CORMAND2--针对工业机器人的欺骗攻击

Industrial robots are becoming increasingly vulnerable to cyber incidents and attacks,particularly with the dawn of the Industrial Internet-of-Things(IIoT).To gain a comprehensive understanding of these cyber risks,vulnerabilities of industrial robots were analyzed empirically,using more than three million communication packets collected with testbeds of two ABB IRB120 robots and five other robots from various original equipment manufacturers(OEMs).This analysis,guided by the confidentiality-integrity-availability(CIA)triad,uncovers robot vulnerabilities in three dimensions:confidentiality,integrity,and availability.These vulnerabilities were used to design Covering Robot Manipulation via Data Deception(CORMAND2),an automated cyber-physical attack against industrial robots.CORMAND2 manipulates robot operation while deceiving the Supervisory Control and Data Acquisition(SCADA)system that the robot is operating normally by modifying the robot’s movement data and data deception.CORMAND2 and its capability of degrading the manufacturing was validated experimentally using the aforementioned seven robots from six different OEMs.CORMAND2 unveils the limitations of existing anomaly detection systems,more specifically the assumption of the authenticity of SCADA-received movement data,to which we propose mitigations for.

An Active Deception Defense Model Based on Address Mutation and Fingerprint Camouflage

The static and predictable characteristics of cyber systems give attackers an asymmetric advantage in gathering useful information and launching attacks.To reverse this asymmetric advantage,a new defense idea,called Moving Target Defense(MTD),has been proposed to provide additional selectable measures to complement traditional defense.However,MTD is unable to defeat the sophisticated attacker with fingerprint tracking ability.To overcome this limitation,we go one step beyond and show that the combination of MTD and Deception-based Cyber Defense(DCD)can achieve higher performance than either of them.In particular,we first introduce and formalize a novel attacker model named Scan and Foothold Attack(SFA)based on cyber kill chain.Afterwards,we develop probabilistic models for SFA defenses to provide a deeper analysis of the theoretical effect under different defense strategies.These models quantify attack success probability and the probability that the attacker will be deceived under various conditions,such as the size of address space,and the number of hosts,attack analysis time.Finally,the experimental results show that the actual defense effect of each strategy almost perfectly follows its probabilistic model.Also,the defense strategy of combining address mutation and fingerprint camouflage can achieve a better defense effect than the single address mutation.

Distributed Fault Estimation for Nonlinear Systems With Sensor Saturation and Deception Attacks Using Stochastic Communication Protocols

This paper is aimed at the distributed fault estimation issue associated with the potential loss of actuator efficiency for a type of discrete-time nonlinear systems with sensor saturation.For the distributed estimation structure under consideration,an estimation center is not necessary,and the estimator derives its information from itself and neighboring nodes,which fuses the state vector and the measurement vector.In an effort to cut down data conflicts in communication networks,the stochastic communication protocol(SCP)is employed so that the output signals from sensors can be selected.Additionally,a recursive security estimator scheme is created since attackers randomly inject malicious signals into the selected data.On this basis,sufficient conditions for a fault estimator with less conservatism are presented which ensure an upper bound of the estimation error covariance and the mean-square exponential boundedness of the estimating error.Finally,a numerical example is used to show the reliability and effectiveness of the considered distributed estimation algorithm.

LDNet: A Robust Hybrid Approach for Lie Detection Using Deep Learning Techniques

Deception detection is regarded as a concern for everyone in their daily lives and affects social interactions.The human face is a rich source of data that offers trustworthy markers of deception.The deception or lie detection systems are non-intrusive,cost-effective,and mobile by identifying facial expressions.Over the last decade,numerous studies have been conducted on deception detection using several advanced techniques.Researchers have focused their attention on inventing more effective and efficient solutions for the detection of deception.So,it could be challenging to spot trends,practical approaches,gaps,and chances for contribution.However,there are still a lot of opportunities for innovative deception detection methods.Therefore,we used a variety of machine learning(ML)and deep learning(DL)approaches to experiment with this work.This research aims to do the following:(i)review and analyze the current lie detection(LD)systems;(ii)create a dataset;(iii)use several ML and DL techniques to identify lying;and(iv)create a hybrid model known as LDNet.By combining layers from Vgg16 and DeneseNet121,LDNet was developed and offered the best accuracy(99.50%)of all the models.Our developed hybrid model is a great addition that significantly advances the study of LD.The findings from this research endeavor are expected to advance our understanding of the effectiveness of ML and DL techniques in LD.Furthermore,it has significant practical applications in diverse domains such as security,law enforcement,border control,organizations,and investigation cases where accurate lie detection is paramount.

