Biblio

As millions of IoT devices are interconnected together for better communication and computation, compromising even a single device opens a gateway for the adversary to access the network leading to an epidemic. It is pivotal to detect any malicious activity on a device and mitigate the threat. Among multiple feasible security threats, malware (malicious applications) poses a serious risk to modern IoT networks. A wide range of malware can replicate itself and propagate through the network via the underlying connectivity in the IoT networks making the malware epidemic inevitable. There exist several techniques ranging from heuristics to game-theory based technique to model the malware propagation and minimize the impact on the overall network. The state-of-the-art game-theory based approaches solely focus either on the network performance or the malware confinement but does not optimize both simultaneously. In this paper, we propose a throughput-aware game theory-based end-to-end IoT network security framework to confine the malware epidemic while preserving the overall network performance. We propose a two-player game with one player being the attacker and other being the defender. Each player has three different strategies and each strategy leads to a certain gain to that player with an associated cost. A tailored min-max algorithm was introduced to solve the game. We have evaluated our strategy on a 500 node network for different classes of malware and compare with existing state-of-the-art heuristic and game theory-based solutions.

In this paper we propose a security and cost aware scheduling heuristic for real-time workflow jobs that process Internet of Things (IoT) data with various security requirements. The environment under study is a four-tier architecture, consisting of IoT, mist, fog and cloud layers. The resources in the mist, fog and cloud tiers are considered to be heterogeneous. The proposed scheduling approach is compared to a baseline strategy, which is security aware, but not cost aware. The performance evaluation of both heuristics is conducted via simulation, under different values of security level probabilities for the initial IoT input data of the entry tasks of the workflow jobs.

Fog computing is a new computing paradigm that utilizes numerous mutually cooperating terminal devices or network edge devices to provide computing, storage, and communication services. Fog computing extends cloud computing services to the edge of the network, making up for the deficiencies of cloud computing in terms of location awareness, mobility support and latency. However, fog nodes are not active enough to perform tasks, and fog nodes recruited by cloud service providers cannot provide stable and continuous resources, which limits the development of fog computing. In the process of cloud service providers using the resources in the fog nodes to provide services to users, the cloud service providers and fog nodes are selfish and committed to maximizing their own payoffs. This situation makes it easy for the fog node to work negatively during the execution of the task. Limited by the low quality of resource provided by fog nodes, the payoff of cloud service providers has been severely affected. In response to this problem, an appropriate incentive mechanism needs to be established in the fog computing environment to solve the core problems faced by both cloud service providers and fog nodes in maximizing their respective utility, in order to achieve the incentive effect. Therefore, this paper proposes an incentive model based on repeated game, and designs a trigger strategy with credible threats, and obtains the conditions for incentive consistency. Under this condition, the fog node will be forced by the deterrence of the trigger strategy to voluntarily choose the strategy of actively executing the task, so as to avoid the loss of subsequent rewards when it is found to perform the task passively. Then, using evolutionary game theory to analyze the stability of the trigger strategy, it proves the dynamic validity of the incentive consistency condition.

The spread of Internet of Things (IoT) devices and high-speed communications, such as 5G, makes their services rich and diverse. Therefore, it is desirable to perform functions of rich services transparently and use edge computing environments flexibly at intermediate locations on the Internet, from the perspective of a network system. When this type of edge computing environment is achieved, IoT nodes as end devices of the Internet can fully utilize edge computing systems and cloud systems without any change, such as switching destination IP addresses between them, along with protocol maintenance for the switching. However, when the data transfer in the communication is encrypted, a decryption method is necessary at the edge, to realize these transparent edge services. In this study, a transparent common key-exchanging method with cloud service has been proposed as the destination node of a communication pair, to transparently decrypt a secure sockets layer-encrypted communication stream at the edge area. This enables end devices to be free from any changes and updates to communicate with the destination node.

Radar sensors have shown great potential for surveillance and security authentication applications. However, a thorough analysis of their vulnerability to spoofing or replay attacks has not been performed yet. In this paper, the feasibility of performing spoofing attacks to radar sensor is studied and experimentally verified. First, a simple binary phase-shift keying system was used to generate artificial spectral components in the radar's demodulated signal. Additionally, an analog phase shifter was driven by an arbitrary signal generator to mimic the human cardio-respiratory motion. Characteristic time and frequency domain cardio-respiratory human signatures were successfully generated, which opens possibilities to perform spoofing attacks to surveillance and security continuous authentication systems based on microwave radar sensors.

