Deceptive Signals Research Articles

Viewing Cancer Through the Lens of Corruption: Using Behavioral Ecology to Understand Cancer

All biological systems depend on signals for coordination: signals which pass information among agents that run the gamut from cells to organisms. However, their very importance makes signals vulnerable to subversion. How can a receiver know whether a signal is honest or deceptive? In other words, are signals necessarily a reliable indicator of agent quality or need? By drawing parallels to ecological phenomena ranging from begging by nestlings to social insects, we investigate the role of signal degradation in cancer. We thus think of cancer as a form of corruption, in which cells command huge resource investment through relatively cheap signals, just as relatively small bribes can leverage large profits. We discuss various mechanisms which prevent deceptive signaling in the natural world and within tissues. We show how cancers evolve ways to escape these controls and relate these back to evasion mechanisms in ecology. We next introduce two related concepts, co-option and collusion, and show how they play critical roles in ecology and cancer. Drawing on public policy, we propose new approaches to view treatment based on taxation, changing the incentive structure, and the recognition of corrupted signaling networks.

Towards a Cognitive Theory of Cyber Deception.

This work is an initial step toward developing a cognitive theory of cyber deception. While widely studied, the psychology of deception has largely focused on physical cues of deception. Given that present-day communication among humans is largely electronic, we focus on the cyber domain where physical cues are unavailable and for which there is less psychological research. To improve cyber defense, researchers have used signaling theory to extended algorithms developed for the optimal allocation of limited defense resources by using deceptive signals to trick the human mind. However, the algorithms are designed to protect against adversaries that make perfectly rational decisions. In behavioral experiments using an abstract cybersecurity game (i.e., Insider Attack Game), we examined human decision-making when paired against the defense algorithm. We developed an instance-based learning (IBL) model of an attacker using the Adaptive Control of Thought-Rational (ACT-R) cognitive architecture to investigate how humans make decisions under deception in cyber-attack scenarios. Our results show that the defense algorithm is more effective at reducing the probability of attack and protecting assets when using deceptive signaling, compared to no signaling, but is less effective than predicted against a perfectly rational adversary. Also, the IBL model replicates human attack decisions accurately. The IBL model shows how human decisions arise from experience, and how memory retrieval dynamics can give rise to cognitive biases, such as confirmation bias. The implications of these findings are discussed in the perspective of informing theories of deception and designing more effective signaling schemes that consider human bounded rationality.

Who do you trust? Wild birds use social knowledge to avoid being deceived.

Many species give deceptive warning calls, enabled by the high risk of ignoring them. In Siberian jays, a territorial, group-living bird, individuals give warning calls toward perched predators and mob them. However, intruding neighbors can emit these warning calls in the absence of predators to access food, but breeders often ignore these calls. Playback field experiments show that breeders flee sooner and return later after warning calls of former group members than those of neighbors or unknown individuals. Thus, breeders respond appropriately only to warning calls of previous cooperation partners. This mechanism facilitates the evolution and maintenance of communication vulnerable to deceptive signaling. This conclusion also applies to human language because of its cooperative nature and thus, its vulnerability to deception.

A Stop Condition for Compressed Recovery of Random Modulated Signal

A stop condition without prior information, applied to compressed sensing recovery in millimeter-wave detection system considering the deceptive jamming signal, is proposed in this brief. The input observation signal is extended to the case consisting of the interference that is correlated to the recovery signal. By making the sufficient statistics of the input parameters and observation values generated from the iterative process, the recovery algorithm can be stopped more appropriately. The main contribution of this brief is to propose a stop criterion to improve the efficiency of the algorithm for random modulated signal recovery. Furthermore, different modulated parameters of the random modulated signal are investigated to verify the feasibility and adaptability of the proposed method. The comparable results show greater performance and the algorithm efficiency is improved by 53.2%.

Efficacy in deceptive vocal exaggeration of human body size

How can deceptive communication signals exist in an evolutionarily stable signalling system? To resolve this age-old honest signalling paradox, researchers must first establish whether deception benefits deceivers. However, while vocal exaggeration is widespread in the animal kingdom and assumably adaptive, its effectiveness in biasing listeners has not been established. Here, we show that human listeners can detect deceptive vocal signals produced by vocalisers who volitionally shift their voice frequencies to exaggerate or attenuate their perceived size. Listeners can also judge the relative heights of cheaters, whose deceptive signals retain reliable acoustic cues to interindividual height. Importantly, although vocal deception biases listeners’ absolute height judgments, listeners recalibrate their height assessments for vocalisers they correctly and concurrently identify as deceptive, particularly men judging men. Thus, while size exaggeration can fool listeners, benefiting the deceiver, its detection can reduce bias and mitigate costs for listeners, underscoring an unremitting arms-race between signallers and receivers in animal communication.

