Biblio

Existing methods for home security monitoring depend on expensive custom battery-powered solutions. In this article, we present a battery-free solution that leverages any off-the-shelf passive radio frequency identification (RFID) tag for real-time entry detection. Sensor consists of a printed RFID antenna on paper, coupled to a magnetic reed switch and is affixed on the door. Opening of the door triggers the reed switch causing RFID signal transmission detected by any off-the-shelf passive RFID reader. This paper shows simulation and experimental results for such magnetically-actuated RFID (or magRFID) opening sensor.

With current IoT architectures, once a single device in a network is compromised, it can be used to disrupt the behavior of other devices on the same network. Even though system administrators can secure critical devices in the network using best practices and state-of-the-art technology, a single vulnerable device can undermine the security of the entire network. The goal of this work is to limit the ability of an attacker to exploit a vulnerable device on an IoT network and fabricate deceitful messages to co-opt other devices. The approach is to limit attackers by using device proxies that are used to retransmit and control network communications. We present an architecture that prevents deceitful messages generated by compromised devices from affecting the rest of the network. The design assumes a centralized and trustworthy machine that can observe the behavior of all devices on the network. The central machine collects application layer data, as opposed to low-level network traffic, from each IoT device. The collected data is used to train models that capture the normal behavior of each individual IoT device. The normal behavioral data is then used to monitor the IoT devices and detect anomalous behavior. This paper reports on our experiments using both a binary classifier and a density-based clustering algorithm to model benign IoT device behavior with a realistic test-bed, designed to capture normal behavior in an IoT-monitored environment. Results from the IoT testbed show that both the classifier and the clustering algorithms are promising and encourage the use of application-level data for detecting compromised IoT devices.

A joint INCOSE/NDIA project is exploring the intersection between systems security engineering and product line engineering teams to develop cyber secure and resilient approaches for feature-based variation management. The project team is investigating existing approaches and developing new approaches to implement systems security in product line design, apply patterns for product line architectures that address systems security, and define variation management approaches for secure and resilient product line products and shared assets.

Certificateless signcryption mechanism can not only provide security services, such as message integrity, non-repudiation and confidentiality, but also solve the problems of public key certificate management and key escrow. Zhou et al. proposed a certificateless signcryption mechanism without bilinear mapping and gave its security proof under the discrete logarithm problem and the computational Diffie Hellman problem in the random oracle model. However, the analysis show that this scheme has security flaws. That is, attackers can forge legitimate signatures of any messages. Finally, we give the specific attack process.

The insurance business plays a quite significant role in people's lives, but in the process of claim settlement, there are still various frauds such that the insurance companies' refusal to compensate or customers' malicious fraud to obtain compensation. Therefore, it is very important to ensure fair and just claims. In this paper, by combining the blockchain technology and the ciphertext-policy attribute-based encryption system, we build a scheme for secure storage and update for insurance records under the InterPlanetary File System (IPFS) storage environment in the insurance system. In this scheme, we use the fog node to outsource encryption of insurance records to improve the efficiency of the staff; In addition, we store encrypted insurance records on IPFS to ensure the security of the storage platform and avoid the single point failure of the centralized mechanism. In addition, we use the immutability of the blockchain to achieve the non-repudiation of both insurance companies and the client. The security proof shows that the proposed scheme can achieve selective security against selected keyword attacks. Our scheme is efficient and feasible under performance analysis and real data set experiments.

SM9 is an identity-based cryptography algorithm published by the State Cryptography Administration of China. With SM9, a user's private key for signing is generated by a central system called key generation center (KGC). When the owner of the private key wants to shirk responsibility by denying that the signature was generated by himself, he can claim that the operator of KGC forged the signature using the generated private key. To address this issue, in this paper, two schemes of SM9 digital signature with non-repudiation are proposed. With the proposed schemes, the user's private key for signing is collaboratively generated by two separate components, one of which is deployed in the private key service provider's site while the other is deployed in the user's site. The private key can only be calculated in the user's site with the help of homomorphic encryption. Therefore, only the user can obtain the private key and he cannot deny that the signature was generated by himself. The proposed schemes can achieve the non-repudiation of SM9 digital signature.