The Interface to Network Security Functions (I2NSF) Working Group in Internet Engineering Task Force (IETF) provides data models of interfaces to easily configure Network Security Functions (NSF). The Working Group presents a high-level data model and a low-level data model for configuring the NSFs. The high-level data model is used for the users to manipulate the NSFs configuration easily without any security expertise. But the NSFs cannot be configured using the high-level data model as it needs a low-level data model to properly deploy their security operation. For that reason, the I2NSF Framework needs a security policy translator to translate the high-level data model into the corresponding low-level data model. This paper improves the previously proposed Security Policy Translator by adding an Automatic Data Model Mapper. The proposed mapper focuses on the mapping between the elements in the high-level data model and the elements in low-level data model to automate the translation without the need for a security administrator to create a mapping table.

TCP SYN Flood is one of the most widespread DoS attack types performed on computer networks nowadays. As a possible countermeasure, we implemented and deployed modified versions of three network-based mitigation techniques for TCP SYN authentication. All of them utilize the TCP three-way handshake mechanism to establish a security association with a client before forwarding its SYN data. These algorithms are especially effective against regular attacks with spoofed IP addresses. However, our modifications allow deflecting even more sophisticated SYN floods able to bypass most of the conventional approaches. This comes at the cost of the delayed first connection attempt, but all subsequent SYN segments experience no significant additional latency (\textbackslashtextless; 0.2ms). This paper provides a detailed description and analysis of the approaches, as well as implementation details with enhanced security tweaks. The discussed implementations are built on top of the hardware-accelerated FPGA-based DDoS protection solution developed by CESNET and are about to be deployed in its backbone network and Internet exchange point at NIX.CZ.

The Internet of Things (IoT) has been growing rapidly in recent years. With the appearance of 5G, it is expected to become even more indispensable to people's lives. In accordance with the increase of Distributed Denial-of-Service (DDoS) attacks from IoT devices, DDoS defense has become a hot research topic. DDoS detection mechanisms executed on routers and SDN environments have been intensely studied. However, these methods have the disadvantage of requiring the cost and performance of the devices. In addition, there is no existing DDoS mitigation algorithm on the network edge that can be performed with the low-cost and low-performance equipment. Therefore, this paper proposes a light-weight DDoS mitigation scheme at the network edge using limited resources of inexpensive devices such as home gateways. The goal of the proposed scheme is to detect and mitigate flooding attacks. It utilizes unused queue resources to detect malicious flows by random shuffling of queue allocation and discard the packets of the detected flows. The performance of the proposed scheme was confirmed via theoretical analysis and computer simulation. The simulation results match the theoretical results and the proposed algorithm can efficiently detect malicious flows using limited resources.

Over the last few years, the deployment of Internet of Things (IoT) is attaining much more concern on smart computing devices. With the exponential growth of small devices and at the same time cheap prices of these sensing devices, there raises an important question for the security of the stored information as these devices generate a large amount of private data for observing and controlling purposes. Distributed Denial of Service (DDoS) attacks are current examples of major security threats to IoT devices. As yet, no standard protocol can fully ensure the security of IoT devices. But adaptive decision making along with elasticity and incessant monitoring is required. These difficulties can be resolved with the assistance of Software Defined Networking (SDN) which can viably deal with the security dangers to the IoT devices in a powerful and versatile way without hampering the lightweightness of the IoT devices. Although SDN performs quite well for managing and controlling IoT devices, security is still an open concern. Nonetheless, there are a few challenges relating to the mitigation of DDoS attacks in IoT systems implemented with SDN architecture. In this paper, a brief overview of some of the popular DDoS attack mitigation techniques and their limitations are described. Also, the challenges of implementing these techniques in SDN-based architecture to IoT devices have been presented.

The efficiency of secure deletion is highly dependent on the data layout of underlying storage devices. In particular, owing to the sequential-write constraint of the emerging Shingled Magnetic Recording (SMR) technology, an improper data layout could lead to serious write amplification and hinder the performance of secure deletion. The performance degradation of secure deletion on SMR drives is further aggravated with the need to securely erase the file system metadata of deleted files due to the small-size nature of file system metadata. Such an observation motivates us to propose a secure-deletion and SMR-aware space allocation (SSSA) strategy to facilitate the process of securely erasing both the deleted files and their metadata simultaneously. The proposed strategy is integrated within the widely-used extended file system 4 (ext4) and is evaluated through a series of experiments to demonstrate the effectiveness of the proposed strategy. The evaluation results show that the proposed strategy can reduce the secure deletion latency by 91.3% on average when compared with naive SMR-based ext4 file system.

