Biblio

In this paper, we investigate a networked control problem in the presence of Denial-of-Service (DoS) attacks, which prevent transmissions over the communication network. The communication between the process and controller is also subject to bit rate constraints. For mitigating the influences of DoS attacks and bit rate constraints, we develop a variable bit rate (VBR) encoding-decoding protocol and quantized controller to stabilize the control system. We show that the system’s resilience against DoS under VBR is preserved comparing with those under constant bit rate (CBR) quantized control, with fewer bits transmitted especially when the attack levels are low. The proposed VBR quantized control framework in this paper is general enough such that the results of CBR quantized control under DoS and moreover the results of minimum bit rate in the absence of DoS can be recovered.

Although DC microgrids using a distributed cooperative control architecture can avoid the instability or shutdown issues caused by a single-point failure as compared to the centralized approach, limited global information in the former makes it difficult to detect cyber attacks. Here, we present a false data injection attack (FDIA)–-termed as a local control input attack–-targeting voltage observers in the secondary controllers and control loops in the primary controllers. Such an attack cannot be detected by only observing the performance of the estimated voltage of each agent, thereby posing a potential threat to the system operation. To address this, a detection method using the outputs of the voltage observers is developed to identify the exact location of an FDIA. The proposed approach is based on the characteristics of the distributed cooperative network and avoids heavy dependency on the system model parameters. Next, an event-driven mitigation approach is deployed to substitute the attacked element with a reconstructed signal upon the detection of an attack. Finally, the effectiveness of the proposed resilient scheme is validated using simulation results.

Federated Learning (FL) is a multiparty learning computing approach that can aid privacy-preservation machine learning. However, FL has several potential security and privacy threats. First, the existing FL requires a central coordinator for the learning process which brings a single point of failure and trust issues for the shared trained model. Second, during the learning process, intentionally unreliable model updates performed by Byzantine colluding parties can lower the quality and convergence of the shared ML models. Therefore, discovering verifiable local model updates (i.e., integrity or correctness) and trusted parties in FL becomes crucial. In this paper, we propose a resilient and verifiable FL algorithm based on a reputation scheme to cope with unreliable parties. We develop a selection algorithm for task publisher and blockchain-based multiparty learning architecture approach where local model updates are securely exchanged and verified without the central party. We also proposed a novel auditing scheme to ensure our proposed approach is resilient up to 50% Byzantine colluding attack in a malicious scenario.

Peer-to-peer (P2P) systems such as BitTorrent and Bitcoin are susceptible to serious attacks from byzantine nodes that join as peers. Due to well-known impossibility results for designing P2P primitives in unrestricted byzantine settings, research has explored many adversarial models with additional assumptions, ranging from mild (such as pre-established PKI) to strong (such as the existence of common random coins). One such widely-studied model is the general-omission model, which yields simple protocols with good efficiency, but has been considered impractical or unrealizable since it artificially limits the adversary only to omitting messages.In this work, we study the setting of a synchronous network wherein peer nodes have CPUs equipped with a recent trusted computing mechanism called Intel SGX. In this model, we observe that the byzantine adversary reduces to the adversary in the general-omission model. As a first result, we show that by leveraging SGX features, we eliminate any source of advantage for a byzantine adversary beyond that gained by omitting messages, making the general-omission model realizable. Our evaluation of 1000 nodes running on 40 DeterLab machines confirms theoretical efficiency claim.

The importance of the security of industrial control network has become increasingly prominent. Aiming at the defects of main security protection system in the intelligent manufacturing industrial control network, we propose a security attack risk detection and defense, and emergency processing capability synchronization technology system suitable for the intelligent manufacturing industrial control system. Integrating system control and network security theories, a flexible and reconfigurable system-wide security architecture method is proposed. On the basis of considering the high availability and strong real-time of the system, our research centers on key technologies supporting system-wide security analysis, defense strategy deployment and synchronization, including weak supervision system reinforcement and pattern matching, etc.. Our research is helpful to solve the problem of industrial control network of “old but full of loopholes” caused by the long-term closed development of the production network of important parts, and alleviate the contradiction between the high availability of the production system and the relatively backward security defense measures.

With the development of openness, sharing and interconnection of computer network, the architecture of enterprise network becomes more and more complex, and various network security problems appear. Threat Intelligence(TI) Analysis and situation awareness(SA) are the prediction and analysis technology of enterprise security risk, while intrusion detection technology belongs to active defense technology. In order to ensure the safe operation of computer network system, we must establish a multi-level and comprehensive security system. This paper analyzes many security risks faced by enterprise computer network, and integrates threat intelligence analysis, security situation assessment, intrusion detection and other technologies to build a comprehensive enterprise security system to ensure the security of large enterprise network.

