An Effective Differential Fault Analysis on the Serpent Cryptosystem in the Internet of Things

Due to the strong attacking ability, fast speed, simple implementation and other characteristics, differential fault analysis has become an important method to evaluate the security of cryptosystem in the Internet of Things. As one of the AES finalists, the Serpent is a 128-bit Substitution-Permutation Network(SPN) cryptosystem. It has 32 rounds with the variable key length between 0 and 256 bits, which is flexible to provide security in the Internet of Things. On the basis of the byte-oriented model and the differential analysis, we propose an effective differential fault attack on the Serpent cryptosystem. Mathematical analysis and simulating experiment show that the attack could recover its secret key by introducing 48 faulty ciphertexts. The result in this study describes that the Serpent is vulnerable to differential fault analysis in detail. It will be beneficial to the analysis of the same type of other iterated cryptosystems.

Analysis of traffic safety in airport aircraft activity areas based on bayesian networks and fault trees

To assess road traffic safety risk in civil aviation airports and develop effective accident prevention measures,this study proposed a risk assessment method based on accident tree and Bayesian network for airport aircraft activity areas.It identified influencing factors in the aircraft activity area from the perspectives of person-vehicle-road-environment-management and analyzed their relationships.The Bayesian network was utilized to determine initial probabilities for each influencing factor.Findings indicated a relatively high overall safety level in the airport's road traffic system.Accident trees were employed to qualitatively and quantitatively analyze common human-vehicle accident patterns.The initial probabilities obtained from the Bayesian network served as basic event probabilities in the accident tree to determine the occurrence probability of the top event.Taking a 4F airport in China as an example,accident cause analysis identified five important risk sources in human-vehicle accidents,including blind spots for special vehicles,illegal driving by drivers,pedestrians violating regulations,passengers entering restricted areas,and blind spots at intersections.Corresponding safety management measures were formulated.The study concluded that the integration of Bayesian networks and accident trees effectively determines accident probabilities and offers specific solutions,thus playing a crucial role in enhancing road traffic safety management within aviation airports.

Interval Fault Tree Analysis of Excavator VariableFrequency Speed Control System

In consideration of the uncertainty of basic events failure rate and lack of probability statistical information in fault tree analysis( FTA) of excavator variable-frequency speed control system, the interval theory was employed and combined with conventional FTA method. The basic events failure probabilities were described by interval numbers,and the interval operators of logical gates in FTA were deduced based on interval theory. Finally,the reliability assessment of excavator variable-frequency speed control system was done by interval FTA method. The result shows that the interval FTA method is suitable for the complex system with insufficient failure data.

Fault Analysis and Innovation in Slag Discharge System of 440-t/h CFB Boiler

CFB boilers have been widely used in China in recent years with their perfect performances in coal adaptability, load variation capability and lower pollutant emission. The No.3 135-MW CFB unit in Lianzhou Power Plant is the f irst 440-t/h series CFB unit in Guangdong Province. It f inished 72-hour trial operation in Feb. 2004 and was transferred to trial operation. During the trial operation and the next commercial operation, there were some problems happened in the boiler slag discharging system, seriously affecting the safe and reliable operation and the loading capability. After innovation, these problems have been completely solved. Hopefully the solutions may be used for reference to the units with similar problems.

Fault Analysis and Structure Optimization of Traveling Transmission System of Shuttle Car

This paper firstly introduces the common faults of traveling transmission system of shuttle car.Secondly,by analyzing the characteristics of shuttle car structure,the layout of traveling transmission system and the common faults on shuttle car,this paper concludes that"internal holding torque"is the main cause of faults.Finally,this paper proposes a corresponding optimization design scheme to reduce the impact of"internal torque",and calculates the relevant results through the finite element simulation analysis method.Through these analyses and calculations,it is shown that the method can effectively reduce the probability of failure of traveling transmission system of shuttle car.