The rapid increase in the use of IoT devices brings many benefits to the digital society, ranging from improved efficiency to higher productivity. However, the limited resources and the open nature of these devices make them vulnerable to various cyber threats. A single compromised device can have an impact on the whole network and lead to major security and physical damages. This paper explores the potential of using network profiling and machine learning to secure IoT against cyber attacks. The proposed anomaly-based intrusion detection solution dynamically and actively profiles and monitors all networked devices for the detection of IoT device tampering attempts as well as suspicious network transactions. Any deviation from the defined profile is considered to be an attack and is subject to further analysis. Raw traffic is also passed on to the machine learning classifier for examination and identification of potential attacks. Performance assessment of the proposed methodology is conducted on the Cyber-Trust testbed using normal and malicious network traffic. The experimental results show that the proposed anomaly detection system delivers promising results with an overall accuracy of 98.35% and 0.98% of false-positive alarms.

In recent years, more and more medical institutions have been using electronic medical records (EMRs) to improve service efficiency and reduce storage cost. However, it is difficult for medical institutions with different management methods to share medical data. The medical data of patients is easy to be abused, and there are security risks of privacy data leakage. The above problems seriously impede the sharing of medical data. To solve these problems, we propose a blockchain-based medical data sharing scheme with attribute-based searchable encryption, named BMDS. In BMDS, encrypted EMRs are securely stored in the interplanetary file system (IPFS), while corresponding indexes and other information are stored in a medical consortium blockchain. The proposed BMDS has the features of tamper-proof, privacy preservation, verifiability and secure key management, and there is no single point of failure. The performance evaluation of computational overhead and security analysis show that the proposed BMDS has more comprehensive security features and practicability.

IoT systems commonly rely on cloud services. However, utilizing cloud providers can be problematic in terms of data security. Data stored in the cloud need to be secured from unauthorized malicious nodes and from the cloud providers themselves. Using a simple symmetric cipher can encrypt the data before uploading and decrypt it while retrieving. However, such a solution can be only applied between two parties with no support for multiple nodes. Whereas in IoT scenarios, many smart devices communicate and share data with each other. This paper proposes a solution that tackles the issue of sharing data securely between IoT devices by implementing a system that allows secure sharing of encrypted data in untrusted clouds. The implementation of the system performs the computation on connectionless clients with no involvement of the cloud server nor any third party. The cloud server is only used as a passive storage server. Analysis of the implemented prototype demonstrates that the system can be used in real-life applications with relatively small overhead. Based on the used hardware, key generation takes about 60 nanoseconds and the storage overhead is only a few kilobytes for large number of files and/or users.

Smart energy management system in smart grid carries a large number of sensitive data, which needs encryption algorithm to ensure the security of system communication. At present, most of the terminal devices of smart grid are embedded devices with limited computing resources, and their communication encryption mostly relies on AES encryption algorithm. It is difficult in key management and key distribution. Therefore, this paper proposes an improved ECC-AES hybrid encryption algorithm. Firstly, ECC algorithm is improved to improve the speed of encryption and decryption, and then the improved ECC algorithm is used as a supplement to AES algorithm. ECC is used to encrypt the AES key, which improves the security of the algorithm. At the same time, the experimental simulation also proves that the improved ECC algorithm has obvious performance improvement in computing time, CPU occupancy and memory usage.

The rapid expansion and diversity of technology throughout society have impacted the growing knowledge gap in conducting analysis on IoT devices. The IoT digital forensic field lacks the necessary tools and guidance to perform digital forensics on these devices. This is mainly attributed to their level of complexity and heterogeneity that is abundant within IoT devices-making the use of a JTAG technique one of the only ways to acquire information stored on an IoT device effectively. Nonetheless, utilizing a JTAG technique can be challenging, especially when having multiple devices with each possibly having its own configuration. To alleviate these issues within the field, we propose the development of an Internet of Things - Forensics Recovery Assistant (IoT-FRA). The IoT-FRA will offer the capabilities of an expert system to assist inexperienced users in performing forensics recovery of IoT devices through a JTAG technique and analysis on the device's capabilities to develop an organized method that will prioritize IoT devices to be analyzed.

