Deceptive Signals Research Articles

The kinematics of false intent Conveyed by deceptive sidestep actions

Researchers have identified kinematic differences between deceptive and non-deceptive rugby reorientation actions. However, the honest and deceptive signals corresponded to ‘deception detection’ (accuracy increasing) rather than signals that caused deception (accuracy decreasing). In this study, statistical parametric mapping and multilevel modelling were applied to examine the kinematic differences between sidestep and non-deceptive actions during the time window of deception. The analysis compared three-dimensional motion capture data from 144 deceptive actions and 144 genuine actions performed by six high-skilled rugby players. Results indicated that the kinematics of deceptive actions were characterized by a combination of exaggerated head roll, outside foot and centre-of-mass displacement, and attenuated thorax roll and yaw relative to genuine actions. These are candidate sources for the cause of deception, either individually or in combination with other sources. Furthermore, the results indicate that previously identified ‘honest’ signals may not be reliable sources of information earlier in the action sequence.

Finite-time fuzzy cooperative control for multi-AUV systems under cyber-attacks with hybrid unknown nonlinearities

In this paper, we investigate the fuzzy cooperative control and collision-free problems of discrete-time multiple autonomous underwater vehicles (AUVs) systems subject to unknown nonlinearities and constrained communication under cyber-attacks. A cooperative controller composed of three components is proposed for each AUV follower. The first component maneuvers the position and velocity for tracking desired signals by the feedback information. The repulsion part protects the AUV from collision with environmental obstacles or other vehicles. The remaining nonlinearities compensation term is employed to compensate for the system’s nonlinearities. Notably, the fuzzy logic systems (FLSs) approximate the hybrid unknown term constituted by external disturbances, unmodeled dynamics and deceptive signals. Then, the unknown terms identification and cooperative control problems are transformed into an error system stability problem. Inequality conditions for finite-time bounded error signals are presented through stability analysis. Also, it is theoretically proven that the cooperative control objective with collision avoidance can be accomplished by using suitable controller parameters. Lastly, the simulation results demonstrate the effectiveness of the complete control framework. Comparisons of various scenarios also affirm the significance of the FLSs and repulsion control for the multi-AUV cooperative control system.

Deceptive pollinator lures benefit from physical and perceptual proximity to flowers.

Predators often use deception to exploit sensory and cognitive biases in prey. In pollinating insects, these include preferences for conspicuous colours associated with flowers, which predators such as orb-web spiders display as prey lures. Theory predicts that deceptive signal efficacy should covary with both their perceptual similarity and physical proximity to the resources-here, flowers-whose cues they are imitating. Here I used the colour-polymorphic jewelled spider Gasteracantha fornicata to test this prediction. I first examined spiders' capture success in the field, and found their visual resemblance and physical proximity to flowers interacted to mediate capture rates, with colour-similarity becoming increasingly important as the distance between spiders and flowers decreased. I then replicated this interaction experimentally. Spiders adjacent to colour-matched flowers enjoyed heightened capture success relative to those with nearby but colour-mismatched flowers. While spiders with flowers placed at a distance (irrespective of colour) recorded the fewest captures. These results support 'neighbourhood' effects in aggressive deception as receivers' vulnerability to exploitation is mediated by the local signalling community. More generally, they emphasise the importance of the broader information landscape in the ecology of communication, and suggest misinformation is most effective when physically and perceptually proximate to the truth.

Sensory trap leads to reliable communication without a shift in nonsexual responses to the model cue.

The sensory trap model of signal evolution suggests that males manipulate females into mating using traits that mimic cues used in a nonsexual context. Despite much empirical support for sensory traps, little is known about how females evolve in response to these deceptive signals. Female sea lamprey (Petromyzon marinus) evolved to discriminate a male sex pheromone from the larval odor it mimics and orient only toward males during mate search. Larvae and males release the attractant 3-keto petromyzonol sulfate (3kPZS), but spawning females avoid larval odor using the pheromone antagonist, petromyzonol sulfate (PZS), which larvae but not males, release at higher rates than 3kPZS. We tested the hypothesis that migratory females also discriminate between larval odor and the male pheromone and orient only to larval odor during anadromous migration, when they navigate within spawning streams using larval odor before they begin mate search. In-stream behavioral assays revealed that, unlike spawning females, migratory females do not discriminate between mixtures of 3kPZS and PZS applied at ratios typical of larval versus male odorants. Our results indicate females discriminate between the sexual and nonsexual sources of 3kPZS during but not outside of mating and show sensory traps can lead to reliable sexual communication without females shifting their responses in the original context.