Power System Modelling and Fault Analysis of NamPower＇s 330 kV HVAC Transmission Line

Electric power systems usually cover large geographical areas and transmission facilities are continuously increasing. These power systems are exposed to different environmental conditions which may cause faults to occur on the system. Different types of studies are usually done on electric power systems to determine how the system behaves before, during and after a fault condition. The behaviour of variables of interest such as currents, voltage, rotor angle and active and reactive power under fault conditions are studied and observed to help determine possible causes of faults in a power system. The objective of this paper is to investigate a fault that occurred on the 330 kV transmission line between Ruacana power station and Omburu sub-station, the fault caused all the generators at Ruacana power station to trip and consequently caused a blackout at the power station that lasted for 6 h. Preliminary findings showed that the observed fault was an earth fault but the exact type of earth fault was however not known at the time. This research investigation sets out to determine the exact fault that occurred; the most probable cause of the fault, and propose possible solutions to prevent reoccurrence of such a fault. The section of the power network in which the fault occurred was modelled using DigSilent Power Factory software tool, and transient fault analysis was carried out on the model for different fault conditions. Results obtained were then compared with data obtained from NamPower records to ascertain the type of fault.

A SIMULATED ANNEALING METHOD FOR FAULT TREE ANALYSIS

This paper presents an approximate algorithm based on simulated annealing to achieve the maximum probability of the minimal cut sets for a fault tree. Near optimal minimal cut sets and important sequence of the basic events are also solved by the method. Computer simulations show that the algorithm performs very well.

Fault Diagnosis Method Based on System-phenomenon-fault Tree

The historical records of mechanical fault contain great amount of important information which is useful to identify the similar fault.The current fault diagnosis methods using historical records are inefficient to deal with intuitive application and multicomponent multiphase fault diagnosis.Towards the problem,the rapid and intelligent fault diagnosis method based on system-phenomenon-fault （SPF） tree is proposed.The method begins with the physical system of the fault system,conceives the fault causes as leaves,the fault causes as leaves and the frequentness of fault as the interrelationship,and finally forms the fault tree with structural relationship of administrative subordination and flexible multi-granularity components.Firstly,the forming method of SPF tree is discussed;Secondly some basic definitions as synonymous branch,the tough degree of the branch,the dominant leaf,and the virtual branch are defined;and then,the performances including the merger of the dominant branches keeping dominant,the merger of the synonymous branches keeping dominant were proved.Furthermore,the merging,optimizing and calculating of virtual branch of SPF tree are proposed,the self-learning mechanism including the procedure and the related parameter calculation is presented,and the fault searching method and main fault statistics calculation are also presented based on SPF tree.Finally,the method is applied in the fault diagnosis of the certain type of embedded terminal to demonstrate fault information searching in the condition of the synonymous branch,the virtual branch merging and visual presentation of search results.The application shows that the proposed method is effective to narrow down the scope of searching fault and reduce the difficulty of computing.The proposed method is a new way to resolve the intelligent fault diagnosis problem of complex systems by organizing the disordering fault records and providing intuitive expression and intelligent computing capabilities.

HYBRID WAVELET PACKET-TEAGER ENERGY OPERATOR ANALYSIS AND ITS APPLICATION FOR GEARBOX FAULT DIAGNOSIS

Based on wavelet packet decomposition （WPD） algorithm and Teager energy operator （TEO）, a novel gearbox fault detection and diagnosis method is proposed. Its process is expatiated after the principles of WPD and TEO modulation are introduced respectively. The preprocessed sigaaal is interpolated with the cubic spline function, then expanded over the selected basis wavelets. Grouping its wavelet packet components of the signal based on the minimum entropy criterion, the interpolated signal can be decomposed into its dominant components with nearly distinct fault frequency contents. To extract the demodulation information of each dominant component, TEO is used. The performance of the proposed method is assessed by means of several tests on vibration signals collected from the gearbox mounted on a heavy truck. It is proved that hybrid WPD-TEO method is effective and robust for detecting and diagnosing localized gearbox faults.

Fault Tree Analysis:Investigation of Epidemic Hemorrhagic Fever Infection Acquired in Animal Laboratories in China

Epidemic hemorrhagic fever has been an ongoing threat to laboratory personnel involved in animal care and use. Laboratory transmissions and severe infections occurred over the past twenty years, even though the standards and regulations for laboratory biosafety have been issued, upgraded, and implemented in China. Therefore, there is an urgent need to identify risk factors and to seek effective preventive measures that can curb the incidences of epidemic hemorrhagic fever among laboratory personnel. In the present study, we reviewed literature that relevant to animals laboratory-acquired hemorrhagic fever infections reported from 1995 to 2015, and analyzed these incidences using fault tree analysis （FTA）.