Currently, the technology of wireless mesh networks is actively developing. In 2021, Gartner included mesh network technologies and the tasks to ensure their security in the TOP global trends. A large number of scientific works focus on the research and modeling the traffic transmission in such networks. At the same time, they often bring up the “bottle neck” problem, characteristic of individual mesh network nodes. To address the issue, the authors of the article propose using the data caching mechanism and placing the cache data straight on the routers. The mathematical model presented in the article allows building a route with the highest access speed to the requested content by the modified Dijkstra algorithm. Besides, if the mesh network cache lacks the required content, the routers with the Internet access are applied. Practically, the considered method of creating routes to the content, which has already been requested by the users in the mesh network, allows for the optimal efficient use of the router bandwidth capacity distribution and reduces the latency period.

Effective identification of Internet of things devices in cyberspace is of great significance to the protection of Cyberspace Security. However, there are a large number of such devices in cyberspace, which can not be identified by the existing methods of identifying IoT devices because of the lack of key information such as manufacturer name and device name in the response message. Their existence brings hidden danger to Cyberspace Security. In order to identify the IoT devices with missing key information in these response messages, this paper proposes an IoT device identification method, IoTCatcher. IoTCatcher uses HTTP response message and the structure and style characteristics of HTML document, and based on natural language processing technology and text similarity technology, classifies and compares the IoT devices whose response message lacks key information, so as to generate their device finger information. This paper proves that the recognition precision of IoTCatcher is 95.29%, and the recall rate is 91.01%. Compared with the existing methods, the overall performance is improved by 38.83%.

One of the most innovative directions in the Internet is Information Centric Networks, in particular the Named Data Network. This approach should make it easier to find and retrieve the desired information on the network through name-based addressing, intranet caching and other schemes. This article presents Named Data Network modeling, results and performance evaluation of proposed caching policies for Named Data Network research, taking into account the influence of external factors on base of Zipf's law and uniform distribution.

With the rise of Internet of Things (IoT) devices, home network management and security are becoming complex. There is an urgent requirement to make smart home network management more efficient. This work proposes an SDN-based architecture to secure smart home networks through K-Nearest Neighbor (KNN) based device classifications and malicious traffic detection. The efficiency is enhanced by offloading the computation-intensive KNN model to a Field Programmable Gate Arrays (FPGA). Furthermore, we propose a custom KNN solution that exhibits the best performance on an FPGA compared with four alternative KNN instances (i.e., 78% faster than a parallel Bubble Sort-based implementation and 99% faster than three other sorting algorithms). Moreover, with 36,225 training samples, the proposed KNN solution classifies a test query with 95% accuracy in approximately 4 ms on an FPGA compared to 57 seconds on a CPU platform. This highlights the promise of FPGA-based platforms for edge computing applications in the smart home.

In mobile edge computing, edge devices collect data, and an edge server performs computational or data processing tasks that need real-time processing. Depending upon the requested task's complexity, an edge server executes it locally or remotely in the cloud. When an edge server needs to offload its computational tasks, there could be a sudden failure in the cloud or network. In this scenario, we need to provide a flexible execution model to edge devices and servers for the continuous execution of the task. To that end, in this paper, we induced a middleware system that allows an edge server to execute a task on the edge devices instead of offloading it to a cloud server. Edge devices not only send data to an edge server for further processing but also execute edge services by utilizing nearby edge devices' computing resources. We extend the concept of service-oriented architecture and integrate a decentralized peer-to-peer network architecture to achieve reusability, location-specific security, and reliability. By following our methodology, software developers can enhance their application in a collaborative environment without worrying about low-level implementation.

In order to protect user privacy and provide better access control in Internet of Things (IoT) environments, designing an appropriate two-party authentication and key exchange protocol is a prominent challenge. In this paper, we propose a lightweight certificateless non-interactive authentication and key exchange (CNAKE) protocol for mutual authentication between remote users and smart devices. Based on elliptic curves, our lightweight protocol provides high security performance, realizes non-interactive authentication between the two entities, and effectively reduces communication overhead. Under the random oracle model, the proposed protocol is provably secure based on the Computational Diffie-Hellman and Bilinear Diffie-Hellman hardness assumption. Finally, through a series of experiments and comprehensive performance analysis, we demonstrate that our scheme is fast and secure.

