Biblio

In this paper, we study the security and system congestion in a risk-based checkpoint screening system with two kinds of inspection queues, named as Selectee Lanes and Normal Lanes. Based on the assessed threat value, the arrival crossing the security checkpoints is classified as either a selectee or a non-selectee. The Selectee Lanes with enhanced scrutiny are used to check selectees, while Normal Lanes are used to check non-selectees. The goal of the proposed modelling framework is to minimize the system congestion under the constraints of total security and limited budget. The system congestion of the checkpoint screening system is determined through a steady-state analysis of multi-server queueing models. By solving an optimization model, we can determine the optimal threshold for differentiating the arrivals, and determine the optimal number of security devices for each type of inspection queues. The analysis conducted in this study contributes managerial insights for understanding the operation and system performance of such risk-based checkpoint screening systems.

There are relatively fewer studies on the security-check waiting lines for screening cargo containers using queueing models. In this paper, we address two important measures at a security-check system, which are concerning the security screening effectiveness and the efficiency. The goal of this paper is to provide a modelling framework to understand the economic trade-offs embedded in container-inspection decisions. In order to analyze the policy initiatives, we develop a stylized queueing model with the novel features pertaining to the security checkpoints.

With the development of artificial intelligence, facial expression recognition (FER) has greatly improved performance in deep learning, but there is still a lot of room for improvement in the study of combining attention to focus the network on key parts of the face. For facial expression recognition, this paper designs a network model, which use spatial transformer network to transform the input image firstly, and then adding channel attention and spatial attention to the convolutional network. In addition, in this paper, the GELU activation function is used in the convolutional network, which improves the recognition rate of facial expressions to a certain extent.

Since the cyber and physical layers in the distribution system are deeply integrated, the traditional distribution system has gradually developed into the cyber-physical distribution system (CPDS), and the failures of the cyber layer will affect the reliable and safe operation of the whole distribution system. Therefore, this paper proposes an CPDS planning method considering the reliability of the cyber-physical system. First, the reliability evaluation model of CPDS is proposed. Specifically, the functional reliability model of the cyber layer is introduced, based on which the physical equipment reliability model is further investigated. Second, an optimal planning model of CPDS considering cyber-physical random failures is developed, which is solved using the Monte Carlo Simulation technique. The proposed model is tested on the modified IEEE 33-node distribution system, and the results demonstrate the effectiveness of the proposed method.

The proliferation of wearable devices, e.g., smartwatches and activity trackers, with embedded sensors has already shown its great potential on monitoring and inferring human daily activities. This paper reveals a serious security breach of wearable devices in the context of divulging secret information (i.e., key entries) while people accessing key-based security systems. Existing methods of obtaining such secret information relies on installations of dedicated hardware (e.g., video camera or fake keypad), or training with labeled data from body sensors, which restrict use cases in practical adversary scenarios. In this work, we show that a wearable device can be exploited to discriminate mm-level distances and directions of the user's fine-grained hand movements, which enable attackers to reproduce the trajectories of the user's hand and further to recover the secret key entries. In particular, our system confirms the possibility of using embedded sensors in wearable devices, i.e., accelerometers, gyroscopes, and magnetometers, to derive the moving distance of the user's hand between consecutive key entries regardless of the pose of the hand. Our Backward PIN-Sequence Inference algorithm exploits the inherent physical constraints between key entries to infer the complete user key entry sequence. Extensive experiments are conducted with over 5000 key entry traces collected from 20 adults for key-based security systems (i.e. ATM keypads and regular keyboards) through testing on different kinds of wearables. Results demonstrate that such a technique can achieve 80% accuracy with only one try and more than 90% accuracy with three tries, which to our knowledge, is the first technique that reveals personal PINs leveraging wearable devices without the need for labeled training data and contextual information.