Video-Based Deception Detection with Non-Contact Heart Rate Monitoring and Multi-Modal Feature Selection

Deception detection plays a crucial role in criminal investigation.Videos contain a wealth of information regarding apparent and physiological changes in individuals,and thus can serve as an effective means of deception detection.In this paper,we investigate video-based deception detection considering both apparent visual features such as eye gaze,head pose and facial action unit(AU),and non-contact heart rate detected by remote photoplethysmography(rPPG)technique.Multiple wrapper-based feature selection methods combined with the K-nearest neighbor(KNN)and support vector machine(SVM)classifiers are employed to screen the most effective features for deception detection.We evaluate the performance of the proposed method on both a self-collected physiological-assisted visual deception detection(PV3D)dataset and a public bag-oflies(BOL)dataset.Experimental results demonstrate that the SVM classifier with symbiotic organisms search(SOS)feature selection yields the best overall performance,with an area under the curve(AUC)of 83.27%and accuracy(ACC)of 83.33%for PV3D,and an AUC of 71.18%and ACC of 70.33%for BOL.This demonstrates the stability and effectiveness of the proposed method in video-based deception detection tasks.

Cyber Deception Using NLP

Cyber security addresses the protection of information systems in cyberspace. These systems face multiple attacks on a daily basis, with the level of complication getting increasingly challenging. Despite the existence of multiple solutions, attackers are still quite successful at identifying vulnerabilities to exploit. This is why cyber deception is increasingly being used to divert attackers’ attention and, therefore, enhance the security of information systems. To be effective, deception environments need fake data. This is where Natural Language (NLP) Processing comes in. Many cyber security models have used NLP for vulnerability detection in information systems, email classification, fake citation detection, and many others. Although it is used for text generation, existing models seem to be unsuitable for data generation in a deception environment. Our goal is to use text generation in NLP to generate data in the deception context that will be used to build multi-level deception in information systems. Our model consists of three (3) components, including the connection component, the deception component, composed of several states in which an attacker may be, depending on whether he is malicious or not, and the text generation component. The text generation component considers as input the real data of the information system and allows the production of several texts as output, which are usable at different deception levels.

A Preliminary Study on Pollination Biology of an Endangered Orchid, Changnienia amoena, in Shennongjia

Changnienia amoena Chien is a monotypic species and endemic to China, and was listed on the Chinese Red Book in 1992. The species was once abundant but has become rare and endangered in recent years because of the habitat fragmentation and unduly commercial collections. Previous observation showed that this species has very low and even no fruit set, and the pollinators are not observed before this report. The present observation was conducted at the Shennongjia, Hubei Province during the spring of 2002. The results showed that Bombus (Diversobombus) trifasciatus Smith, B. imitator Pittion and one species of Apis visited the flowers of the orchid, but only B. tritasciatus could carry pollinaria on its body and was the legitimate pollinators of C amoena. During 113 h of observation, only nine visitations were recorded. The bumblebees mainly appeared during 12:00-15:00 during the day. Bumblebees stayed in a flower only a few seconds and never more than ten seconds. The flowers would persist in fresh for about three weeks when they were not pollinated, but 3 or 4 d after pollinated, the pollinated flowers underwent a series of color and morphological changes including stalk elongation and ovary swelling. Therefore, stalk elongation can be considered an index of fruits set. Artificial pollination indicated that C amoena is a highly self-compatible and outcrossing species, but dependent on pollinators for fruit set. Based on the field observations, we concluded that pollination system of C amoena is deceptive. The fruit set in nature is not very low (26.98% on average) compared to other deceptive orchids, which may be related to small population sizes. The number of pollinia removal is much higher than that of fruit set, indicating that there are some degrees of pollinia wasting in C amoena.

Physical Safety and Cyber Security Analysis of Multi-Agent Systems:A Survey of Recent Advances

Multi-agent systems(MASs)are typically composed of multiple smart entities with independent sensing,communication,computing,and decision-making capabilities.Nowadays,MASs have a wide range of applications in smart grids,smart manufacturing,sensor networks,and intelligent transportation systems.Control of the MASs are often coordinated through information interaction among agents,which is one of the most important factors affecting coordination and cooperation performance.However,unexpected physical faults and cyber attacks on a single agent may spread to other agents via information interaction very quickly,and thus could lead to severe degradation of the whole system performance and even destruction of MASs.This paper is concerned with the safety/security analysis and synthesis of MASs arising from physical faults and cyber attacks,and our goal is to present a comprehensive survey on recent results on fault estimation,detection,diagnosis and fault-tolerant control of MASs,and cyber attack detection and secure control of MASs subject to two typical cyber attacks.Finally,the paper concludes with some potential future research topics on the security issues of MASs.