With the development of various technologies, the modern industry has been promoted to a new era known as Industry 4.0. Within such paradigm, smart factories are becoming widely recognized as the fundamental concept. These systems generate and exchange vast amounts of privacy-sensitive data, which makes them attractive targets of attacks and unauthorized access. To improve privacy and security within such environments, a more decentralized approach is seen as the solution to allow their longterm growth. Currently, the blockchain technology represents one of the most suitable candidate technologies able to support distributed and secure ecosystem for Industry 4.0 while ensuring reliability, information integrity and access authorization. Blockchain based access control frameworks address encountered challenges regarding the confidentiality, traceability and notarization of access demands and procedures. However significant additional fears are raised about entities' privacy regarding access history and shared policies. In this paper, our main focus is to ensure strong privacy guarantees over the access control related procedures regarding access requester sensitive attributes and shared access control policies. The proposed scheme called PDAMF based on ring signatures adds a privacy layer for hiding sensitive attributes while keeping the verification process transparent and public. Results from a real implementation plus performance evaluation prove the proposed concept and demonstrate its feasibility.

Blockchain-based cryptocurrencies offer an appealing alternative to Fiat currencies, due to their decentralized and borderless nature. However the decentralized settings make the authentication process more challenging: Standard cryptographic methods often rely on the ability of users to reliably store a (large) secret information. What happens if one user's key is lost or stolen? Blockchain systems lack of fallback mechanisms that allow one to recover from such an event, whereas the traditional banking system has developed and deploys quite effective solutions. In this work, we develop new cryptographic techniques to integrate security policies (developed in the traditional banking domain) in the blockchain settings. We propose a system where a smart contract is given the custody of the user's funds and has the ability to invoke a two-factor authentication (2FA) procedure in case of an exceptional event (e.g., a particularly large transaction or a key recovery request). To enable this, the owner of the account secret-shares the answers of some security questions among a committee of users. When the 2FA mechanism is triggered, the committee members can provide the smart contract with enough information to check whether an attempt was successful, and nothing more. We then design a protocol that securely and efficiently implements such a functionality: The protocol is round-optimal, is robust to the corruption of a subset of committee members, supports low-entropy secrets, and is concretely efficient. As a stepping stone towards the design of this protocol, we introduce a new threshold homomorphic encryption scheme for linear predicates from bilinear maps, which might be of independent interest. To substantiate the practicality of our approach, we implement the above protocol as a smart contract in Ethereum and show that it can be used today as an additional safeguard for suspicious transactions, at minimal added cost. We also implement a second scheme where the smart contract additionally requests a signature from a physical hardware token, whose verification key is registered upfront by the owner of the funds. We show how to integrate the widely used universal two-factor authentication (U2F) tokens in blockchain environments, thus enabling the deployment of our system with available hardware.

Commodity I/O hardware often fails to separate I/O transfers of isolated OS and applications code. Even when using the best I/O hardware, commodity systems sometimes trade off separation assurance for increased performance. Remarkably, device firmware need not be malicious. Instead, any malicious driver, even if isolated in its own execution domain, can manipulate its device to breach I/O separation. To prevent such vulnerabilities with high assurance, a formal I/O separation model and its use in automatic generation of secure I/O kernel code is necessary.This paper presents a formal I/O separation model, which defines a separation policy based on authorization of I/O transfers and is hardware agnostic. The model, its refinement, and instantiation in the Wimpy kernel design, are formally specified and verified in Dafny. We then specify the kernel implementation and automatically generate verified-correct assembly code that enforces the I/O separation policies. Our formal modeling enables the discovery of heretofore unknown design and implementation vulnerabilities of the original Wimpy kernel. Finally, we outline how the model can be applied to other I/O kernels and conclude with the key lessons learned.

With the advancement of network technology, more electronic devices have begun to connect to the Internet. The era of IoE (Internet of Everything) is coming. However, the number of serious incidents of cyberattacks on important facilities has gradually increased at the same time. Security becomes an important issue when setting up plenty of network devices in an environment. Thus, we propose an innovative mechanism of the Moving Target Defense (MTD) to solve the problems happening to other MTD mechanisms in the past. This method applies Dynamic Host Configuration Protocol (DHCP) to dynamically change the IPv4 address of information equipment in the medical environment. In other words, each of the nodes performs IP-Hopping and effectively avoids malicious attacks. Communication between devices relies on DNS lookup. The mechanism avoids problems such as time synchronization and IP conflict. Also, it greatly reduces the costs of large-scale deployment. All of these problems are encountered by other MTD mechanisms in the past. Not only can the mechanism be applied to the medical and information equipment, it can also be applied to various devices connected to the Internet, including Industrial Control System (ICS). The mechanism is implemented in existing technologies and prevents other problems, which makes it easy to build a system.