Machine learning based online fault prognostics for nonstationary industrial process via degradation feature extraction and temporal smoothness analysis

Fault degradation prognostic, which estimates the time before a failure occurs and process breakdowns, has been recognized as a key component in maintenance strategies nowadays. Fault degradation processes are, in general,slowly varying and can be modeled by autoregressive models. However, industrial processes always show typical nonstationary nature, which may bring two challenges: how to capture fault degradation information and how to model nonstationary processes. To address the critical issues, a novel fault degradation modeling and online fault prognostic strategy is developed in this paper. First, a fault degradation-oriented slow feature analysis(FDSFA) algorithm is proposed to extract fault degradation directions along which candidate fault degradation features are extracted. The trend ability assessment is then applied to select major fault degradation features. Second, a key fault degradation factor(KFDF) is calculated to characterize the fault degradation tendency by combining major fault degradation features and their stability weighting factors. After that, a time-varying regression model with temporal smoothness regularization is established considering nonstationary characteristics. On the basis of updating strategy, an online fault prognostic model is further developed by analyzing and modeling the prediction errors. The performance of the proposed method is illustrated with a real industrial process.

Milling Fault Detection Method Based on Fault Tree Analysis and Hierarchical Belief Rule Base

Expert knowledge is the key to modeling milling fault detection systems based on the belief rule base.The construction of an initial expert knowledge base seriously affects the accuracy and interpretability of the milling fault detection model.However,due to the complexity of the milling system structure and the uncertainty of the milling failure index,it is often impossible to construct model expert knowledge effectively.Therefore,a milling system fault detection method based on fault tree analysis and hierarchical BRB(FTBRB)is proposed.Firstly,the proposed method uses a fault tree and hierarchical BRB modeling.Through fault tree analysis(FTA),the logical correspondence between FTA and BRB is sorted out.This can effectively embed the FTA mechanism into the BRB expert knowledge base.The hierarchical BRB model is used to solve the problem of excessive indexes and avoid combinatorial explosion.Secondly,evidence reasoning(ER)is used to ensure the transparency of the model reasoning process.Thirdly,the projection covariance matrix adaptation evolutionary strategies(P-CMA-ES)is used to optimize the model.Finally,this paper verifies the validity model and the method’s feasibility techniques for milling data sets.

Feature Extraction Technique for Fault Prognosis Based on Fault Trend Analysis

Fault prognosis is one of the key techniques for prognosis and health management,and an effective fault feature can improve prediction accuracy and performance. A novel approach of feature extraction for fault prognosis based on fault trend analysis was proposed in this paper. In order to describe the ability of tracking fault growth process,definitions and calculations of fault trackability was developed, and the feature which had the maximum fault trackability was selected for fault prognosis. The vibration data in bearing life tests were used to verify the effectiveness of the method was proposed. The results showed that the trackability of energy entropy for bearing fault growth was the maximum,and it was the best fault feature among selected features root mean square( RMS),kurtosis,new moment and energy entropy. The proposed approach can provide a better strategy for fault feature extraction of bearings in order to improve prediction accuracy.

Fault Type Analysis along the San Andreas Fault Zone: A Numerical Approach

Finite Element (FE) modeling under plane stress condition is used to analyze the fault type variation with depth along and around the San Andreas Fault (SAF) zone. In this simulation elastic rheology was used and was thought justifiable as the variation in depth from 0.5 km to 20 km was considered. Series of calculations were performed with the variation in domain properties. Three types of models were created based on simple geological map of California, namely, 1) single domain model considering whole California as one homogeneous domain, 2) three domains model including the North American plate, Pacific plate, and SAF zone as separate domains, and 3) Four domains model including the three above plus the Garlock Fault zone. Mohr-Coulomb failure criterion and Byerlee's law were used for the calculation of failure state. All the models were driven by displacement boundary condition imposing the fixed North American plate and Pacific plate motion along N34°W vector up to the northern terminus of SAF and N50°E vector motion for the subducting the Gorda and Juan de Fuca plates. Our simulated results revealed that as the depth increased, the fault types were generally normal, and at shallow depth greater strike slip and some thrust faults were formed. It is concluded that SAF may be terminated as normal fault at depth although the surface expression is clearly strike slip.

An aligned mixture probabilistic principal component analysis for fault detection of multimode chemical processes

A novel approach named aligned mixture probabilistic principal component analysis(AMPPCA) is proposed in this study for fault detection of multimode chemical processes. In order to exploit within-mode correlations,the AMPPCA algorithm first estimates a statistical description for each operating mode by applying mixture probabilistic principal component analysis(MPPCA). As a comparison, the combined MPPCA is employed where monitoring results are softly integrated according to posterior probabilities of the test sample in each local model. For exploiting the cross-mode correlations, which may be useful but are inadvertently neglected due to separately held monitoring approaches, a global monitoring model is constructed by aligning all local models together. In this way, both within-mode and cross-mode correlations are preserved in this integrated space. Finally, the utility and feasibility of AMPPCA are demonstrated through a non-isothermal continuous stirred tank reactor and the TE benchmark process.