Mobile payment has drawn considerable attention due to its convenience of paying via personal mobile devices at anytime and anywhere, and passcodes (i.e., PINs) are the first choice of most consumers to authorize the payment. This work demonstrates a serious security breach and aims to raise the awareness of the public that the passcodes for authorizing transactions in mobile payments can be leaked by exploiting the embedded sensors in wearable devices (e.g., smartwatches). We present a passcode inference system, which examines to what extent the user's PIN during mobile payment could be revealed from a single wrist-worn wearable device under different input scenarios involving either two hands or a single hand. Extensive experiments with 15 volunteers demonstrate that an adversary is able to recover a user's PIN with high success rate within 5 tries under various input scenarios.

Mobile payment has drawn considerable attention due to its convenience of paying via personal mobile devices at anytime and anywhere, and passcodes (i.e., PINs or patterns) are the first choice of most consumers to authorize the payment. This paper demonstrates a serious security breach and aims to raise the awareness of the public that the passcodes for authorizing transactions in mobile payments can be leaked by exploiting the embedded sensors in wearable devices (e.g., smartwatches). We present a passcode inference system, WristSpy, which examines to what extent the user's PIN/pattern during the mobile payment could be revealed from a single wrist-worn wearable device under different passcode input scenarios involving either two hands or a single hand. In particular, WristSpy has the capability to accurately reconstruct fine-grained hand movement trajectories and infer PINs/patterns when mobile and wearable devices are on two hands through building a Euclidean distance-based model and developing a training-free parallel PIN/pattern inference algorithm. When both devices are on the same single hand, a highly challenging case, WristSpy extracts multi-dimensional features by capturing the dynamics of minute hand vibrations and performs machine-learning based classification to identify PIN entries. Extensive experiments with 15 volunteers and 1600 passcode inputs demonstrate that an adversary is able to recover a user's PIN/pattern with up to 92% success rate within 5 tries under various input scenarios.

Web applications, as a conventional platform for sensitive data and important transactions, are of great significance to human society. But with its open source framework, the existing security vulnerabilities can easily be exploited by malicious users, especially when web developers fail to follow the secure practices. Here we present a distributed scanning system, FalconEye, with great precision and high performance, it will help prevent potential threats to Web applications. Besides, our system is also capable of covering basically all the web vulnerabilities registered in the Common Vulnerabilities and Exposures (CVE). The FalconEye system is consists of three modules, an input source module, a scanner module and a support platform module. The input module is used to improve the coverage of target server, and other modules make the system capable of generic vulnerabilities scanning. We then experimentally demonstrate this system in some of the most common vulnerabilities test environment. The results proved that the FalconEye system can be a strong contender among the various detection systems in existence today.

Electromagnetic (EM) analysis is to reveal the secret information by analyzing the EM emission from a cryptographic device. EM analysis (EMA) attack is emerging as a serious threat to hardware security. It has been noted that the on-chip power grid (PG) has a security implication on EMA attack by affecting the fluctuations of supply current. However, there is little study on exploiting this intrinsic property as an active countermeasure against EMA. In this paper, we investigate the effect of PG on EM emission and propose an active countermeasure against EMA, i.e. EM Equalizer (EME). By adjusting the PG impedance, the current waveform can be flattened, equalizing the EM profile. Therefore, the correlation between secret data and EM emission is significantly reduced. As a first attempt to the co-optimization for power and EM security, we extend the EME method by fixing the vulnerability of power analysis. To verify the EME method, several cryptographic designs are implemented. The measurement to disclose (MTD) is improved by 1138x with area and power overheads of 0.62% and 1.36%, respectively.

The security of energy supply in a power grid critically depends on the ability to accurately estimate the state of the system. However, manipulated power flow measurements can potentially hide overloads and bypass the bad data detection scheme to interfere the validity of estimated states. In this paper, we use an autoencoder neural network to detect anomalous system states and investigate the impact of hyperparameters on the detection performance for false data injection attacks that target power flows. Experimental results on the IEEE 118 bus system indicate that the proposed mechanism has the ability to achieve satisfactory learning efficiency and detection accuracy.

