A method of detecting UHF RFID tags with SQL in-jection virus code written in its user memory bank is explored. A spectrum analyzer took signal strength readings in the frequency spectrum while an RFID reader was reading the tag. The strength of the signal transmitted by the RFID tag in the UHF range, more specifically within the 902–908 MHz sub-band, was used as data to train a Random Forest model for Malware detection. Feature reduction is accomplished by dividing the observed spectrum into 15 ranges with a bandwidth of 344 kHz each and detecting the number of maxima in each range. The malware-infested tag could be detected more than 80% of the time. The frequency ranges contributing most in this detection method were the low (903.451-903.795 MHz, 902.418-902.762 MHz) and high (907.238-907.582 MHz) bands in the observed spectrum.

The Internet of Things (IoT) is rapidly evolving, allowing physical items to share information and coordinate with other nodes, increasing IoT’s value and being widely applied to various applications. Radio Frequency Identification (RFID) is usually used in IoT applications to automate item identification by establishing symmetrical communication between the tag device and the reader. Because RFID reading data is typically in plain text, a security mechanism is required to ensure that the reading results from this RFID data remain confidential. Researchers propose a lightweight encryption algorithm framework for IoT-based RFID applications to address this security issue. Furthermore, this research assesses the implementation of lightweight encryption algorithms, such as Grain v1 and Espresso, as two systems scenarios. The Grain v1 encryption is the final eSTREAM project that accepts an 80-bit key, 64-bit IV, and has a 160-bit internal state with limited application. In contrast, the Espresso algorithm has been implemented in various applications such as 5G wireless communication. Furthermore, this paper tested the performance of each encryption algorithm in the microcontroller and inspected the network performance in an IoT system.

A single RFID (Radio Frequency Identification) is a technology for the remote identification of objects or people. It integrates a reader that receives the information contained in an RFID tag through an RFID authentication protocol. RFID provides several security services to protect the data transmitted between the tag and the reader. However, these advantages do not prevent an attacker to access this communication and remaining various security and privacy issues in these systems. Furthermore, with the rapid growth of IoT, there is an urgent need of security authentication and confidential data protection. Authentication protocols based on elliptic curve cryptographic (ECC) were widely investigated and implemented to guarantee protection against the various attacks that can suffer an RFID system. In this paper, we are going to focus on a comparative study between the most efficient ECC-based RFID authentication protocols that are already published, and study their security against the different wireless attacks.

In the operation of information technology (IT) services, operators monitor the equipment-issued alarms, to locate the cause of a failure and take action. Alarms generate simultaneously from multiple devices with physical/logical connections. Therefore, if the time and location of the alarms are close to each other, it can be judged that the alarms are likely to be caused by the same event. In this paper, we propose a method that takes a novel approach by correlating alarms considering event units using a Bayesian network based on alarm generation time, generation place, and alarm type. The topology information becomes a critical decision element when doing the alarm correlation. However, errors may occur when topology information updates manually during failures or construction. Therefore, we show that event-by-event correlation with 100% accuracy is possible even if the topology information is 25% wrong by taking into location information other than topology information.

The selection of distribution network faults is of great significance to accurately identify the fault location, quickly restore power and improve the reliability of power supply. This paper mainly studies the fault phase selection method of distribution network based on wavelet singular entropy and deep belief network (DBN). Firstly, the basic principles of wavelet singular entropy and DBN are analyzed, and on this basis, the DBN model of distribution network fault phase selection is proposed. Firstly, the transient fault current data of the distribution network is processed to obtain the wavelet singular entropy of the three phases, which is used as the input of the fault phase selection model; then the DBN network is improved, and an artificial neural network (ANN) is introduced to make it a fault Select the phase classifier, and specify the output label; finally, use Simulink to build a simulation model of the IEEE33 node distribution network system, obtain a large amount of data of various fault types, generate a training sample library and a test sample library, and analyze the neural network. The adjustment of the structure and the training of the parameters complete the construction of the DBN model for the fault phase selection of the distribution network.

