Full-duplex (FD) communication is one of the key drivers for improving spectrum utilization. Many researches have been studied to suppress the self-interference for realizing FD communications in addition to reducing the co-channel interference from other ongoing transmissions. Since controlling the transmission power will affect the total interference and improve the overall network capacity, in this paper, we investigate an improved consensus transmit power control (CTPC) algorithm with optimal search technique to maximize the saturation throughput while reducing the transmission power and message exchange overhead in the FD wireless multihop networks. Numerical simulations reveal that the CTPC performance can achieve better performance in terms of saturation throughput, transmit power and message exchange overhead.

Smart Home is generally considered to be the final solution for human living problem, especially for health care of the elderly and disabled, power saving, etc. Human activity recognition in smart home is the key to achieve home automation, which enables smart services automatically run according to human mind. Recent researches have made several progresses in this field, however most of them can only recognize default activities which is probably not needed by smart home services. In addition, low scalability makes such researches infeasible out of laboratory. In this work, we unwrap this issue and propose a novel framework to not only recognize human activity, but also predict it. The framework contains three stages: recognition after the activity; recognition in progress and activity prediction in advance. With the help of RFID tags, the hardware cost of our framework is low enough to popularize. And the experiment result shows that our framework can realize good performance in activity recognition and prediction with high scalability.

Conformance testing is a formal approach for checking the validity of an implemented system against its specification. This paper adopts it to comprehensively check the conformance of smart house temperature with its requirements. However, besides its limited capability to detect thermal problems, e.g., temperature fluctuation, it is inefficient when dealing with thermal problems in different time intervals of a test duration. To overcome these problems, this paper proposes a multiple-conformance approach. We adopt hybrid automata to model the required indoor temperature change as the specification, which enabled the check in different time intervals of the test duration. More conformance rules are prescribed in the multiple-conformance approach to enhance its capability to detect thermal problems. We demonstrate its practical usefulness through an experiment, the results of which demonstrate the effectiveness of the proposed approach in detecting thermal problems.

A physical-layer network coding approach, compute-and- forward based on nested lattice code (NLC), is considered for multi-source multi-relay networks. This paper proposes a design of overlapped chunked code (OCC) which is applied before NLC, which we call OCC/CF. Random linear network coding is applied within each chunk. Only the transmissions from the sources to the relays are considered. The design is based on the empirical rank distribution and the empirical probability distributions of the participation factor of all sources. A consecutive OCC is employed with the proposed design to investigate the performance of OCC/CF. From the numerical results, the design overhead of OCC/CF is low when the probability distribution of the participation factor is dense at chunk size for each source.

Hybrid automata are a formal model for dynamical systems with discrete and continuous components. This paper exploits the capability of hybrid automata to model the required indoor temperature change for comprehensively detecting thermal problems. The requirement is to specify the changes in the indoor temperature by considering thermal discomfort that ranges from uncomfortable to serious. We first devise an example of home appliance control service for indoor temperature adjustment. Then, based on the example, this paper proposes the modeling of the required indoor temperature change by hybrid automata. To this end, we represent different states of the hybrid automata, which correspond to different thermal sensations of indoor temperature change, by resorting to various indices. Then, the required indoor temperature change is prescribed by heat exchange between indoor and outdoor. Experiment results demonstrate the capability of hybrid automata to model the required indoor temperature change.

In a world in which cyber-attacks occur on a daily basis, cybersecurity education and training are indispensable. Current training programs rely on manual setup and configuration for hands-on activities, which is a tedious and error-prone task. In this paper we present CyTrONE, an integrated cybersecurity training framework that we designed and implemented to address such shortcomings. The key insight is automating the training content generation and environment setup tasks. The advantages of this approach are: (i) improve the accuracy of the training setup; (ii) decrease the setup time and cost; (iii) make training possible repeatedly, and for a large number of participants. In the paper we thoroughly discuss the architecture and implementation of the framework, and we evaluate it from several perspectives in order to demonstrate that CyTrONE meets the aforementioned objectives.

This paper proposes a transmission scheme which applies random linear network coding (RLNC) over compute-and-forward (CF), called RLNC/CF, in multi-source multi-relay networks. Instead of solving the full rank failure at relays, this paper compensates for this overhead to increase the possibility of successfully decoding computed messages at the destination. The concept of the overlapped generations is applied with a proposed computing and storing strategy. This paper provides a compensation based on the estimation of the channel state information (CSI) of the previous generation and a compensation based on the learning data of CSI. By comparing to an orthogonal channel transmission scheme, a performance trade-off is considered. An expression for estimated performances of RLNC/CF in function of the probabilities of the parameters related to CSI is provided to help for the decision of selecting transmission scheme. From the numerical result, RLNC/CF scheme works better than a conventional CF transmission scheme in reducing the transmission latency.