Multivariate time delay analysis based local KPCA fault prognosis approach for nonlinear processes

Currently, some fault prognosis technology occasionally has relatively unsatisfied performance especially for in- cipient faults in nonlinear processes duo to their large time delay and complex internal connection. To overcome this deficiency, multivariate time delay analysis is incorporated into the high sensitive local kernel principal component analysis. In this approach, mutual information estimation and Bayesian information criterion （BIC） are separately used to acquire the correlation degree and time delay of the process variables. Moreover, in order to achieve prediction, time series prediction by back propagation （BP） network is applied whose input is multivar- iate correlated time series other than the original time series. Then the multivariate time delayed series and future values obtained by time series prediction are combined to construct the input of local kernel principal component analysis （LKPCA） model for incipient fault prognosis. The new method has been exemplified in a sim- ple nonlinear process and the complicated Tennessee Eastman （TE） benchmark process. The results indicate that the new method has suoerioritv in the fault prognosis sensitivity over other traditional fault prognosis methods.

Short-circuit fault analysis and isolation strategy for matrix converters

The behavior of matrix converter(MC) drive systems under the condition of MC short-circuit faults is comprehensively investigated. Two isolation strategies using semiconductors and high speed fuses(HSFs) for MC short-circuit faults are examined and their performances are compared. The behavior of MC drive systems during the fuse action time under different operating conditions is explored. The feasibility of fault-tolerant operation during the fuse action time is also studied. The basic selection laws for the HSFs and the requirements for the passive components of the MC drive system from the point view of short-circuit faults are also discussed. Simulation results are used to demonstrate the feasibility of the proposed isolation strategies.

Formalization of Fault Analysis and a New Approach of Fault Detection

Substitution permutation network (SPN) is one important structure of block cipher cryptosystems. Prior work has shown different fault analyses on SPN. The formalization of fault analysis of both attack and protect on SPN have been given. The overhead and time tolerance of fault detection have been discussed. The pseudo-blinding method to detect fault attack is introduced, and the balance of the security, overhead and time tolerance based on the evaluation could be made.

Temperature-Triggered Hardware Trojan Based Algebraic Fault Analysis of SKINNY-64-64 Lightweight Block Cipher

SKINNY-64-64 is a lightweight block cipher with a 64-bit block length and key length,and it is mainly used on the Internet of Things(IoT).Currently,faults can be injected into cryptographic devices by attackers in a variety of ways,but it is still difficult to achieve a precisely located fault attacks at a low cost,whereas a Hardware Trojan(HT)can realize this.Temperature,as a physical quantity incidental to the operation of a cryptographic device,is easily overlooked.In this paper,a temperature-triggered HT(THT)is designed,which,when activated,causes a specific bit of the intermediate state of the SKINNY-64-64 to be flipped.Further,in this paper,a THT-based algebraic fault analysis(THT-AFA)method is proposed.To demonstrate the effectiveness of the method,experiments on algebraic fault analysis(AFA)and THT-AFA have been carried out on SKINNY-64-64.In the THT-AFA for SKINNY-64-64,it is only required to activate the THT 3 times to obtain the master key with a 100%success rate,and the average time for the attack is 64.57 s.However,when performing AFA on this cipher,we provide a relation-ship between the number of different faults and the residual entropy of the key.In comparison,our proposed THT-AFA method has better performance in terms of attack efficiency.To the best of our knowledge,this is the first HT attack on SKINNY-64-64.

Method Based on Time Randomization to Resist Fault Sensitivity Analysis

A fault sensitivity analysis（FSA）-resistance model based on time randomization is proposed.The randomization unit is composed of two parts,namely the configurable register array（R-A）and the decoder（chiefly random number generator,RNG）.In this way,registers chosen can be either valid or invalid depending on the configuration information generated by the decoder.Thus,the fault sensitivity information can be confusing.Meanwhile,based on this model,a defensive scheme is designed to resist both fault sensitivity analysis（FSA）and differential power analysis（DPA）.This scheme is verified with our experiments.