VCB is an important component to ensure the safe and smooth operation of the power system. As an important driving part of the vacuum circuit breaker, the operating mechanism is prone to mechanical failure, which leads to power grid accidents. This paper offers an in-depth analysis of the mechanical faults of the operating mechanism of vacuum circuit breaker and their causes, extracts the current signal of the opening and closing coil strongly correlated with the mechanical faults of the operating mechanism as the characteristic information to build a Deep Belief Network (DBN) model, trains each data set via Restricted Boltzmann Machine(RBM) and updates the model parameters. The number of hidden layer nodes, the structure of the network layer, and the learning rate are determined, and the mechanical fault diagnosis system of vacuum circuit breaker based on the Deep Belief Network is established. The results show that when the network structure is 8-110-110-6 and the learning rate is 0.01, the recognition accuracy of the DBN model is the highest, which is 0.990871. Compared with BP neural network, DBN has a smaller cross-entropy error and higher accuracy. This method can accurately diagnose the mechanical fault of the vacuum circuit breaker, which lays a foundation for the smooth operation of the power system.

In view of the characteristics that rolling bearing is prone to failure under actual working conditions, and it is difficult to classify the fault category and fault degree, the deep belief network is introduced to diagnose the rolling bearing fault. Firstly, principal component analysis is used to reduce the dimension of original input data and delete redundant input information. Then, the dimension reduced data are input into the deep belief network to extract the low dimensional fault feature representation, and the extracted features are input into the classifier for rolling bearing fault pattern recognition. Finally, the diagnosis effect of the proposed network is compared with the existing common shallow neural network. The simulation experiment is carried out through the bearing data in the United States.

Higher education management has problems producing 100% of graduates capable of responding to the needs of industry while industry also is struggling to find qualified graduates that responded to their needs in part because of the inefficient way of evaluating problems, as well as because of weaknesses in the evaluation of problem-solving capabilities. The objective of this paper is to propose an appropriate classification model to be used for predicting and evaluating the attributes of the data set of the student in order to meet the selection criteria required by the industries in the academic field. The dataset required for this analysis was obtained from a private firm and the execution was carried out using Chimp Optimization Algorithm (COA) based Deep Belief Neural Network (COA-DBNN) and the obtained results are compared with various classifiers such as Logistic Regression (LR), Decision Tree (DT) and Random Forest (RF). The proposed model outperforms other classifiers in terms of various performance metrics. This critical analysis will help the college management to make a better long-term plan for producing graduates who are skilled, knowledgeable and fulfill the industry needs as well.

Historically, energy resources are of strategic importance for the social welfare and economic growth. So, predicting crude oil price fluctuations is an important issue. Since crude oil price changes are affected by many risk factors in markets, this price shows more complicated nonlinear behavior and creates more risk levels for investors than in the past. We propose a new method of prediction of crude oil price to model nonlinear dynamics. The results of the experiments show that the superior performance of the model based on the proposed method against statistical previous works is statistically significant. In general, we found that the combination of the IDBN or LSTM model lowered the MSE value to 4.65, which is 0.81 lower than the related work (Chen et al. protocol), indicating an improvement in prediction accuracy.

The topological structure of the network relationship is described by the network diagram, and the formation and evolution process of the network is analyzed by using the cost-benefit method. Assuming that the self-interested network member nodes can connect or break the connection, the network topology model is established based on the dynamic random pairing evolution network model. The static structure of the network is studied. Respecting the psychological cognition law of college students and innovating the core value cultivation model can reverse the youth's identification dilemma with the core values, and then create a good political environment for the normal, healthy, civilized and orderly network participation of the youth. In recognition of the atmosphere, an automatic learning algorithm of Bayesian network structure that effectively integrates expert knowledge and data-driven methods is realized.

Aim: Object Detection is one of the latest topics in today’s world for detection of real time objects using Deep Belief Networks. Methods & Materials: Real-Time Object Detection is performed using Deep Belief Networks (N=24) over Convolutional Neural Networks (N=24) with the split size of training and testing dataset 70% and 30% respectively. Results: Deep Belief Networks has significantly better accuracy (81.2%) compared to Convolutional Neural Networks (47.7%) and attained significance value of p = 0.083. Conclusion: Deep Belief Networks achieved significantly better object detection than Convolutional Neural Networks for identifying real-time objects in traffic surveillance.

In recent years, radar automatic target recognition (RATR) technology based on high-resolution range profile (HRRP) has received extensive attention in various fields. However, insufficient data on non-cooperative targets seriously affects recognition performance of this technique. For HRRP target recognition under few-shot condition, we proposed a novel gaussian deep belief network based on model-agnostic meta-learning (GDBN-MAML). In the proposed method, GDBN allowed real-value data to be transmitted over the entire network, which effectively avoided feature loss due to binarization requirements of conventional deep belief network (DBN) for data. In addition, we optimized the initial parameters of GDBN by multi-task learning based on MAML. In this way, the number of training samples required by the model for new recognition tasks could be reduced. We applied the proposed method to the HRRP recognition experiments of 3 types of 3D simulated aircraft models. The experimental results showed that the proposed method had higher recognition accuracy and generalization performance under few-shot condition compared with conventional deep learning methods.