Real-time system is playing an important role in our society. For such a system, sensitivity to timing is the central feature of system behaviors, which means tasks in the system are required to be completed before their deadlines. Currently, almost all the practical real-time systems are equipped within multiple processors, for which the schedule synthesis to make sure that all the tasks can be completed before their deadlines is known to be an NP complete problem. In this paper, to solve the scheduling problem, we propose a scheduling method based on satisfiability modulo theories (SMT). In the method, the problem of scheduling is treated as a satisfiability problem. The key work is to formalize the satisfiability problem using first-order language. After the formalization, a SMT solver (e.g., Z3, Yices) is employed to solve such a satisfiability problem. An optimal schedule can be generated based on a solution model returned by the SMT solver. Moreover, in the SMT-based scheduling method, we define the scheduling constraints as system constraints and target constraints. Such design makes the proposed method apply more widely compared with existing methods.

In this paper we introduce a network coding aware Medium Access Control (necoMAC) scheme that incorporates many protocols such as NCA-2PSP, 2PSP and NCA-CSMA in order to provide data transmission in higher rates with fewer number of transmissions for multirate wireless networks. We create two golden topologies called golden chain and golden triangle, and calculate their energy consumption, overhead ratio, throughput and fairness for each protocol. We also set up a simulation to further analyze the performance of these protocols with the increasing number of nodes and flows. The simulation results show that the proposed scheme provides higher through-put and less energy consumption compared to the conventional CSMA/CA.

In a real-time system, tasks are required to be completed before their deadlines. Due to heavy workload, the system may be in overload condition under which some tasks may miss their deadlines. To alleviate the degrees of system performance degradation cased by the missed deadline tasks, the design of scheduling is crucial. Many design objectives can be considered. In this paper, we focus on maximizing the total number of tasks that can be completed before their deadlines. A scheduling method based on satisfiability modulo theories (SMT) is proposed. In the method, the problem of scheduling is treated as a satisfiability problem. The key work is to formalize the satisfiability problem using first-order language. After the formalization, a SMT solver (e.g., Z3, Yices) is employed to solver such a satisfiability problem. An optimal schedule can be generated based on a solution model returned by the SMT solver. The correctness of this method and the optimality of the generated schedule are straightforward. The time efficiency of the proposed method is demonstrated through various simulations. To the best of our knowledge, it is the first time introducing SMT to solve overload problem in real-time scheduling domain.

Although the Semantic Web data standards are established, ontology-based applications built on the standards are relatively limited. This is partly due to high learning curve and efforts demanded in building ontology-based Semantic Web applications. In this paper, we describe an ontology application management (OAM) framework that aims to simplify creation and adoption of ontology-based application that is based on the Semantic Web technology. OAM introduces an intermediate layer between user application and programming and development environment in order to support ontology-based data publishing and access, abstraction and interoperability. The framework focuses on providing reusable and configurable data and application templates, which allow the users to create the applications without programming skill required. Three forms of templates are introduced: database to ontology mapping configuration, recommendation rule and application templates. We describe two case studies that adopted the framework: activity recognition in smart home domain and thalassemia clinical support system, and how the framework was used in simplifying development in both projects. In addition, we provide some performance evaluation results to show that, by limiting expressiveness of the rule language, a specialized form of recommendation processor can be developed for more efficient performance. Some advantages and limitations of the application framework in ontology-based applications are also discussed.

In real-time systems, a task is required to be completed before its deadline. When workload is heavy, the system may become overloaded. Under such condition, some tasks may miss their deadlines. To deal with this overload problem, the design of scheduling algorithm is crucial. In this paper, we focus on studying on-line scheduling for overloaded realtime systems. The objective is to maximize the total number of tasks that meet their deadlines. To achieve this goal, the idea of greedy algorithm is used to propose a greedy scheduling (GS) algorithm. In each time, GS makes an optimum choice for currently known task set. As the uncertainty of new arriving tasks, GS cannot make an optimum choice for the set of overall tasks. To deal with this uncertainty, by applying feedback control, a greedy scheduling with feedback control (GSFC) is introduced. Three widely used scheduling algorithms and their corresponding deferrable scheduling (DS) methods are discussed and compared with GSFC. Simulation results reveal that GSFC can effectively improve the system performance.

The key factor of influencing the network capacity performance is the effect of interference power of receiving nodes, which is obtained from the other transmitting nodes in multihop wireless networks (MWNs) that are simultaneously using the same channel. Minimizing total interference power can improve overall network capacity and reduce total energy consumption. In this paper, we propose a consensus transmit power control (CTPC) algorithm to maximize end-to-end throughput in MWNs. The CTPC algorithm tunes the nodes' transmit powers to maximize the average end-to-end throughput with a consensus coefficient. Simulation results reveal that the CTPC algorithm enables all the traffic flows to accomplish the maximum average end-to-end throughput. At the same time, the total interference power and the total power consumption are decreasing. Only in the dense MWNs, under usual threshold of received signal strength indicator (RSSI) setting, the CTPC algorithm cannot achieve good performance. In addition, an advanced wmediumd emulator over the StarBED testbed is used to further verify the performance evaluation of CTPC algorithm.