State estimation is of considerable significance for the power system operation and control. However, well-designed false data injection attacks can utilize blind spots in conventional residual-based bad data detection methods to manipulate measurements in a coordinated manner and thus affect the secure operation and economic dispatch of grids. In this paper, we propose a detection approach based on an autoencoder neural network. By training the network on the dependencies intrinsic in `normal' operation data, it effectively overcomes the challenge of unbalanced training data that is inherent in power system attack detection. To evaluate the detection performance of the proposed mechanism, we conduct a series of experiments on the IEEE 118-bus power system. The experiments demonstrate that the proposed autoencoder detector displays robust detection performance under a variety of attack scenarios.

With the maturity of Internet of things technology, massive data transmission has become the focus of research. In order to solve the problem of low speed of traditional hybrid data fast distribution algorithm for wireless sensor networks, a hybrid data fast distribution algorithm for wireless sensor networks based on visual Internet of things is designed. The logic structure of mixed data input gate in wireless sensor network is designed through the visual Internet of things. The objective function of fast distribution of mixed data in wireless sensor network is proposed. The number of copies of data to be distributed is dynamically calculated and the message deletion strategy is determined. Then the distribution parameters are calibrated, and the fitness ranking is performed according to the distribution quantity to complete the algorithm design. The experimental results show that the distribution rate of the designed algorithm is significantly higher than that of the control group, which can solve the problem of low speed of traditional data fast distribution algorithm.

With the development of deep learning technology, a large number of new technologies for video anomaly detection have emerged. This paper proposes a video anomaly detection algorithm based on the future frame prediction using Generative Adversarial Network (GAN) and attention mechanism. For the generation model, a U-Net model, is modified and added with an attention module. For the discrimination model, a Markov GAN discrimination model with self-attention mechanism is proposed, which can affect the generator and improve the generation quality of the future video frame. Experiments show that the new video anomaly detection algorithm improves the detection performance, and the attention module plays an important role in the overall detection performance. It is found that the more the attention modules are appliedthe deeper the application level is, the better the detection effect is, which also verifies the rationality of the model structure used in this project.

We consider the problem of preserving the privacy of a set of private parameters while allowing inference of a set of public parameters based on observations from sensors in a network. We assume that the public and private parameters are correlated with the sensor observations via a linear model. We define the utility loss and privacy gain functions based on the Cramér-Rao lower bounds for estimating the public and private parameters, respectively. Our goal is to minimize the utility loss while ensuring that the privacy gain is no less than a predefined privacy gain threshold, by allowing each sensor to perturb its own observation before sending it to the fusion center. We propose methods to determine the amount of noise each sensor needs to add to its observation under the cases where prior information is available or unavailable.

We consider the problem of preserving the privacy of a set of private parameters while allowing inference of a set of public parameters based on observations from sensors in a network. We assume that the public and private parameters are correlated with the sensor observations via a linear model. We define the utility loss and privacy gain functions based on the Cramér-Rao lower bounds for estimating the public and private parameters, respectively. Our goal is to minimize the utility loss while ensuring that the privacy gain is no less than a predefined privacy gain threshold, by allowing each sensor to perturb its own observation before sending it to the fusion center. We propose methods to determine the amount of noise each sensor needs to add to its observation under the cases where prior information is available or unavailable.

In the era of big data, more and more users store data in the cloud. Massive amounts of data have brought huge storage costs to cloud storage providers, and data deduplication technology has emerged. In order to protect the confidentiality of user data, user data should be encrypted and stored in the cloud. Therefore, deduplication of encrypted data has become a research hotspot. Cloud storage provides users with data sharing services, and the sharing of encrypted data is another research hotspot. The combination of encrypted data deduplication and sharing will inevitably become a future trend. The current better-performing updateable block-level message-locked encryption (UMLE) deduplication scheme does not support data sharing, and the performance of the encrypted data de-duplication scheme that introduces data sharing is not as good as that of UMLE. This paper introduces the ciphertext policy attribute based encryption (CP-ABE) system sharing mechanism on the basis of UMLE, applies the CP-ABE method to encrypt the master key generated by UMLE, to achieve secure and efficient data deduplication and sharing. In this paper, we propose a permission verification method based on bilinear mapping, and according to the definition of the security model proposed in the security analysis phase, we prove this permission verification method, showing that our scheme is secure. The comparison of theoretical analysis and simulation experiment results shows that this scheme has more complete functions and better performance than existing schemes, and the proposed authorization verification method is also secure.

