Recent developments in IoT technology are expected to enable the realization of systems that collect and process physical-space data at the edge for each region and provide it in real time. However, if large volumes of raw data are transmitted without considering the recipient’s location or requirements, network traffic increases and the risk of private information leakage beyond the region also rises. Therefore, regional data must be abstracted according to the user context before delivery. To address these challenges, this study proposes a content delivery method based on semantic communication, which extracts only the semantic information from the data, transmits it, and reconstructs it on the receiver side. Through image transmission experiments, we confirmed that the proposed method reduces communication traffic by approximately 40 percent while maintaining image quality (SSIM of at least 0.9), demonstrating both the potential and the challenges of user-context-adaptive content delivery.

Currently, LoRa suffers from scalability issues, making it still far from a feasible option for LPWAN in large deployment scenarios. During Signal Transmission, no other signal employing the same Spreading Factor should be transmitted simultaneously, as this condition would render both signals unrecoverable. We proposed introducing orthogonal codes to the LoRa Physical layer. We demonstrated that the introduction of orthogonal codes significantly enhanced the signal recovery rate, achieving a Detection Error Rate (DER) of $\mathbf{9 9. 5 \%}$ for singledevice scenarios and maintaining robust performance with an $\mathbf{8 5. 3 3 \%}$ DER for two devices per SF.

Biometric verification is essential for secure identity verification and authentication during banking transactions using fingerprints, facial features, irises, and voices. Among these methods, voice biometrics is a promising alternative owing to its potential for robust and convenient user authentication. However, their effectiveness is significantly challenged by variations in the voice caused by different device configurations and environmental conditions. These variations can reduce the effectiveness of speaker identification and undermine the reliability of voice-based systems for securing online transactions. For an effective comparative solution, this study addresses these challenges by focusing on the difficulties posed by voice variations due to differences in device hardware, microphone quality, and environmental noise. Our approach employs machine-learning techniques using advanced speech enhancement methods to improve the consistency and accuracy of voice biometric verification across diverse devices. Specifically, we employ an adaptive filter model that enhances signal extraction, noise suppression, and predictive precision. Furthermore, our empirical demonstration showed that the adaptive filter significantly improved the accuracy of voice biometric systems by mitigating the impact of device-induced voice variations. In addition, we evaluate the performance of this model using a range of metrics.

The introduction of Network Function Virtualization (NFV) has shifted network processing from specialized hardware to more flexible commodity servers, and this transition is still evolving. New industrial applications, the Internet of Things (IoT), and technologies like augmented, virtual, and mixed reality (AR/VR/MR) require networks that can handle event-based operations and middleware with very low and predictable latency. These requirements pose performance optimization challenges for packet processing in a layered infrastructure. In this study, we look deep into the challenges of implementing such network infrastructures using general-purpose hardware, a strategy motivated by its flexibility to realize telco-cloud and the potential to reduce electronic waste. Focusing on NFV with an emphasis on containerized network functions (CNFs), we investigate the performance limitations, particularly the high jitter and throughput variation observed in packet forwarding. We used a network function (NF) implemented using defacto industry standard user-space I/O architecture DPDK in bare-metal and containerized environments for performance evaluation. We conducted ten experiments in a 40 GbE environment to measure throughput, latency, and jitter across various packet sizes, traffic rates, and system configurations. The results indicate that adjusting CPU settings can significantly enhance throughput for CNFs despite a potential increase in jitter. We found that CNFs are feasible for latency-sensitive tasks, particularly under conditions of low traffic and specific packet sizes. With careful system-level configuration, CNFs can be used in beyond 5G cloud-native networking, offering promising potential for latency-sensitive applications.

International audience

Mobile phones were originally designed for commu-nication, wherein they have evolved into multifunctional devices used for financial transactions, social media, and more, making them an integral mode of communication for the dweller's world. However, the mostly used OS in mobile devices, Android consist of various vulnerabilities and lack sufficient security measures, leaving them susceptible to malware injection. Moreover, the attackers develop sophisticated malware, which is challenging to detect by traditional detection approaches. Henceforth, an effective malware detection method is imperative to ensure the safety and security of Android systems. In this study, we address the dynamic analysis of Android malware using supervised and semi-supervised deep neural network techniques to tackle existing challenges. Our investigation is conducted on the CCCS-CIC-AndMal-2020 dataset and the results reveal that our proposed supervised models (1D CNN, MLP, RNN and LSTM) outperform state-of-the-art supervised models significantly with an accuracy of 99.76%. Additionally, we explore semi-supervised approach using limited label data, where our label spreading approach showcases a highly effective detection accuracy of 97.26%, approaching that of a fully supervised approach.