Existing works indicate that Spiking Neural Networks (SNNs) are resilient to adversarial attacks by testing against few attack models. This paper studies adversarial attacks on SNNs using additional attack models and shows that SNNs are not inherently robust against many few-pixel L0 black-box attacks. Additionally, a method to defend against such attacks in SNNs is presented. The SNNs and the effects of adversarial attacks are tested on both software simulators as well as on SpiNNaker neuromorphic hardware.

The traditional approach to distributed deep neural network (DNN) training is data-distributed learning, which partitions and distributes data to workers. This approach, although has good convergence properties, has high communication cost, which puts a strain especially on edge systems and increases delay. An emerging approach is model-distributed learning, where a training model is distributed across workers. Model-distributed learning is a promising approach to reduce communication and storage costs, which is crucial for edge systems. In this paper, we design ResPipe, a novel resilient model-distributed DNN training mechanism against delayed/failed workers. We analyze the communication cost of ResPipe and demonstrate the trade-off between resiliency and communication cost. We implement ResPipe in a real testbed consisting of Android-based smartphones, and show that it improves the convergence rate and accuracy of training for convolutional neural networks (CNNs).

With the spread of the Internet, various devices are now connected to it and the number of IoT devices is increasing. Data generated by IoT devices has traditionally been aggregated in the cloud and processed over time. However, there are two issues with using the cloud. The first is the response delay caused by the long distance between the IoT device and the cloud, and the second is the difficulty of implementing sufficient security measures on the IoT device side due to the limited resources of the IoT device at the end. To address these issues, fog computing, which is located in the middle between IoT devices and the cloud, has been attracting attention as a new network component. However, the risks associated with the introduction of fog computing have not yet been fully investigated. In this study, we conducted a risk assessment of fog computing, which is newly established to promote the use of IoT devices, and identified 24 risk factors. The main countermeasures include the gradual introduction of connected IoT connection protocols and security policy matching. We also demonstrated the effectiveness of the proposed risk measures by evaluating the risk values. The proposed risk countermeasures for fog computing should help us to utilize IoT devices in a safe and secure manner.

The presence of the Information Technology System (ITS) has become one of the components for basic needs that must be met in navigating through the ages. Organizational programs in responding to the industrial era 4.0 make the use of ITS is a must in order to facilitate all processes related to quality service in carrying out the main task of protecting and serving the community. The implementation of ITS is actually not easy forthe threat of challenges and disturbances in the form of risks haunts ITS's operations. These conditions must be able to be identified and analyzed and then action can be executed to reduce the negative impact, so the risks are acceptable. This research will study about ITS risk management using the the guideline of Information Technology Infrastructure Library (ITIL) to formulate an operational strategy in order ensure that STI services at the Papa Oscar Cikeas Data Center (DC) can run well in the form of recommendations. Based on a survey on the implementing elements of IT function, 82.18% of respondents considered that the IT services provided by DC were very important, 86.49% of respondents knew the importance of having an emergency plan to ensure their products and services were always available, and 67.17% of respondents believes that DC is well managed. The results of the study concludes that it is necessary to immediately form a structural DC organization to prepare a good path for the establishment of a professional data center in supporting public service information technology systems.

In the era of Big Data, opportunities and challenges are mixed. The data transfer is increasingly frequent and speedy, and the data lifecycle is also extended, bringing more challenges to security and privacy risk governance. Currently, the common measures of risk governance covering the entire data life cycle are the data-related staff management, equipment security management, data encryption codes, data content identification and de-identification processing, etc. With the trend of data globalization, regulations fragmentation and governance technologization, “International standards”, a measure of governance combining technology and regulation, has the potential to become the best practice. However, “voluntary compliance” of international standards derogates the effectiveness of risk governance through this measure. In order to strengthen the enforcement of the international standards, the paper proposes a governance approach which is “the framework regulated by international standards, and regulations and technologies specifically implemented by national legislation.” It aims to implement the security and privacy risk governance of Big Data effectively.

In recent years, new cyber attacks such as targeted attacks have caused extensive damage. With the continuing development of the IoT society, various devices are now connected to the network and are being used for various purposes. The Internet of Things has the potential to link cyber risks to actual property damage, as cyberspace risks are connected to physical space. With this increase in unknown cyber risks, the demand for cyber insurance is increasing. One of the most serious emerging risks is the silent cyber risk, and it is likely to increase in the future. However, at present, security measures against silent cyber risks are insufficient. In this study, we conducted a risk management of silent cyber risk for organizations with the objective of contributing to the development of risk management methods for new cyber risks that are expected to increase in the future. Specifically, we modeled silent cyber risk by focusing on state transitions to different risks. We newly defined two types of silent cyber risk, namely, Alteration risk and Combination risk, and conducted risk assessment. Our assessment identified 23 risk factors, and after analyzing them, we found that all of them were classified as Risk Transference. We clarified that the most effective risk countermeasure for Alteration risk was insurance and for Combination risk was measures to reduce the impact of the risk factors themselves. Our evaluation showed that the silent cyber risk could be reduced by about 50%, thus demonstrating the effectiveness of the proposed countermeasures.