HTTPS provides authentication, data confidentiality, and integrity for secure web applications in the Internet. In order to establish secure connections with the target website but not a man-in-the-middle or impersonation attacker, a browser shows security warnings to users, when different HTTPS errors happen (e.g., it fails to build a valid certificate chain, or the certificate subject does not match the domain visited). Each browser implements its own design of warnings on HTTPS errors, to balance security and usability. This paper presents a list of common HTTPS errors, and we investigate the browser behaviors on each error. Our study discloses browser defects on handling HTTPS errors in terms of cryptographic algorithm, certificate verification, name validation, HPKP, and HSTS.

The communication security issue is of great importance and should not be ignored in backbone optical networks which is undergoing the evolution toward software defined networks (SDN). With the aim to solve this problem, this paper conducts deep analysis into the security challenge of software defined optical networks (SDON) and proposes a so-called security-enhanced signaling scheme of SDON. The proposed scheme makes full advantage of current OpenFIow protocol with some necessary extensions and security improvement, by combining digital signatures and message feedback with efficient PKI (Public Key Infrastructure) in signaling procedure of OpenFIow interaction. Thus, this security-enhanced signaling procedure is also designed in details to make sure the end-to-end trusted service connection. Simulation results show that this proposed approach can greatly improve the security level of large-scale optical network for Energy Internet services with better performance in term of connection success rate performance.

Network security is a prominent topic that is gaining international attention. Distributed Denial of Service (DDoS) attack is often regarded as one of the most serious threats to network security. Software Defined Network (SDN) decouples the control plane from the data plane, which can meet various network requirements. But SDN can also become the object of DDoS attacks. This paper proposes an automated DDoS attack mitigation method that is based on the programmability of the Ryu controller and the features of the OpenFlow switch flow tables. The Mininet platform is used to simulate the whole process, from SDN traffic generation to using a K-Nearest Neighbor model for traffic classification, as well as identifying and mitigating DDoS attack. The packet counts of the victim's malicious traffic input port are significantly lower after the mitigation method is implemented than before the mitigation operation. The purpose of mitigating DDoS attack is successfully achieved.

Cognitive acoustic (CA) is emerging as a promising technique for spectrum-efficient Underwater Acoustic Networks (UANs). Due to the unique features of UANs, especially the long propagation delay, the busy terminal problem and large interference range, traditional spectrum decision methods used for radio networks need an overhaul to work efficiently in underwater environment. In this paper, we propose a dynamic spectrum decision algorithm called Receiver-viewed Dynamic Borrowing (RvDB) algorithm for Underwater Cognitive Acoustic Networks (UCANs) to improve the efficiency of spectrum utilization. RvDB algorithm is with the following features. Firstly, the spectrum resource is decided by receiver. Secondly, the receivers can borrow the idle spectrum resource from neighbouring nodes dynamically. Finally, the spectrum sensing is completed by control packets on control channel which is separated from data channels. Simulation results show that RvDB algorithm can greatly improve the performance on spectrum efficiency.

Supervisory control and data acquisition system is an important part of the country's critical infrastructure, but its inherent network characteristics are vulnerable to attack by intruders. The vulnerability of supervisory control and data acquisition system was analyzed, combining common attacks such as information scanning, response injection, command injection and denial of service in industrial control systems, and proposed an intrusion detection model based on graphical features. The time series of message transmission were visualized, extracting the vertex coordinates and various graphic area features to constitute a new data set, and obtained classification model of intrusion detection through training. An intrusion detection experiment environment was built using tools such as MATLAB and power protocol testers. IEC 60870-5-104 protocol which is widely used in power systems had been taken as an example. The results of tests have good effectiveness.