Secure Impulsive Synchronization in Lipschitz-Type Multi-Agent Systems Subject to Deception Attacks

Cyber attacks pose severe threats on synchronization of multi-agent systems.Deception attack,as a typical type of cyber attack,can bypass the surveillance of the attack detection mechanism silently,resulting in a heavy loss.Therefore,the problem of mean-square bounded synchronization in multi-agent systems subject to deception attacks is investigated in this paper.The control signals can be replaced with false data from controllerto-actuator channels or the controller.The success of the attack is measured through a stochastic variable.A distributed impulsive controller using a pinning strategy is redesigned,which ensures that mean-square bounded synchronization is achieved in the presence of deception attacks.Some sufficient conditions are derived,in which upper bounds of the synchronization error are given.Finally,two numerical simulations with symmetric and asymmetric network topologies are given to illustrate the theoretical results.

Deceptive jamming suppression in multistatic radar based on coherent clustering

This paper proposes a suppression method of the deceptive false target(FT) produced by digital radio frequency memory(DRFM) in a multistatic radar system. The simulated deceptive false targets from DRFM cannot be easily discriminated and suppressed with traditional radar systems. Therefore, multistatic radar has attracted considerable interest as it provides improved performance against deception jamming due to several separated receivers. This paper first investigates the received signal model in the presence of multiple false targets in all receivers of the multistatic radar. Then, obtain the propagation time delays of the false targets based on the cross-correlation test of the received signals in different receivers. In doing so, local-density-based spatial clustering of applications with noise(LDBSCAN) is proposed to discriminate the FTs from the physical targets(PTs) after compensating the FTs time delays, where the FTs are approximately coincident with one position, while PTs possess small dispersion.Numerical simulations are carried out to demonstrate the feasibility and validness of the proposed method.

Cross-eye gain distribution of multiple-element retrodirective cross-eye jamming

The total cross-eye gain of multiple-element retrodirective cross-eye jamming(MRCJ) in the presence of the platform skin return is a distribution rather than a constant value, due to the random variation in the phase of the skin return. Although the median value of the total cross-eye gain distribution had been analyzed in previous studies, the extreme values providing useful indications of the upper and lower bounds of the total cross-eye gain have not been analyzed until now. In this paper, the cumulative distribution function and the extreme values of the total cross-eye gain of MRCJ are derived. The angular error induced in threat monopulse radar as a figure of merit is used to analyze the performance of MRCJ system. Simulation results demonstrate the variation of the angular error and discuss the proper value of jamming-to-signal ratio(JSR) making the MRCJ system more effective in consideration of the whole distribution of the total cross-eye gain.

Evaluation on safety performance of a millimetre wave radar-based autonomous vehicle

This paper presents a method using range deception jamming to evaluate the safety performance of the autonomous vehicle with millimetre wave(MMW)radar.The working principle of this method is described.Combined with a waveform edition software,an experimental platform is developed to generate a deceptive signal that contains false distance information.According to related theories and its principle,the configuration parameters of the experimental setup are calculated and configured.The MMW radar of evaluated vehicle should identify an objective when it receives the deceptive signal from the experimental setup.Even if no obstacle,the evaluated vehicle can immediately brake in order that its braking distance is measured.The experimental results show that the proposed method can meet the requirements of the safety performance evaluation for the autonomous vehicle with MMW radar,and it also overcomes some deficiencies of previous methods.

Parameters Design of Pull-off Deceptive Jamming to Terminal Guidance Radar

A deceptive pull-off jamming method to terminal guidance radar is put forward in this paper.The design rules about the important jamming parameters are discussed in detail,including the number of the decoy targets in range dimension,the velocity of the range gate pull-off,and the number of the decoy targets in velocity dimension and the velocity of the Doppler frequency pull-off.Also,the steps to design these parameters are brought out.The rules and design procedure discussed in this paper have important meaning for the choice of the reasonable jamming parameters in the practical applications,which can help to obtain good jamming effect.

Performance analysis of multi-group three-tuple cross-eye jamming

Monopulse radar is widely used in military.Jamming monopulse radar has always been a research hotspot in electronic warfare(EW).Cross-eye jamming has always been considered as the most effective measures to jam with monopulse radar.In this paper, we propose a multi-group three-tuple crosseye jamming structure where each group contains three antenna elements with a definite phase and an amplitude relationship.Then, based on the principle of monopulse angle measurement, the error angle is deduced theoretically.Simulations show that such a multi-group three-tuple cross-eye jamming structure performs better than the multi-element cross-eye jamming structure previously proposed, and the analysis of the centroid shows that the centroid of the structure proposed in this paper is more widely distributed in space.