The mechanism for distributing information on the source of the attack by combining blockchain technology with the Intrusion Prevention System (IPS) can be done so that DDoS attack mitigation becomes more flexible, saves resources and costs. Also, by informing the blacklisted Internet Protocol(IP), each IPS can share attack source information so that attack traffic blocking can be carried out on IPS that are closer to the source of the attack. Therefore, the attack traffic passing through the network can be drastically reduced because the attack traffic has been blocked on the IPS that is closer to the attack source. The blocking of existing DDoS attack traffic is generally carried out on each IPS without a mechanism to share information on the source of the attack so that each IPS cannot cooperate. Also, even though the DDoS attack traffic did not reach the server because it had been blocked by IPS, the attack traffic still flooded the network so that network performance was reduced. Through smart contracts on the Ethereum blockchain, it is possible to inform the source of the attack or blacklisted IP addresses without requiring additional infrastructure. The blacklisted IP address is used by IPS to detect and handle DDoS attacks. Through the blacklisted IP distribution scheme, testing and analysis are carried out to see information on the source of the attack on each IPS and the attack traffic that passes on the network. The result is that each IPS can have the same blacklisted IP so that each IPS can have the same attack source information. The results also showed that the attack traffic through the network infrastructure can be drastically reduced. Initially, the total number of attack packets had an average of 115,578 reduced to 27,165.

There have been researches on Distributed Denial of Service (DDoS) attack detection based on deep learning, but most of them use the feature data processed by data mining for feature learning and classification. Based on the original data flow, this paper combines the method of Gray Level Co-occurrence Matrix (GLCM), which not only retains the original data but also can further extract the potential relationship between the original data. The original data matrix and the reconstructed matrix were taken as the input of the model, and the Convolutional Neural Network(CNN) was used for feature learning. Finally, the classifier model was trained for detection. The experimental part is divided into two parts: comparing the detection effect of different data processing methods and different deep learning algorithms; the effectiveness and objectivity of the proposed method are verified by comparing the detection effect of the deep learning algorithm with that of the statistical analysis feature algorithm.

In recent years, software defined networking (SDN) has been proposed for enhancing the security of industrial control networks. However, its ability to guarantee the quality of service (QoS) requirements of such networks in the presence of adversarial flow still needs to be investigated. Queueing theory and particularly queueing network models have long been employed to study the performance and QoS characteristics of networks. The latter appears to be particularly suitable to capture the behaviour of SDN owing to the dependencies between layers, planes and components in an SDN architecture. Also, several authors have used queueing network models to study the behaviour of different application of SDN architectures, but none of the existing works have considered the strong periodic network traffic in software-defined industrial control networks. In this paper, we propose a queueing network model for softwaredefined industrial control networks, taking into account the strong periodic patterns of the network traffic in the data plane. We derive the performance measures for the analytical model and apply the queueing network model to study the effect of adversarial flow in software-defined industrial control networks.

Industrial control systems (ICSs) are facing serious security challenges due to their inherent flaws, and emergence of vulnerabilities from the integration with commercial components and networks. To that end, assessing the security plays a vital role for current industrial enterprises which are responsible for critical infrastructure. This paper accomplishes a complex task of quantitative assessment based on attack graphs in order to look forward critical paths. For the purpose of application to a large-scale heterogeneous ICSs, we propose a flexible attack graph generation algorithm is proposed with the help of the graph data model. Hereafter, our quantitative assessment takes a consideration of graph indicators on specific nodes and edges to get the security metrics. In order to improve results of obtaining the critical attack path, we introduced a formulating selection rule, considering the asset value of industrial control devices. The experimental results show validation and verification of the proposed method.