Nowadays, edge computing has become part of the Internet of Things (IoT) that plays a vital role in developing smart applications. As the usage of IoT devices significantly increases, at the same time, network edge infrastructure faces several security challenges. Distributed Denial-of-Service (DDoS) attack is one of the most severe threats to edge-cloud services. Therefore, designing a robust mitigating system is unavoidable for the network edge, and it must be able to recognize emerging attacks. This work proposes an anomaly-based DDoS detection approach that combines information-theoretic metrics and multivariate correlation analysis. The information-theoretic metric captures the randomness and complex nature of traffic behaviour. Similarly, multivariate correlation analysis identifies the relationship among traffic features. Combining information metrics and correlation analysis, we generate normal and attack traffic profiles for the training base to estimate density. The generated profiles build on the metrics including Triangle Area Mapping (TAM) with correlation analysis, Renyi’s divergence, covariance, mean, and standard deviation, which enhances the detection performance of the proposed approach. The effectiveness of the proposed approach is evaluated using testbed and benchmark datasets. The results show that the proposed approach achieves 0.17% and 2.32%, and 0.50% higher accuracy compared to the baseline approaches on the testbed, UNSW and CIC-DDoS datasets, respectively.

Electricity thefts are conventionally manually detected by inspections, accusations, and the failure of meters. However, the recent evolution of machine learning may allow the automatic detection of electricity theft only from the patterns of meter readings. Electric consumption heavily relies on many factors, e.g., the lifestyle of the day and the weather, and thus the accuracy of detection is questioned. We propose an electricity theft detection framework for smart homes with knowledge-based synthetic attack data. This allows training of the attack classifier only from the legitimate power consumption data, i.e, without attack actions and associated labels. We identified five attack patterns as the knowledge which consisted of smart attacks and legacy attacks. We have conducted comprehensive evaluations with nine machine learning models using the Almanac of Minutely Power dataset version 2 (AMPds2) dataset fine-grained time-series data of a smart home. We found that Gradient Boosting-based algorithms achieved the best, and Random Forest performed alternatively with almost 100% accuracy for detecting and classifying legacy attacks. Some smart attacks were not detected, but those algorithms achieved good performance in detection and classification.

Purpose Smart-home security involves multilayered security challenges related to smart-home devices, networks, mobile applications, cloud servers and users. However, very few studies focus on smart-home users. This paper aims to fill this gap by investigating the potential interests of adult smart-home users in cybersecurity awareness training and nonfinancial rewards that may encourage them to adopt sound cybersecurity practices. Design/methodology/approach A total of 423 smart-home users between the ages of 25 and 64 completed a survey questionnaire for this study, with 224 participants from Japan and 199 from the UK. Findings Cultural factors considerably influence adult smart-home users’ attitudes toward cybersecurity. Specifically, cultural differences impact their willingness to participate in cybersecurity awareness training, their views on the importance of cybersecurity training for children and senior citizens and their preference for nonfinancial rewards as an incentive for good cybersecurity behavior. These results highlight the need to consider cultural differences and their potential impact when developing and implementing cybersecurity programs that target smart-home users. Practical implications This research has two main implications. First, it provides insights for information security professionals on the importance of designing cost-effective and time-efficient cybersecurity awareness training programs for smart-home users. Second, the findings may assist governments in establishing nonfinancial incentives to encourage greater uptake of cybersecurity practices among smart-home users. Originality/value The paper investigates whether adult smart-home users are willing to spend time and money to engage in cybersecurity awareness training and to encourage their children and elderly parents to participate in training, as well. In addition, the paper examines incentives, especially nonfinancial rewards, that may motivate adult smart-home users to adopt cybersecurity behaviors at home. Furthermore, the paper analyses demographic differences among smart-home users in Japan and the UK.

Cloud computing dominates the information communication and technology landscape despite the presence of lingering security issues such as the interdependency problem. The latter is a co-residence conundrum where the attacker successfully compromises his target virtual machine by first exploiting the weakest (in terms of security) virtual machine that is hosted in the same server. To tackle this issue, we propose a novel virtual machine allocation policy that is based on the attacker's efficiency and coverage. By default, our allocation policy considers all legitimate users as attackers and then proceeds to host the users' virtual machines to the server where their efficiency and/or coverage are the smallest. Our simulation results show that our proposal performs better than the existing allocation policies that were proposed to tackle the same issue, by reducing the attacker's possibilities to zero and by using between 30 - 48% less hosts.

There is a growing need to anonymize data as new businesses are increasingly utilizing vast amount of unstructured text. Also, unstructured text have a risk of personal location estimation by considering location information. Nevertheless, existing generalizations do not take into location information and therefore cannot robustly handle this attack. In this study, we proposed anonymizing location information in unstructured text using knowledge graph newly constructed from an actual geographic information system. Our method has the advantages of anonymization, taking into account actual geographic information, handling abbreviations and spelling inconsistencies, and allowing for dynamic graph updates. The results of the evaluation experiments show that anonymization is more robust than existing methods against location estimation attacks without compromising its usefulness as a dataset. Also, we found that the names of organizations and places with a high probability of occurrence in unstructured text are more likely to lead to personal identification.