System Design and Signal Processing for Frequency Diverse Array Radar

Frequency diverse array(FDA)radar has been studied for more than 15 years and has attracted a lot of attention due to its potential advantages over the well-known phased array radar.The representative feature of FDA is range-angle-time-dependent transmit beampattern and its underlying properties are continuously revealed in the research.The formulation and exploitation of the transmit diversity with a frequency increment is the fundamental principle,which brings extra degrees-of-freedom(DOFs)in the transmit dimension.As the FDA radar carries additional information in range,it provides more flexibility in signal processing and also brings in new technical issues.This article overviews the state-of-the-art in FDA radar area and its applications,mainly based on the progress in our group.There are two main catalogs in FDA radar area,namely coherent FDA and FDA-MIMO(multiple-input multiple-output)radars.Potential applications including target parameter estimation,ambiguous clutter suppression,and deceptive jammer suppression are discussed.

An Opinion Spam Detection Method Based on Multi-Filters Convolutional Neural Network

With the continuous development of e-commerce,consumers show increasing interest in posting comments on consumption experience and quality of commodities.Meanwhile,people make purchasing decisions relying on other comments much more than ever before.So the reliability of commodity comments has a significant impact on ensuring consumers’equity and building a fair internet-trade-environment.However,some unscrupulous online-sellers write fake praiseful reviews for themselves and malicious comments for their business counterparts to maximize their profits.Those improper ways of self-profiting have severely ruined the entire online shopping industry.Aiming to detect and prevent these deceptive comments effectively,we construct a model of Multi-Filters Convolutional Neural Network(MFCNN)for opinion spam detection.MFCNN is designed with a fixed-length sequence input and an improved activation function to avoid the gradient vanishing problem in spam opinion detection.Moreover,convolution filters with different widths are used in MFCNN to represent the sentences and documents.Our experimental results show that MFCNN outperforms current state-of-the-art methods on standard spam detection benchmarks.

Pattern recognitionbased method for radar antideceptive jamming

In order to make the effective ECCM to the deceptive jamming, especially the angle deceptive jamming, this paper establishes a signal-processing model for anti-deceptive jamming firstly, in which two feature-extracting algorithms, i.e. the statistical algorithm and the neural network （NN） algorithm are presented, then uses the RBF NN as the classitier in the processing model. Finally the two algorithms are validated and compared through some simulations.

Genome of a thermophilic bacterium Geobacillus sp. TFV3 from Deception Island, Antarctica

Thermophilic microorganisms have always been an important part of the ecosystem,particularly in a hot environment,as they play a key role in nutrient recycling at high temperatures where most microorganisms cannot cope.While most of the thermophiles are archaea,thermophiles can also be found among some species of bacteria.These bacteria are very useful in the fundamental study of heat adaptation,and they are also important as potential sources of thermostable enzymes and metabolites.Recently,we have isolated a Gram-positive thermophilic bacterium,Geobacillus sp.TFV3 from a volcanic soil sample from Deception Island,Antarctica.This project was undertaken to analyze the genes of this thermophilic Antarctic bacterium and to determine the presence of thermal-stress adaptation proteins in its genome.The genome of Geobacillus sp.TFV3 was first purified,sequenced,assembled,and annotated.The complete genome was found to harbor genes encoding for useful thermal-stress adaptation proteins.The majority of these proteins were categorized under the family of molecular chaperone and heat shock protein.This genomic information could eventually provide insights on how the bacterium adapts itself towards high growth temperatures.

High-Frequency Trading:Deception and Consequences

This commentary is based on the work of Cooper,Davis,and Van Vliet(2016)and the commentary focuses on what problem high-frequency trading poses.It lists key literature on high-frequency trading that is missing and points out that the poker analogy to defend deception in financial markets is weak and misleading.The article elaborates on the negative impact created by spoofing and quote stuffing,the two typical deceptive practices used by high-frequency traders.The recent regulations regarding high-frequency trading,in response to the“Flash Crash”of 2010,are preventive,computerized and more effective.They reflect ethical requirements to maintain fair and stable financial markets.

A Reflection on The Icarus Deception—How High Will You Fly Translation Practice

This thesis is based on the author's experience of English to Chinese translation practice of two parts of The Icarus Deception—How High Will You Fly which is an economic motivation book by American best selling author Seth Godin published by Penguin Books Ltd Dec, 2012. The thesis is a reflection about the process of translation, from the perspectives of lexical, sentence structure, discourse and the understanding of the source text etc.