Due to the difficulty of obtaining a database of original images that are required in the classification process to detect tampering, this paper presents a technique for detecting image tampering such as image steganography in the spatial domain. The system depends on deriving the auto-correlation function of the image histogram, then applying a high-pass filter with a threshold. This technique can be used to decide which image is cover or a stego image, without adopting the original image. The results have eventually revealed the validity of this system. Although this study has focused on least-significant-bit (LSB) steganography, we expect that it could be extended to other types of image tapering.

With the rapid development of the internet of things and cloud computing, users can instantly transmit a large amount of data to various fields, with the development of communication technology providing convenience for people's life, information security is becoming more and more important. Therefore, it is of great significance to study the technology of image hiding information detection. This paper mainly uses the support vector machine learning algorithm to detect the hidden information of the image, based on a standard image library, randomly selecting images for embedding secret information. According to the bit-plane correlation and the gradient energy change of a single bit-plane after encryption of an image LSB matching algorithm, gradient energy change is selected as characteristic change, and the gradient energy change is innovatively applied to a support vector machine classifier algorithm, and has very good detection effect and good stability on the dense image with the embedding rate of more than 40 percent.

As a new service-oriented computing paradigm, cloud computing facilitates users to share and use resources. However, due to the dynamic and openness of its operating environment, only relying on traditional identity authentication technology can no longer fully meet the security requirements of cloud computing. The trust evaluation of user behavior has become the key to improve the security of cloud computing. Therefore, in view of some problems existing in our current research on user behavior trust, this paper optimizes and improves the construction of the evaluation index system and the calculation of trust value, and proposes a cloud end-user behavior trust evaluation model based on sliding window. Finally, the model is proved to be scientific and effective by simulation experiments, which has certain significance for the security protection of cloud resources.

This paper explores a practical approach to securing large wireless networks by applying Physical Layer Security (PLS). To date, PLS has mostly been seen as an information theory concept with few practical implementations. We present an Access Point (AP) selection algorithm that uses PLS to find an AP that offers the highest secrecy capacity to a legitimate user. We then propose an implementation of this algorithm using the novel concept of spectrum programming which extends Software-Defined Networking to the physical and data-link layers and makes wireless network management and control more flexible and scalable than traditional platforms. Our Wi-Fi network evaluation results show that our approach outperforms conventional solutions in terms of security, but at the expense of communication capacity, thus identifying a trade-off between security and performance. These results encourage implementation and extension to further wireless technologies.

To support secure and reliable operation of optical networks, we propose a framework for autonomous anomaly detection, root cause analysis and visualization of the anomaly impact on optical signal parameters. Verification on experimental physical layer security data reveals important properties of different attack profiles.

Phishing is a frequent assault in which unsuspecting people’s unique, private, and sensitive information is stolen through fake websites. The primary objective of phishing websites’consistent resource allocators isto steal unique, private, and sensitive information such as user login passwords and online financial transactions. Phishers construct phony websites that look and sound just like genuine things. With the advent of technology, there are protecting users significantly increased in phishing methods. It necessitates the development of an anti-phishing technology to identify phishing and protect users. Machine learning is a useful technique for combating phishing attempts. These articles were utilized to examine Machine learning for detection strategies and characteristics.

With the development of wireless sensor networks (WSNs), WSNs have been widely used in various fields such as animal habitat detection, military surveillance, etc. This paper focuses on protecting the source location privacy (SLP) in WSNs. Existing algorithms perform poorly in non-uniform networks which are common in reality. In order to address the performance degradation problem of existing algorithms in non-uniform networks, this paper proposes a robust fixed path-based random routing scheme (RFRR), which guarantees the path diversity with certainty in non-uniform networks. In RFRR, the data packets are sent by selecting a routing path that is highly differentiated from each other, which effectively protects SLP and resists the backtracking attack. The experimental results show that RFRR increases the difficulty of the backtracking attack while safekeeping the balance between security and energy consumption.

Identity resolution system is the primary technical research problem to set up the data collection capability of industrial internet, and the configuration resolution of complex assets is an application difficulty. To implement the particular requirements of complex equipment configuration management, an industry-oriented identity resolution architecture and the configuration resolution service were designed. In accordance with the technical information management of high-speed train, corresponding handle structures was proposed to describe the configuration structure and related components information of EMU (Electric Multiple Unit). A distributed processing algorithm for configuration resolution and the hit-ratio evaluation method of handle service sites was proposed. The performance, stability, and resolution consistency of the handle system in this paper are proved by experiments, which is also great significant to the intelligent identity applications in other industries.