Deceptive signaling on globalized digital platforms: Institutional hypnosis and firm internationalization

We investigate the degree and duration of the effects of deceptive signaling on globalized digital platforms, vis-a-vis naturally formed organic signaling such as importer review scores and discernible inducing signaling such as banner advertisements. Sponsored listing has emerged as a powerful tool for facilitating Internet-mediated exchanges in internationalization activities. Some digital platforms based in weakly regulated countries provide sponsored listing to exporters without explicitly disclosing that sponsorship. This constitutes a deceptive signal to importers in strongly regulated countries whose governments require sponsorship disclosure. Deceptive signaling is hypothesized to stimulate an institutional hypnosis on importers, with the most immediate and largest enhancing effects on exporter performance of the three types of signaling. Organic signaling is hypothesized to exert the most durable effect. Using transaction-level data from a globalized digital platform assembled with web mining methods, we develop a novel algorithm to detect deceptive signaling. Regression results from generalized method of moments estimation support these hypotheses. Our study is among the first to investigate institutional hypnosis manifested in increasingly widespread and unchecked forms in digitized international business transacting. By discovering and reporting on a dark side of institutional arbitrage, we offer actionable implications for digital platform governance, participants, and regulators.

Extremum Seeking Under Persistent Gradient Deception: A Switching Systems Approach

This letter focuses on extremum seeking (ES) controllers with adversarial attacks in the form of deception signals. While a persistent attack in a feedback controller may be difficult to identify or mitigate, for a broad class of algorithms it suffices to achieve mitigation “sufficiently often” in order to preserve the stability properties of the system. In this letter, we explore for the first time the resilience properties of ES controllers with respect to a class of persistent multiplicative attacks that are purposely designed to destabilize optimization-based feedback controllers. By leveraging Lyapunov-based arguments for switching systems and singular-perturbation theory for hybrid dynamical systems, we characterize a family of persistent multiplicative attacks under which gradient-based ES, Newton-Like ES, and Accelerated gradient ES controllers provably preserve their stability properties.

Synthetic Aperture Radar Interference Based on Scene Fusion and Active Cancellation

Echo cancellation is used for eliminating echo signals by signal modulation in the frequency domain and time domain. However, it depends on the high accuracy of parameter estimation. In addition, it introduces an incompatible black mark in the synthetic aperture radar (SAR) image, which will be easily detected and inevitably degrades the deception performance. To address this problem, this article proposed a compound interference method that combines echo cancellation and deception jamming. In order to fuse the deception scene with the surrounding scene, the optimal deception template is generated by using the scene around the target of interest (TOI), and the deception template is used to modulate the coefficient of the original signal to generate the deception signal. Then, the deception signal is sent to the SAR platform together with the cancellation signal, which is generated by an active source. After the SAR process, the incompatible black mark in the image is replaced by a template with high similarity of surrounding scene, which effectively improves the deception performance. The proposed method can be used to cover the higher scattering characteristic of the TOI with the lower scattering characteristic of the deception template. Simulation results are provided and analyzed to validate the universality and the performance superiority of the proposed method.

An Approach of Linear Regression‐Based UAV GPS Spoofing Detection

A prominent security threat to unmanned aerial vehicle (UAV) is to capture it by GPS spoofing, in which the attacker manipulates the GPS signal of the UAV to capture it. This paper introduces an anti‐spoofing model to mitigate the impact of GPS spoofing attack on UAV mission security. In this model, linear regression (LR) is used to predict and model the optimal route of UAV to its destination. On this basis, a countermeasure mechanism is proposed to reduce the impact of GPS spoofing attack. Confrontation is based on the progressive detection mechanism of the model. In order to better ensure the flight security of UAV, the model provides more than one detection scheme for spoofing signal to improve the sensitivity of UAV to deception signal detection. For better proving the proposed LR anti‐spoofing model, a dynamic Stackelberg game is formulated to simulate the interaction between GPS spoofer and UAV. In particular, for GPS spoofer, it is worth mentioning that for the scenario that the UAV is cheated by GPS spoofing signal in the mission environment of the designated route is simulated in the experiment. In particular, UAV with the LR anti‐spoofing model, as the leader in this game, dynamically adjusts its response strategy according to the deception’s attack strategy when upon detection of GPS spoofer’s attack. The simulation results show that the method can effectively enhance the ability of UAV to resist GPS spoofing without increasing the hardware cost of the UAV and is easy to implement. Furthermore, we also try to use long short‐term memory (LSTM) network in the trajectory prediction module of the model. The experimental results show that the LR anti‐spoofing model proposed is far better than that of LSTM in terms of prediction accuracy.