The paper proposes a collection method of SOS messages in a disaster area with collapsed communication service. So far, we have proposed and implemented SOSCast as a smartphone application to support rescue operations for finding immobilized victims. However, in its current implementation, we found that SOSCast limits the search coverage area and disregards some SOS messages. Moreover, the smartphone rapidly consumes battery due to the required constant communication. We propose, therefore, to implement Wi-Fi Direct (WD) in addition to Bluetooth (BT) to increase the search coverage area. Furthermore, we introduce an information-sharing cluster in order to collect SOS messages efficiently while preserving the smartphones' battery life. By implementing the proposed methods on actual smartphones for a performance evaluation, we showed that controlling the use of both WD and BT increases the probability of locating immobilized victims. Also, enabling an information-sharing cluster by immobilized victims' smartphones can send SOS messages to a mobile victim smartphone all at once, which can extend the lifetime of the smartphone's battery as compared with the previous method.

This study presents new channel utilization methods for a multi-channel wireless backbone network (WBN). To flexibly and efficiently use multiple channels, we exploit OpenFlow as a baseline function. In this paper, we propose a new framework to build an OpenFlow-based multi-channel WBN and then implement two new channel utilization methods: (i) a flow direction aware channel assignment method (DCA) and (ii) a flow balance aware channel assignment method (BCA). BCA is classified into two simple multi-hop transmission procedures: (ii-1) each flow is transmitted on a persistent channel (BCA-PC), while (ii-2) being transmitted on a different channel for each hop (BCA-DC). Finally, we evaluate the proposed channel utilization methods in a real testbed. As a result, our methods can obtain approximately three times as much network capacity as the conventional WBN. In addition, BCA-DC can avoid the radio interference with neighbor hops effectively, thereby not only achieving efficient channel utilization but also expanding network capacity significantly.

In this paper, we propose a relay recovery route maintenance protocol for ad hoc networks to combine the benefits of both proactive and reactive route recovery strategies and to minimize their drawbacks. In our proposal, one or more substitute routes usually become ready for the recovery of every link in a route before its break, while the route recovery process actually starts only when the upstream node of a link confirms the link break. Since this scheme does not broadcast any control packet, it can effectively recover a broken link without heavy control overhead traffic. Also, it helps reduce the time delay due to the recovery, since substitute routes are already available when the upstream node initiates the route recovery process. We further propose two automatic route shortening schemes to optimize the route during successful packet forwarding without causing extra control overhead. We have implemented our proposed schemes based on AODV and compared their performance with competitive schemes including original AODV. Simulation results demonstrate that our proposal definitely reduces the time delay and control overhead traffic in route repairing process, and that the route shortening schemes further leads to shorter time delay and average route length.

In ubiquitous Wireless LANs (WLANs), Mobile Nodes (MNs) are likely to experience many and frequent handovers between WLANs managed by different organizations/ISPs during TCP communication. Although we proposed a WLAN handover management scheme based on the number of frame retransmissions, the proposed scheme cannot adapt to multirate WLAN used in a more realistic environment. Furthermore, the handover is controlled based on only a predetermined threshold of the number of frame retransmissions, thereby degrading the performance drastically when the inappropriate threshold is employed. Therefore, in this paper, to enhance the practicality of handover scheme in a realistic environment where WLAN supports multiple data rates and automatically changes the rate in response to wireless link condition, we propose a handover management scheme adaptable to multi-rate WLANs. Our scheme exploits two sorts of information to recognize the wireless condition appropriately: (1) data rate used most frequently (DRMF) by each interface and (2) frame retransmission ratio (FRR) on each interface for some duration. The former of two criteria first enables us to estimate an area where we should start handover, and if DRMFs of two interfaces are same in the area, the latter then allows us to compare wireless condition on two interfaces precisely, thereby giving an optimal handover point. The simulation results demonstrate the advantages of the proposed method, especially in the multi-rate WLAN environment.

In the near future, wireless local area networks (WLANs) will overlap to provide continuous coverage over a wide area. In such ubiquitous WLANs, a mobile node (MN) freely moves between WLANs with different IP subnets during VoIP communication. In such situations, since an MN experiences several handovers, the communication quality is degraded. In previous studies, in order to solve this problem, we proposed a seamless handover scheme based on frame retransmissions and demonstrated its effectiveness through simulation experiments. However, no existing scheme has demonstrated the effectiveness of cross-layer architecture exploiting the number of frame retransmissions on a real system. In the present paper, therefore, we design and implement a handover scheme based on the number of frame retransmissions. In our implementation, we propose a cross-layer architecture using a shared memory to pass the information, i.e., the number of frame retransmissions, from MAC layer to Transport layer. Finally, we show preliminary results in a real wireless environment and evaluate the performance of the proposed prototype system in a simple topology.