Layer assignment, a major step in global routing of integrated circuits, is usually performed to assign segments of nets to multiple layers. Besides the traditional optimization goals such as overflow and via count, interconnect delay plays an important role in determining chip performance and has been attracting much attention in recent years. Accordingly, in this paper, we propose MiniDelay, a timing-aware layer assignment algorithm to minimize delay for advanced technology nodes, taking both wire congestion and coupling effect into account. MiniDelay consists of the following three key techniques: 1) a non-default-rule routing technique is adopted to reduce the delay of timing critical nets, 2) an effective congestion assessment method is proposed to optimize delay of nets and via count simultaneously, and 3) a net scalpel technique is proposed to further reduce the maximum delay of nets, so that the chip performance can be improved in a global manner. Experimental results on multiple benchmarks confirm that the proposed algorithm leads to lower delay and few vias, while achieving the best solution quality among the existing algorithms with the shortest runtime.

Conversational AI agents are proliferating, embodying a range of devices such as smart speakers, smart displays, robots, cars, and more. We can envision a future where a personal conversational agent could migrate across different form factors and environments to always accompany and assist its user to support a far more continuous, personalized and collaborative experience. This opens the question of what properties of a conversational AI agent migrates across forms, and how it would impact user perception. To explore this, we developed a Migratable AI system where a user's information and/or the agent's identity can be preserved as it migrates across form factors to help its user with a task. We validated the system by designing a 2x2 between-subjects study to explore the effects of information migration and identity migration on user perceptions of trust, competence, likeability and social presence. Our results suggest that identity migration had a positive effect on trust, competence and social presence, while information migration had a positive effect on trust, competence and likeability. Overall, users report highest trust, competence, likeability and social presence towards the conversational agent when both identity and information were migrated across embodiments.

Cloud architecture has become a valuable solution for different applications, such as big data analytics, due to its high degree of availability, scalability and strategic value. However, there still remain challenges in managing cloud architecture, in areas such as cloud security. In this paper, we exploit software-defined networking (SDN) and blockchain technologies to secure cloud management platforms from a networking perspective. We develop a blockchain-powered SDN-enabled networking infrastructure in which the integration between blockchain-based security and autonomy management layer and multi-controller SDN networking layer is defined to enhance the integrity of the control and management messages. Furthermore, our proposed networking infrastructure also enables the autonomous bandwidth provisioning to enhance the availability of cloud architecture. In the simulation section, we evaluate the performance of our proposed blockchain-powered SDN-enabled networking infrastructure by considering different scenarios.

Using erasure codes increases consumption of network traffic and disk I/O tremendously when systems recover data, resulting in high latency of degraded reads. In order to mitigate this problem, we present an adaptive storage scheme based on data access skew, a fact that most data accesses are applied in a small fraction of data. In this scheme, we use both Local Reconstruction Code (LRC), whose recovery cost is low, to store frequently accessed data, and Hitchhiker (HH) code, which guarantees minimum storage cost, to store infrequently accessed data. Besides, an efficient switching algorithm between LRC and HH code with low network and computation costs is provided. The whole system will benefit from low degraded read latency while keeping a low storage overhead, and code-switching will not become a bottleneck.

The following topics are dealt with: formal verification; formal specification; cyber-physical systems; program verification; mobile robots; control engineering computing; temporal logic; security of data; Internet of Things; traffic engineering computing.

Using steganography for data hiding is becoming a main subject to ensure both information security and picture quality. Traditional steganography algorithms usually convert secret information into a binary string and embed it in the pixel data of the cover image. In order to ensure the information security as well as convenient transmission, this work studies the steganography algorithm of embedding the QR code containing secret information into the cover image, based on the JSteg algorithm. Secret messages with different sizes have been tested by many cover images and standard parameters have adopted to verify the efficiency. According to the experimental results, all the PSNR in a value that is greater than 47.6 dB. The proposed method has high security and more imperceptibility.