Secure consensus of multi-agent systems with redundant signal and communication interference via distributed dynamic event-triggered control

This paper studies a class of multi-agent systems (MASs) subject to deception signal and communication interference. The objective of the present work is to establish a flexible and generalized distributed dynamic event-triggered control (DDETC) with impulsive signal to make the investigated MASs achieve secure consensus under redundant signal and communication interference. It is shown that Zeno behavior can be precluded with such a DDETC. The challenging but valuable new designed DDETC scheme shows the trigger is developed to achieve itself away from exceeding the data transmission load through parameter adjustment, to reduce redundant triggering, to flexibly adjust the triggered frequency, and even to replace sampled-data scheme as special cases. By the impulsive DDETC, anti-deception and anti-interference techniques, the secure consensus criteria of MASs are constructed cleverly. Numerical examples with simulations are given to illustrate the effectiveness of the proposed scheme and control protocol.

Toward Personalized Deceptive Signaling for Cyber Defense Using Cognitive Models.

Recent research in cybersecurity has begun to develop active defense strategies using game-theoretic optimization of the allocation of limited defenses combined with deceptive signaling. These algorithms assume rational human behavior. However, human behavior in an online game designed to simulate an insider attack scenario shows that humans, playing the role of attackers, attack far more often than predicted under perfect rationality. We describe an instance-based learning cognitive model, built in ACT-R, that accurately predicts human performance and biases in the game. To improve defenses, we propose an adaptive method of signaling that uses the cognitive model to trace an individual's experience in real time. We discuss the results and implications of this adaptive signaling method for personalized defense.

Spider lures exploit insect preferences for floral colour and symmetry

Sensory systems can capture only a fraction of available information, which creates opportunities for deceptive signalling. The sensory traps and sensory bias models have proven valuable for explaining how visual systems and environments shape the design of sexual signals, but their application to deceptive signals is largely limited to the context of pollination. Here we use the ‘jewelled’ orb-web spider Gasteracantha fornicata to experimentally test two longstanding hypotheses for the function of deceptive visual lures. Namely, that they: (1) exploit generalised preferences for conspicuous colouration (sensory bias), or (2) co-opt the otherwise-adaptive foraging response of prey toward flowers (sensory traps). In a field-based study we manipulated the conspicuous dorsal signals of female spiders along two axes—colour pattern and symmetry—to generate a gradient of floral resemblance and monitored the per-individual consequences for prey interception. As predicted by the sensory traps model, the most attractive phenotypes were those with flower-like radial symmetry and solid colour patterns, and their attractiveness equaled that of natural spiders. Taken with recent work demonstrating a close resemblance between G. fornicata and sympatric floral ‘models’, and pollinating insects as primary prey items, our results suggest that the deceptive colour-based lures of spiders function as inter-kingdom sensory traps via floral mimicry, and support the broader extension of sensory-based models to deceptive signalling contexts.

Problems for Predictive Information

Predictive information is a popular and promising family of information-based theories of biological communication. It is difficult to adjudicate between predictive information-based theories and influence-based theories of biological communication because the same acts seem to count as communicative on both theories. In this paper, I argue that predictive information theories and influence-based theories give importantly different descriptions of deceptive signals in some non-evolutionarily stable communicative systems by citing a novel case observed in nature. Moreover, predictive information gives a counter-intuitive description to the case while some of its rival influence-based theories do not. I argue that there are no clear ways for defenders of predictive information to respond to this apparent problem without sacrificing important virtues of their theory or deflating the difference between the rival views.

A Composite Detection Method for Direct GPS Deception Attack

On account of the GPS initial technology limitations, it is vulnerable to interference, deception and other security attacks; GPS is widely used. If it is attacked, it will bring very serious consequences. Due to the problem of cost, civil GPS seldom considers how to defend against security attack. Therefore, it is necessary to study a fast and efficient GPS deception signal detection method to resist the attack. By improving the existing detection methods of GPS deception signal, a composite detection method of GPS deception signal is proposed. Based on DOA detection, the detection of signal propagation time difference is added, and the existence of GPS deception signal is judged synthetically according to whether the signal angle of arrival and the signal propagation time difference exceed the threshold value of real signal. The improved method improves the detection efficiency by reducing the calculation times of the signal angle of arrival. By reducing the number of detection signals to improve the detection accuracy of signal propagation time difference when there are more detection signals and less deception signals, thus ensuring the accuracy of composite detection. And subsequent simulation experiments successfully detected direct GPS deception signals, which verified the feasibility of this method for rapid detection of direct deception signals.