IoT device usually has an associated application to facilitate customers' interactions with the device, and customers need to register an account to use this application as well. Due to the popularity of mobile phone, a customer is encouraged to register an account with his own mobile phone number. After binding the device to his account, the customer can control his device remotely with his smartphone. When a customer forgets his password, he can use his mobile phone to receive a verification code that is sent by the Short Message Service (SMS) to authenticate and reset his password. If an attacker gains this code, he can steal the victim's account (reset password or login directly) to control the IoT device. Although IoT device vendors have already deployed a set of security countermeasures to protect account such as setting expiration time for SMS authentication code, HTTP encryption, and application packing, this paper shows that existing IoT account password reset via SMS authentication code are still vulnerable to brute-force attacks. In particular, we present an automatic brute-force attack to bypass current protections and then crack IoT device user account. Our preliminary study on popular IoT devices such as smart lock, smart watch, smart router, and sharing car has discovered six account login zero-day vulnerabilities.

As we all know, computer network security evaluation is an important link in the field of network security. Traditional computer network security evaluation methods use BP neural network combined with network security standards to train and simulate. However, because BP neural network is easy to fall into local minimum point in the training process, the evalu-ation results are often inaccurate. In this paper, the LM (Levenberg-Marquard) algorithm is used to optimize the BP neural network. The LM-BP algorithm is constructed and applied to the computer network security evaluation. The results show that compared with the traditional evaluation algorithm, the optimized neural network has the advantages of fast running speed and accurate evaluation results.

Recently, graph-based malware similarity analysis has been widely used in the field of malware detection. However, the wide application of code obfuscation, polymorphism, and deformation changes the structure of malicious code, which brings great challenges to the malware similarity analysis. To solve these problems, in this paper, we present a new approach to malware similarity analysis based on the network control structure graph (NCSG). This method analyzed the behavior of malware by application program interface (API) association and constructed NCSG. The graph could reflect the command-and-control(C&C) logic of malware. Therefore, it can resist the interference of code obfuscation technology. The structural features extracted from NCSG will be used as the basis of similarity analysis for training the detection model. Finally, we tested the dataset constructed from five known malware family samples, and the experimental results showed that the accuracy of this method for malware variation analysis reached 92.75%. In conclusion, the malware similarity analysis based on NCSG has a strong application value for identifying the same family of malware.

The Controller Area Network (CAN) is a widely used industry-standard intra-vehicle broadcast network that connects the Electronic Control Units (ECUs) which control most car systems. The CAN contains substantial vulnerabilities that can be exploited by attackers to gain control of the vehicle, due to its lack of security measures. To prevent an attacker from sending malicious messages through the CAN bus to take over a vehicle, we propose the addition of a secure hardware-based module, or Security ECU (SECU), onto the CAN bus. The SECU can perform key distribution and message verification, as well as corrupting malicious messages before they are fully received by an ECU. Only software modification is needed for existing ECUs, without changing the CAN protocol. This provides backward compatibility with existing CAN systems. Furthermore, we collect 6.673 million CAN bus messages from various cars, and find that the CAN messages collectively have low entropy, with an average of 11.915 bits. This finding motivates our proposal for CAN bus message compression, which allows us to significantly reduce message size to fit the message and its message authentication code (MAC) within one CAN frame, enabling fast authentication. Since ECUs only need to generate the MACs (and not verify them), the delay and computation overhead are also reduced compared to traditional authentication mechanisms. Our authentication mechanism is implemented on a realistic testbed using industry standard MCP2551 CAN transceivers and Raspberry Pi embedded systems. Experimental results demonstrate that our mechanism can achieve real-time message authentication on the CAN bus with minimal latency.