The objective of the project is to structure a portable application for inspecting and putting away information through a distributed storage administration. The information is remotely put away in the cloud. In some distributed storage frameworks, the cloud record may contain some touchy data. Scrambling the entire shared record doesn't permit different clients to get to it. In the current framework the client needs to use the biometric information to guarantee the character of the client and afterward a marking key will be checked which is to ensure the personality and security of the client. The principle downside of utilizing the biometric information is that it can't be coordinated precisely because of the elements that influence the difference in biometric information. A reference id made is naturally changed over to the QR code and it is checked utilizing a scanner and the specific record is downloaded. This record whenever erased or lost in the customer's inward stockpiling it very well may be recovered again from the cloud.

In order to solve the statistical fluctuation problem caused by the finite data length in the practical quantum key distribution system, four commonly used statistical methods, DeMoivre-Laplace theorem, Chebyshev inequality, Chernoff boundary and Hoeffding boundary, are used to analyze. The application conditions of each method are discussed, and the effects of data length and confidence level on quantum key distribution security performance are simulated and analyzed. The simulation results show that the applicable conditions of Chernoff boundary are most consistent with the reality of the practical quantum key distribution system with finite-length data. Under the same experimental conditions, the secure key generation rate and secure transmission distance obtained by Chernoff boundary are better than those of the other three methods. When the data length and confidence level change, the stability of the security performance obtained by the Chernoff boundary is the best.

A novel efficiency-shifting radial-axial hybrid permanent magnet synchronous motor that can realize two high-efficiency regions at low and high speeds is developed to extend the maximum driving distance and track the reference speed more accurately for electric vehicle application. The motor has two stators, which are radial and axial, to rotate one shared rotor. The rotor employs two combined topologies, i.e., inner surface-inset-mounted and outer V-shaped interior-mounted. For both outer and inner permanent magnets, Nd-Fe-B, having the remanent flux density of 1.23 T and coercivity of 890 kA/m, is used. The simulation result shows that the designed motor exhibits not only high maximum torque of 400 Nm and the maximum speed of 18,000 rpm but also two high-efficiency regions of 97.6 % and 92.0 % at low and high speed, respectively. Lastly, the developed motor shows better performance than corresponding separated radial and axial permanent magnet motor.

Interface transformer used in asynchronous networking was a kind of special transformer which's different from normal power transformer. During no-load switch-in, the magnitude of inrush current will be high, and the waveform distortion also be severity. Maybe the protections will be activated, even worse may lead the lockdown of the DC system. In this paper, field-circuit coupled finite element method was used for the study of transient characteristic of no-load switch-in, remanence simulation methods were presented. Quantitative analysis of the effect of closing making angle and core remanence on inrush current peak value, meanwhile, the distribution of magnetic field inside the tank during the transient process. The result indicated that the closing making angle and core remanence have obvious effect on inrush current peak value. The research results of this paper can be used to guide the formulation of no-load switch-in strategy of interface transformer, which was of great significance to ensure the smooth operation of HVDC Flexible system.

Cloud computing is applied in various domains, among which social recommender systems are well-received because of their effectivity to provide suggestions for users. Social recommender systems perform well in alleviating cold start problem, but it suffers from shilling attack due to its natural openness. Shilling attack is an injection attack mainly acting on the training process of machine learning, which aims to advance or suppress the recommendation ranking of target items. Some researchers have studied the influence of shilling attacks in two perspectives simultaneously, which are user-item's rating and user-user's relation. However, they take more consideration into user-item's rating, and up to now, the construction of user-user's relation has not been explored in depth. To explore shilling attacks with complex relations, in this paper, we propose two novel attack models based on triangle relations in social networks. Furthermore, we explore the influence of these models on five social recommendation algorithms. The experimental results on three datasets show that the recommendation can be affected by the triangle relation attacks. The attack model combined with triangle relation has a better attack effect than the model only based on rating injection and the model combined with random relation. Besides, we compare the functions of triangle relations in friend recommendation and product recommendation.