To detect human behaviour and measure accuracy of classification rate. Materials and Methods: A novel deep belief network with sample size 10 and support vector machine with sample size of 10. It was iterated at different times predicting the accuracy percentage of human behaviour. Results: Human behaviour detection utilizing novel deep belief network 87.9% accuracy compared with support vector machine 87.0% accuracy. Deep belief networks seem to perform essentially better compared to support vector machines \$(\textbackslashmathrmp=0.55)(\textbackslashtextPiˆ0.05)\$. The deep belief algorithm in computer vision appears to perform significantly better than the support vector machine algorithm. Conclusion: Within this human behaviour detection novel deep belief network has more precision than support vector machine.

Automatic speech recognition (ASR) models are used widely in applications for voice navigation and voice control of domestic appliances. ASRs have been misused by attackers to generate malicious outputs by attacking the deep learning component within ASRs. To assess the security and robustnesss of ASRs, we propose techniques within our framework SPAT that generate blackbox (agnostic to the DNN) adversarial attacks that are portable across ASRs. This is in contrast to existing work that focuses on whitebox attacks that are time consuming and lack portability. Our techniques generate adversarial attacks that have no human audible difference by manipulating the input speech signal using a psychoacoustic model that maintains the audio perturbations below the thresholds of human perception. We propose a framework SPAT with three attack generation techniques based on the psychoacoustic concept and frame selection techniques to selectively target the attack. We evaluate portability and effectiveness of our techniques using three popular ASRs and two input audio datasets using the metrics- Word Error Rate (WER) of output transcription, Similarity to original audio, attack Success Rate on different ASRs and Detection score by a defense system. We found our adversarial attacks were portable across ASRs, not easily detected by a state-of the-art defense system, and had significant difference in output transcriptions while sounding similar to original audio.

Classic black-box adversarial attacks can take advantage of transferable adversarial examples generated by a similar substitute model to successfully fool the target model. However, these substitute models need to be trained by target models' training data, which is hard to acquire due to privacy or transmission reasons. Recognizing the limited availability of real data for adversarial queries, recent works proposed to train substitute models in a data-free black-box scenario. However, their generative adversarial networks (GANs) based framework suffers from the convergence failure and the model collapse, resulting in low efficiency. In this paper, by rethinking the collaborative relationship between the generator and the substitute model, we design a novel black-box attack framework. The proposed method can efficiently imitate the target model through a small number of queries and achieve high attack success rate. The comprehensive experiments over six datasets demonstrate the effectiveness of our method against the state-of-the-art attacks. Especially, we conduct both label-only and probability-only attacks on the Microsoft Azure online model, and achieve a 100% attack success rate with only 0.46% query budget of the SOTA method [49].

Black-box adversarial attack has aroused much research attention for its difficulty on nearly no available information of the attacked model and the additional constraint on the query budget. A common way to improve attack efficiency is to transfer the gradient information of a white-box substitute model trained on an extra dataset. In this paper, we deal with a more practical setting where a pre-trained white-box model with network parameters is provided without extra training data. To solve the model mismatch problem between the white-box and black-box models, we propose a novel algorithm EigenBA by systematically integrating gradient-based white-box method and zeroth-order optimization in black-box methods. We theoretically show the optimal directions of perturbations for each step are closely related to the right singular vectors of the Jacobian matrix of the pretrained white-box model. Extensive experiments on ImageNet, CIFAR-10 and WebVision show that EigenBA can consistently and significantly outperform state-of-the-art baselines in terms of success rate and attack efficiency.