Digitization has been rapidly integrated with manufacturing industries and critical infrastructure to increase efficiency, productivity, and reduce wastefulness, a transition being labeled as Industry 4.0. However, this expansion, coupled with the poor cybersecurity posture of these Industrial Internet of Things (IIoT) devices, has made them prolific targets for exploitation. Moreover, modern Programmable Logic Controllers (PLC) used in the Operational Technology (OT) sector are adopting open-source operating systems such as Linux instead of proprietary software, making such devices susceptible to Linux-based malware. Traditional malware detection approaches cannot be applied directly or extended to such environments due to the unique restrictions of these PLC devices, such as limited computational power and real-time requirements. In this paper, we propose ORRIS, a novel lightweight and out-of-the-device framework that detects malware at both kernel and user-level by processing the information collected using the Joint Test Action Group (JTAG) interface. We evaluate ORRIS against in-the-wild Linux malware achieving maximum detection accuracy of ≈99.7% with very few false-positive occurrences, a result comparable to the state-of-the-art commercial products. Moreover, we also develop and demonstrate a real-time implementation of ORRIS for commercial PLCs.

The development of artificial intelligence will certainly fundamentally change the pattern of human work. With the promotion of top-level strategies, vocational education can only develop sustainably by integrating with science and technology. Artificial intelligence is a branch of computer science that studies the basic theories, methods and techniques of how to apply computer hardware and software to simulate certain intelligent human behaviors. Artificial intelligence applied to vocational education mainly focuses on resource network technology and integrated distributed intelligent system, which organically integrates various different expert systems (ES), management information systems (MIS), intelligent networks, decision support systems (DSS), databases, numerical computing packages and graphics processing programs to solve complex problems. Artificial intelligence will certainly empower vocational education and give rise to a vocational education revolution. In the process of continuous improvement of AI, it is a more practical approach to apply various already mature AI technologies to vocational education practice. Establishing an intelligent vocational education ecology enables traditional education and AI to complement each other's advantages and jointly promote the healthy and sustainable development of vocational education ecology.

Security of physical layer key generation is based on the randomness and reciprocity of wireless fading channel, which has attracted more and more attention in recent years. This paper proposes a rate adaptive key agreement scheme and utilizes the received signal strength (RSS) of the channel between two wireless devices to generate the key. In conventional information reconciliation process, the bit inconsistency rate is usually eliminated by using the filter method, which increases the possibility of exposing the generated key bit string. Building on the strengths of existing secret key extraction approaches, this paper develops a scheme that uses Reed-Solomon (RS) codes, one of forward error correction channel codes, for information reconciliation. Owing to strong error correction performance of RS codes, the proposed scheme can solve the problem of inconsistent key bit string in the process of channel sensing. At the same time, the composition of RS codes can help the scheme realize rate adaptation well due to the construction principle of error correction code, which can freely control the code rate and achieve the reconciliation method of different key bit string length. Through experiments, we find that when the number of inconsistent key bits is not greater than the maximum error correction number of RS codes, it can well meet the purpose of reconciliation.

Emerging technologies change the qualities of modern healthcare by employing smart systems for patient monitoring. To well use the data surrounding the patient, tiny sensing devices and smart gateways are involved. These sensing systems have been used to collect and analyze the real-time data remotely in Internet of Medical Thinks (IoM). Since the patient sensed information is so sensitive, the security and privacy of medical data are becoming challenging problem in IoM. It is then important to ensure the security, privacy and integrity of the transmitted data by designing a secure and a lightweight authentication protocol for the IoM. In this paper, in order to improve the authentication and communications in health care applications, we present a novel secure and anonymous authentication scheme. We will use elliptic curve cryptography (ECC) with random numbers generated by fuzzy logic. We simulate IoM scheme using network simulator 3 (NS3) and we employ optimized link state routing protocol (OLSR) algorithm and ECC at each node of the network. We apply some attack algorithms such as Pollard’s ρ and Baby-step Giant-step to evaluate the vulnerability of the proposed scheme.

The supply chain has been much developed with the internet technology being used in the business world. Some issues are becoming more and more evident than before in the course of the fast evolution of the supply chain. Among these issues, the remarkable problems include low efficiency of communication, insufficient operational outcomes and lack of the credit among the participants in the whole chain. The main reasons to cause these problems lie in the isolated information unable to be traced and in the unclear responsibility, etc. In recent years, the block chain technology has been growing fast. Being decentralized, traceable and unable to be distorted, the block chain technology is well suitable for solving the problems existing in the supply chain. Therefore, the paper first exposes the traditional supply chain mode and the actual situation of the supply chain management. Then it explains the block chain technology and explores the application & effects of the block chain technology in the traditional supply chain. Next, a supply chain style is designed on the base of the block chain technology. Finally the potential benefits of the remolded supply chain are foreseen if it is applied in the business field.