With the development of the Internet, the amount of information on the Internet is increasing rapidly, which makes it difficult for people to select the information they really want. A recommendation system is an effective way to solve this problem. Fake users can be injected by criminals to attack the recommendation system; therefore, accurate identification of fake users is a necessary feature of the recommendation system. Existing fake user detection algorithms focus on designing recognition methods for different types of attacks and have limited detection capabilities against unknown or hybrid attacks. The use of deep learning models can automate the extraction of false user scoring features, but neural network models are not applicable to discrete user scoring data. In this paper, random walking is used to rearrange the otherwise discrete user rating data into a rating feature matrix with spatial continuity. The rating data and the text data have some similarity in the distribution mode. By effective analogy, the TextCNN model originally used in NLP domain can be improved and applied to the classification task of rating feature matrix. Combining the ideas of random walking and word vector processing, this paper proposes a TextCNN detection model for user rating data. To verify the validity of the proposed model, the model is tested on MoiveLens dataset against 7 different attack detection algorithms, and exhibits better performance when compared with 4 attack detection algorithms. Especially for the Aop attack, the proposed model has nearly 100% detection performance with F1 - value as the evaluation index.

This paper puts forward a formalized method of emergency plan based on ontology, sums up the main concepts such as system, event, rule, measure, constraint and resource, and analyzes the logical relationship among concepts. A digital intelligent emergency plan storage scheme based on relational database model is proposed. In this paper, full-text search, data search and knowledge search are comprehensively used to adapt to the information needs and characteristics of different users' query plans. Finally, an example of emergency plan made by a power supply company is given to illustrate the effectiveness of the method.

The solution of using existing WiFi devices for measurement and maintenance, and establishing a WiFi fingerprint database for precise localization has become a popular method for indoor localization. The traditional WiFi fingerprint privacy protection scheme increases the calculation amount of the client, but cannot completely protect the security of the client and the fingerprint database. In this paper, we make use of WiFi devices to present a Practical Privacy Protection Scheme In WiFi Fingerprint-based Localization PPWFL. In PPWFL, the localization server establishes a pre-partition in the fingerprint database through the E-M clustering algorithm, we divide the entire fingerprint database into several partitions. The server uses WiFi fingerprint entries with partitions as training data and trains a machine learning model. This model can accurately predict the client's partition based on fingerprint entries. The client uses the trained machine learning model to obtain its partition location accurately, picks up WiFi fingerprint entries in its partition, and calculates its geographic location with the localization server through secure multi-party computing. Compared with the traditional solution, our solution only uses the WiFi fingerprint entries in the client's partition rather than the entire fingerprint database. PPWFL can reduce not only unnecessary calculations but also avoid accidental errors (Unexpected errors in fingerprint similarity between non-adjacent locations due to multipath effects of electromagnetic waves during the propagation of complex indoor environments) in fingerprint distance calculation. In particular, due to the use of Secure Multi-Party Computation, most of the calculations are performed in the local offline phase, the client only exchanges data with the localization server during the distance calculation phase. No additional equipment is needed; our solution uses only existing WiFi devices in the building to achieve fast localization based on privacy protection. We prove that PPWFL is secure under the honest but curious attacker. Experiments show that PPWFL achieves efficiency and accuracy than the traditional WiFi fingerprint localization scheme.

The ever-growing security issues in various mobile applications and smart devices create an urgent demand for a reliable and convenient user verification method. Traditional verification methods request users to provide their secrets (e.g., entering passwords and collecting fingerprints). We envision that the essential trend of user verification is to free users from active participation in the verification process. Toward this end, we propose a continuous user verification system, which re-uses the widely deployed WiFi infrastructure to capture the unique physiological characteristics rooted in user's respiratory motions. Different from the existing continuous verification approaches, posing dependency on restricted scenarios/user behaviors (e.g., keystrokes and gaits), our system can be easily integrated into any WiFi infrastructure to provide non-intrusive continuous verification. Specifically, we extract the respiration-related signals from the channel state information (CSI) of WiFi. We then derive the user-specific respiratory features based on the waveform morphology analysis and fuzzy wavelet transformation of the respiration signals. Additionally, a deep learning based user verification scheme is developed to identify legitimate users accurately and detect the existence of spoofing attacks. Extensive experiments involving 20 participants demonstrate that the proposed system can robustly verify/identify users and detect spoofers under various types of attacks.