Today, when a huge number of information resources appear in the Internet, one problem arising is how to search and exploit these information resources efficiently and flexibly with high scalability. We believe that the realization of multiple-attribute searching in a distributed manner is an important solution for this problem. In this paper, we propose a solution of building a distributed attribute-value tree (AV tree) on DHT-based networks to realize multiple-attribute searching. Each resource is named by an AV tree and DHT keys are created from each AV branch of a resource name. Resource information is distributed to a DHT-based network by the use of these DHT keys. Our solution keeps the locality of resource names in the key space of DHT networks such that AV branches that share a number of attribute/value pairs (AV pairs) will be mapped to a continuous portion of the key space. Therefore, multiple-attribute searching can be achieved efficiently by querying nodes responsible for a continuous portion of the key space. Our system can achieve both efficiency and a good degree of load balancing even when the distribution of AV pairs in resource names is skewed. Our simulation result shows the efficiency of our solution in respects of query success ratio and the degree of load balancing, comparing with conventional approaches.

IEEE 802.15.4 networks are promising solutions for wireless personal area networks, and in particular for wireless home area networks. IEEE 802.15.4 has numerous applications in fields such as energy management and home automation. However, real-world trials with 802.15.4 devices are difficult because of the characteristics of these devices (small dimensions, wireless communication), and the potentially large size of the network. We present in this paper an IEEE 802.15.4 network emulation testbed that makes possible repeatable and controllable live experiments with 802.15.4-based devices. The testbed is built by extending the functionality of the wireless network emulation testbed named QOMB with 802.15.4 PHY and MAC layer capabilities, as well as 802.15.4 device processor emulation. We illustrate the usability of the 802.15.4 network emulation testbed with a case study of home networking used for automation related to environment control.

In this paper we present QOMB, a testbed we designed and implemented for the evaluation of wireless network systems, protocols and applications. The testbed uses the wireless network emulation set of tools QOMET so as to reproduce in a wired network, in real time, the wireless network conditions corresponding to a given scenario. In this context QOMET also provides support for features such as realistic virtual 3D environments, and node mobility generation. The infrastructure of QOMB is StarBED, the large-scale network experiment testbed at the National Institute of Information and Communications Technology, Hokuriku Research Center, in Ishikawa, Japan. The multi-hop wireless network emulation experimental results related to OLSR performance analysis in mesh networks and MANETs illustrate the main features and the usability of QOMB.

In disaster areas, office buildings, or at home, multiple autonomous networked mobile robots may act instead of human beings. These robots have to move to their destiantion so as to perform their function. For this purpose they need to be able to recognize the changes in the surrounding environment. They are equipped with a motion-planning method in order to avoid in real time collisions with other robots or obtacles. In this paper we propose a motion planning method based on PRM (Probabilistic Roadmap) algorithm. To evaluate our method, we constructed an experiment platform based on StarBED, which is a large-scale network testbed. By using the virtual environment manager Map Manager, the WLAN emulator QOMET, and the experiment-support software RUNE we are able to perform emulation of large-scale autonomous networked mobile robot systems. The experimental results confirm the usefulness of collaborative motion planning, which results in reaching faster the estimation and in less frequent re-planning.

Unbalanced energy consumption is an inherent problem in wireless sensor networks where some nodes may be overused and die out early, resulting in a short network lifetime. In this paper, we investigate the problem of balancing energy consumption for data gathering sensor networks. Our key idea is to exploit the tradeoff between hop-by-hop transmission and direct transmission to balance energy dissipation among sensor nodes. By assigning each node a transmission probability which controls the ratio between hop-by-hop transmission and direct transmission, we formulate the energy consumption balancing problem as an optimal transmission probability allocation problem. We discuss this problem for both chain networks and general networks. Moreover, we present the solution to compute the optimal number of sections in terms of maximizing the network lifetime. Numerical results demonstrate that our methods outperform the traditional hop-by-hop and direct transmission schemes and achieve significant lifetime extension especially for dense sensor networks.

Data aggregation is a widely used technique in wireless sensor networks. The security issues, data confidentiality and integrity, in data aggregation become vital when the sensor network is deployed in a hostile environment. There has been many related work proposed to address these security issues. In this paper we survey these work and classify them into two cases: hop-by-hop encrypted data aggregation and end-to-end encrypted data aggregation. We also propose two general frameworks for the two cases respectively. The framework for end-to-end encrypted data aggregation has higher computation cost on the sensor nodes, but achieves stronger security, in comparison with the framework for hop-by-hop encrypted data aggregation