Recent studies show that the state-of-the-art deep neural networks are vulnerable to model inversion attacks, in which access to a model is abused to reconstruct private training data of any given target class. Existing attacks rely on having access to either the complete target model (whitebox) or the model's soft-labels (blackbox). However, no prior work has been done in the harder but more practical scenario, in which the attacker only has access to the model's predicted label, without a confidence measure. In this paper, we introduce an algorithm, Boundary-Repelling Model Inversion (BREP-MI), to invert private training data using only the target model's predicted labels. The key idea of our algorithm is to evaluate the model's predicted labels over a sphere and then estimate the direction to reach the target class's centroid. Using the example of face recognition, we show that the images reconstructed by BREP-MI successfully reproduce the semantics of the private training data for various datasets and target model architectures. We compare BREP-MI with the state-of-the-art white-box and blackbox model inversion attacks, and the results show that despite assuming less knowledge about the target model, BREP-MI outperforms the blackbox attack and achieves comparable results to the whitebox attack. Our code is available online.11https://github.com/m-kahla/Label-Only-Model-Inversion-Attacks-via-Boundary-Repulsion

Adversarial attacks have recently been proposed to scrutinize the security of deep neural networks. Most blackbox adversarial attacks, which have partial access to the target through queries, are target-specific; e.g., they require a well-trained surrogate that accurately mimics a given target. In contrast, target-agnostic black-box attacks are developed to attack any target; e.g., they learn a generalized surrogate that can adapt to any target via fine-tuning on samples queried from the target. Despite their success, current state-of-the-art target-agnostic attacks require tremendous fine-tuning steps and consequently an immense number of queries to the target to generate successful attacks. The high query complexity of these attacks makes them easily detectable and thus defendable. We propose a novel query-efficient target-agnostic attack that trains a generalized surrogate network to output the adversarial directions iv.r.t. the inputs and equip it with an effective fine-tuning strategy that only fine-tunes the surrogate when it fails to provide useful directions to generate the attacks. Particularly, we show that to effectively adapt to any target and generate successful attacks, it is sufficient to fine-tune the surrogate with informative samples that help the surrogate get out of the failure mode with additional information on the target’s local behavior. Extensive experiments on CIFAR10 and CIFAR-100 datasets demonstrate that the proposed target-agnostic approach can generate highly successful attacks for any target network with very few fine-tuning steps and thus significantly smaller number of queries (reduced by several order of magnitudes) compared to the state-of-the-art baselines.

Most existing deep neural networks (DNNs) are inexplicable and fragile, which can be easily deceived by carefully designed adversarial example with tiny undetectable noise. This allows attackers to cause serious consequences in many DNN-assisted scenarios without human perception. In the field of speaker recognition, the attack for speaker recognition system has been relatively mature. Most works focus on white-box attacks that assume the information of the DNN is obtainable, and only a few works study gray-box attacks. In this paper, we study blackbox attacks on the speaker recognition system, which can be applied in the real world since we do not need to know the system information. By combining the idea of transferable attack and query attack, our proposed method NMI-FGSM-Tri can achieve the targeted goal by misleading the system to recognize any audio as a registered person. Specifically, our method combines the Nesterov accelerated gradient (NAG), the ensemble attack and the restart trigger to design an attack method that generates the adversarial audios with good performance to attack blackbox DNNs. The experimental results show that the effect of the proposed method is superior to the extant methods, and the attack success rate can reach as high as 94.8% even if only one query is allowed.

The widespread adoption of eCommerce, iBanking, and eGovernment institutions has resulted in an exponential rise in the use of web applications. Due to a large number of users, web applications have become a prime target of cybercriminals who want to steal Personally Identifiable Information (PII) and disrupt business activities. Hence, there is a dire need to audit the websites and ensure information security. In this regard, several web vulnerability scanners are employed for vulnerability assessment of web applications but attacks are still increasing day by day. Therefore, a considerable amount of research has been carried out to measure the effectiveness and limitations of the publicly available web scanners. It is identified that most of the publicly available scanners possess weaknesses and do not generate desired results. In this paper, the evaluation of publicly available web vulnerability scanners is performed against the top ten OWASP11OWASP® The Open Web Application Security Project (OWASP) is an online community that produces comprehensive articles, documentation, methodologies, and tools in the arena of web and mobile security. vulnerabilities and their performance is measured on the precision of their results. Based on these results, we proposed an Integrated Multi-Agent Blackbox Security Assessment Tool (SAT) for the security assessment of web applications. Research has proved that the vulnerabilities assessment results of the SAT are more extensive and accurate.

Speech recognition technology has been applied to all aspects of our daily life, but it faces many security issues. One of the major threats is the adversarial audio examples, which may tamper the recognition results of the acoustic speech recognition system (ASR). In this paper, we propose an adversarial detection framework to detect adversarial audio examples. The method is based on the transformer self-attention mechanism. Spectrogram features are extracted from the audio and divided into patches. Position information are embedded and then fed into transformer encoder. Experimental results show that the method achieves good performance with the detection accuracy of above 96.5% under the white-box attacks and blackbox attacks, and noisy circumstances. Even when detecting adversarial examples generated by the unknown attacks, it also achieves satisfactory results.