Research on adaptive artificial intelligence algorithm in signal denoising and enhancement

Abstract Multimodal signals are susceptible to external environmental disturbances, such as weather conditions, electromagnetic interference, etc., which may affect the accuracy and stability of the data. This paper utilizes the characteristics of continuous and discrete wavelet transforms to study the wavelet threshold denoising algorithm in-depth, and by adjusting the parameters therein, it avoids the problem that the traditional threshold function is set to zero when the wavelet coefficients are smaller than the threshold value. Then, the deep learning algorithm in artificial intelligence is used to complete the signal adaptive denoising and realize the suppression of deceptive signal interference. The test results show that the adaptive denoising optimization model based on the deep learning algorithm has significantly improved signal characteristics compared to the original signal. Under a 15% noise environment, the average error of the model is less than 0.1, and the signal-to-noise gain of the signal is 5.0547 dB, which can reliably complete the interference suppression of both multiple interference and single interference and realize the adaptive anti-jamming optimization of the signal.

Deception in double extortion ransomware attacks: An analysis of profitability and credibility

Ransomware attacks have evolved with criminals using double extortion schemes, where they signal data exfiltration to inflate ransom demands. This development is further complicated by information asymmetry, where victims are compelled to respond to ambiguous and often deceptive signals from attackers. This study explores the complex interactions between criminals and victims during ransomware attacks, especially focusing on how data exfiltration is communicated. We use a signaling game to understand the strategies both parties use when dealing with uncertain information. We identify five distinct equilibria, each characterized by the criminals' varied approaches to signaling data exfiltration, influenced by the strategic parameters inherent in each attack scenario. Calibrating the game parameters with real-world like values, we identify the most probable equilibrium, offering insights into anticipated ransom amounts and corresponding payoffs for both victims and criminals. Our findings suggest criminals are likely to claim data exfiltration, true or not, highlighting a strategic advantage for intensifying attack efforts. The study underscores the need for victims' caution towards criminals' claims and highlights the unintended consequences of policies making false claims costlier for criminals.

Localization of GNSS Spoofing Interference Source Based on a Moving Array Antenna

GNSS spoofing interference utilizes falsified navigation signals to launch attacks on GNSS systems, posing a significant threat to applications that rely on GNSS signals for positioning, navigation, and time services. Therefore, achieving effective localization of the sources causing spoofing interference is crucial in ensuring the secure operation of GNSS. This article proposes a method for locating GNSS spoofing interference sources using a moving array antenna. Firstly, the proposed method utilizes the inherent characteristics of the double-differenced carrier phase from the deception signals received by the array antenna to effectively extract the spoofing signals. Subsequently, by moving the antenna array, the original carrier phase single-difference data of multiple observation points for deception signals are fused to provide a cost function for direct localization of spoofing interference, and a solution method for the cost function is designed. The proposed method addresses the challenge of extracting and localizing GNSS spoofing interference weak signals, effectively avoiding the data correlation of traditional two-step methods for DOA estimation parameters and ensuring the location accuracy of spoofing interference and the robustness of the method. The effectiveness of the proposed method has been validated through simulation experiments, and its adaptability to factors such as errors in carrier phase measurements has been examined. The method exhibits strong applicability and is well-suited for the hardware platform of the GNSS nulling antenna, thereby enabling it to possess simultaneous capabilities in both anti-interference and spoofing interference localization.

Distributed hybrid control for heterogeneous multi-agent systems subject to deception attacks and its application to secondary control for DC microgrid

This paper addresses the mean-square quasi-consensus problem for a class of heterogeneous multi-agent systems (MASs) with cascade-type two-layer structure subject to discrete-time deception attacks. A two-layer distributed hybrid controller is proposed based on the structural characteristics of the considered MASs. The deception attacks are supposed to occur in the upper-layer communication channel and their occurrences are modeled by a Bernoulli process. The discrete-time deception signal is considered as a bounded external disturbance sequence, so the mean-square quasi-consensus problem can be reduced to a mean-square exponential input-to-state stability (MSEISS) problem. A novel MSEISS analysis method combined with the use of a weighted discontinuous Lyapunov function is developed to establish a MSEISS criterion of the consensus error system, where the robustness performance of the mean-square quasi-consensus with respect to the randomly occurring deception attacks is quantified by the MSEISS gain. The effectiveness of the proposed resilient distributed hybrid control algorithm is verified through numerical simulations. Specifically, the proposed resilient control algorithm is applied to the secondary control of DC microgrid with discrete interaction. Several case studies show that the resulting resilient distributed hybrid secondary control algorithm has a good robustness performance on resisting the deception attacks.