Parameter Identification and Sensor Configuration in Tip Timing for Asynchronous Vibration of a Deformed Blade With Finite Element Method Simulated Data Verification

Abstract Blade tip timing (BTT) measurement has been used to measure the vibration of turbomachinery blades in recent years. People researches focus on the development of processing methods. Newly developed BTT processing methods are usually tested by numerical simulations. Because blades will deform when loaded, the detected points by sensors relative to the blade tip will deviate from their expected positions. A novel simulated BTT model is proposed to identify the movement of the detected points. Finite element method (FEM) is used, and the blade tip profile is discretized into series of segments, each of which is composed of adjacent nodes from the blade tip grid to avoid the error possibly caused by coarse girds. There are two factors which will deviate the detected points from their initial positions: static deformation and vibration. This method is validated by BTT datasets from three different simulations. In addition, an improved sine fitting method and a displacement modification method are proposed to process the deceptive signal of the detected point. The effect of the sensor configuration on the parameter identification is also studied. The result shows that when the static point cannot be determined, it would be better if the sensor could be mounted at a place where it is far away from the nodal line and the static deformation there is small.

Robot Betrayal: a guide to the ethics of robotic deception

If a robot sends a deceptive signal to a human user, is this always and everywhere an unethical act, or might it sometimes be ethically desirable? Building upon previous work in robot ethics, this article tries to clarify and refine our understanding of the ethics of robotic deception. It does so by making three arguments. First, it argues that we need to distinguish between three main forms of robotic deception (external state deception; superficial state deception; and hidden state deception) in order to think clearly about its ethics. Second, it argues that the second type of deception—superficial state deception—is not best thought of as a form of deception, even though it is frequently criticised as such. And third, it argues that the third type of deception is best understood as a form of betrayal because doing so captures the unique ethical harm to which it gives rise, and justifies special ethical protections against its use.

Deception Against Near-Field Synthetic Aperture Radar Using Networked Jammers

We propose a new deceptive jamming method using networked receivers to perform deceptive jamming against multimode synthetic aperture radar. Using multiple receivers to measure the time difference of beam arrival, we solve the instantaneous modulation parameters precisely via linear equations. We then solve the time delays and frequency offsets of the deceptive signal without approximation to avoid accumulating electronic reconnaissance errors with traditional deceptive methods. Additionally, we design the distribution of receivers to minimize the condition number, reducing our method's sensitivity to measurement error. Simulation results prove the effectiveness of the proposed method.

Active Defense Strategy Selection Method Based on Two-Way Signaling Game

Most network security research studies based on signaling games assume that either the attacker or the defender is the sender of the signal and the other party is the receiver of the signal. The attack and defense process is commonly modeled and analyzed from the perspective of one-way signal transmission. Aiming at the reality of two-way signal transmission in network attack and defense confrontation, we propose a method of active defense strategy selection based on a two-way signaling game. In this paper, a two-way signaling game model is constructed to analyze the network attack and defense processes. Based on the solution of a perfect Bayesian equilibrium, a defense strategy selection algorithm is presented. The feasibility and effectiveness of the method are verified using examples from real-world applications. In addition, the mechanism of the deception signal is analyzed, and conclusions for guiding the selection of active defense strategies are provided.

Robust Partial-Nodes-Based State Estimation for Complex Networks Under Deception Attacks.

In this paper, the partial-nodes-based state estimators (PNBSEs) are designed for a class of uncertain complex networks subject to finite-distributed delays, stochastic disturbances, as well as randomly occurring deception attacks (RODAs). In consideration of the likely unavailability of the output signals in harsh environments from certain network nodes, only partial measurements are utilized to accomplish the state estimation task for the addressed complex network with norm-bounded uncertainties in both the network parameters and the inner couplings. The RODAs are taken into account to reflect the compromised data transmissions in cyber security. We aim to derive the gain parameters of the estimators such that the overall estimation error dynamics satisfies the specified security constraint in the simultaneous presence of stochastic disturbances and deception signals. Through intensive stochastic analysis, sufficient conditions are obtained to guarantee the desired security performance for the PNBSEs, based on which the estimator gains are acquired by solving certain matrix inequalities with nonlinear constraints. A simulation study is carried out to testify the security performance of the presented state estimation method.

A novel set-membership estimation approach for preserving security in networked control systems under deception attacks

This paper is concerned with the security problem for a networked control system subject to deception attacks. An adversary is present to corrupt both the sensor-to-controller channel and the controller-to-actuator channel with deception signals when sensor and control data are transmitted via the network medium. We model the malicious deception signals as unknown-but-bounded signals in such a way that no priori knowledge of the attack signals is required. First, a delicate set-membership performance index is proposed for meeting the system security requirement. Second, a novel set-membership estimation method is developed to obtain the desired ellipsoidal estimation set which guarantees the enclosing of true state of the system at every instant of time. Such an ellipsoidal estimation set is then used for deriving the sufficient conditions such that the resulting closed-loop system is set-membership input-to-state-stable and achieves the prescribed security requirement. Finally, a target tracking system is given to demonstrate the effectiveness of the proposed method.