Co-salient object detection (CoSOD) has recently achieved significant progress and played a key role in retrieval-related tasks. However, it inevitably poses an entirely new safety and security issue, i.e., highly personal and sensitive content can potentially be extracting by powerful CoSOD methods. In this paper, we address this problem from the perspective of adversarial attacks and identify a novel task: adversarial co-saliency attack. Specially, given an image selected from a group of images containing some common and salient objects, we aim to generate an adversarial version that can mislead CoSOD methods to predict incorrect co-salient regions. Note that, compared with general white-box adversarial attacks for classification, this new task faces two additional challenges: (1) low success rate due to the diverse appearance of images in the group; (2) low transferability across CoSOD methods due to the considerable difference between CoSOD pipelines. To address these challenges, we propose the very first blackbox joint adversarial exposure and noise attack (Jadena), where we jointly and locally tune the exposure and additive perturbations of the image according to a newly designed high-feature-level contrast-sensitive loss function. Our method, without any information on the state-of-the-art CoSOD methods, leads to significant performance degradation on various co-saliency detection datasets and makes the co-salient objects undetectable. This can have strong practical benefits in properly securing the large number of personal photos currently shared on the Internet. Moreover, our method is potential to be utilized as a metric for evaluating the robustness of CoSOD methods.

Secured data transmission between one to many authorized users is achieved through Broadcast Encryption (BE). In BE, the source transmits encrypted data to multiple registered users who already have their decrypting keys. The Untrustworthy users, known as Traitors, can give out their secret keys to a hacker to form a pirate decoding system to decrypt the original message on the sly. The process of detecting the traitors is known as Traitor Tracing in cryptography. This paper presents a new Black Box Tracing method that is fully collusion resistant and it is designated as Traitor Tracing in Broadcast Encryption using Vector Keys (TTBE-VK). The proposed method uses integer vectors in the finite field Zp as encryption/decryption/tracing keys, reducing the computational cost compared to the existing methods.

Smart phones have become the preferred way for Chinese Internet users currently. The mobile phone traffic is large from the operating system. These traffic is mainly generated by the services. In the context of the universal encryption of the traffic, classification identification of mobile encryption services can effectively reduce the difficulty of analytical difficulty due to mobile terminals and operating system diversity, and can more accurately identify user access targets, and then enhance service quality and network security management. The existing mobile encryption service classification methods have two shortcomings in feature selection: First, the DL model is used as a black box, and the features of large dimensions are not distinguished as input of classification model, which resulting in sharp increase in calculation complexity, and the actual application is limited. Second, the existing feature selection method is insufficient to use the time and space associated information of traffic, resulting in less robustness and low accuracy of the classification. In this paper, we propose a feature enhancement method based on adjacent flow contextual features and evaluate the Apple encryption service traffic collected from the real world. Based on 5 DL classification models, the refined classification accuracy of Apple services is significantly improved. Our work can provide an effective solution for the fine management of mobile encryption services.

A long-standing open question in computational learning theory is to prove NP-hardness of learning efficient programs, the setting of which is in between proper learning and improper learning. Ko (COLT’90, SICOMP’91) explicitly raised this open question and demonstrated its difficulty by proving that there exists no relativizing proof of NP-hardness of learning programs. In this paper, we overcome Ko’s relativization barrier and prove NP-hardness of learning programs under randomized polynomial-time many-one reductions. Our result is provably non-relativizing, and comes somewhat close to the parameter range of improper learning: We observe that mildly improving our inapproximability factor is sufficient to exclude Heuristica, i.e., show the equivalence between average-case and worst-case complexities of N P. We also make progress on another long-standing open question of showing NP-hardness of the Minimum Circuit Size Problem (MCSP). We prove NP-hardness of the partial function variant of MCSP as well as other meta-computational problems, such as the problems MKTP* and MINKT* of computing the time-bounded Kolmogorov complexity of a given partial string, under randomized polynomial-time reductions. Our proofs are algorithmic information (a.k. a. Kolmogorov complexity) theoretic. We utilize black-box pseudorandom generator constructions, such as the Nisan-Wigderson generator, as a one-time encryption scheme secure against a program which “does not know” a random function. Our key technical contribution is to quantify the “knowledge” of a program by using conditional Kolmogorov complexity and show that no small program can know many random functions.