Dynamic‐event‐based state estimation of complex networks against attacks and innovation outliers: The probability constraint case

AbstractThis article is devoted to the state estimation issue under probability constraint for a class of time‐varying stochastic complex networks with uncertain inner couplings and cyber‐attacks. To save the communication energy, the dynamic event‐triggered strategy is deployed in each individual sensor‐to‐estimator channel to govern the data transmissions. A multi‐attack model incorporating two different deception signals is considered to describe the phenomenon of randomly occurring cyber‐attacks. The saturation‐type function is involved to reduce the possible innovation outliers resulting from the hostile deception signals. A sufficient condition is first developed to guarantee the probability performance constraint on estimation error dynamics over certain finite horizon, and then the suitable state estimator gains are derived by means of a set of matrix inequalities. Subsequently, a recursive algorithm is put forward for the addressed finite‐horizon state estimator design problem. Finally, the validity of the proposed state estimation scheme is testified by a numerical example.

Distributed Model-Free Adaptive Control for MIMO Nonlinear Multiagent Systems Under Deception Attacks

The consensus tracking and containment control problems of multiple-input and multiple-output (MIMO) nonaffine nonlinear multiagent systems (MASs) are studied in this article under deception attacks using the distributed model-free adaptive control (DMFAC) method. An equivalent dynamic linearized data model of MIMO MASs’s distributed output vector containing deception signals is established using dynamic linearization technology. Then, a fully data-driven DMFAC strategy is designed just using I/O information instead of the knowledge of mathematical model. Furthermore, the boundedness of distributed output vector of MIMO-MASs under deception attacks is proved through the contraction mapping principle without employing global topology graph information. Finally, the validity of the proposed DMFAC scheme is verified through detailed simulations.

Protocol-Based Fuzzy Control of Networked Systems Under Joint Deception Attacks

The security control issue of nonlinear networked systems is considered in this article on the basis of interval type-2 fuzzy modeling strategy. For avoiding communication congestion, a stochastic scheduling strategy called Markovian communication protocol is introduced to coordinate the sensor transmission order. An asynchronous observer is designed for estimating the unmeasured states via the hidden Markov model. In addition, a more comprehensive scenario on deception attack is considered, in which attacks occur both in the sensor–observer and controller–actuator communication channels with different types of deception signals. In view of slack matrix approach and stochastic analysis technique, some sufficient conditions for ensuring the ultimately boundedness of the resulting closed-loop system are obtained. In the end, simulations show the validity of the proposed protocol-based fuzzy control method.

Signaling High Sensitivity to Influence Others: Initial Evidence for the Roles of Reinforcement Sensitivity, Sensory Processing Sensitivity, and the Dark Triad.

Signaling high sensitivity to others may help individuals with heightened sensitivity to stimuli to craft their social relations to a preferred activation level ("assertive signaling of specific needs" hypothesis). Signaling sensitivity could be also a deceptive strategy helping to extract resources or obtain the desired treatment ("deceptive signaling" hypothesis). The study presents the construction of the measure of signaling high sensitivity to influence others and an initial test of both hypotheses. Two hundred and one participants ranging in age from 18 to 67 (M = 25.6; SD = 7.03) reported on their reinforcement sensitivity (behavioral approach and inhibition system), sensory processing sensitivity, and the Dark Triad. Signaling high sensitivity to influence others was weakly, but significantly, associated with behavioral inhibition system sensitivity, which was consistent with the "assertive signaling of specific needs" hypothesis. The behavioral approach system was positively correlated with signaling high sensitivity, which indicated that this influence strategy could be treated as an assertive self-presentational strategy helping in achieving social rewards for individuals with high reward responsiveness. Narcissism and psychopathy accounted for an additional amount of variance in signaling high sensitivity over and beyond reinforcement sensitivity. This was consistent with the "deceptive signaling" hypothesis. Signaling high sensitivity could be regarded as a strategy to achieve social goals using deception rather than as an expression of sensitive personality.

Detection and Orientation of GNSS Spoofing Based on Positioning Solutions of Three Receivers

Spoofing poses a serious security threat to the satellite navigation system. Most of the current spoofing detection methods are based on the information in the receiver-solving process. Applying the above spoofing detection algorithm on the receiver requires a redesign of the original receiver, which limits the application of the deceptive detection method on the ex-factory receiver. In the case of single-station spoofing, the positions of the successfully spoofed multiple receivers are the same according to the deception signal, but the clock offset is different. The single-difference clock offset is positively correlated with the distance between the two receivers. Based on the above principles, this paper proposes a spoofing detection and spoofing direction method based on the positioning solutions of three receivers. The observation value of the baseline length is calculated from the position information in the positioning solution, and spoofing detection is realized by hypothesis testing. The clocks of the three receivers are synchronized. The Adaptive Delta Gradient Descent(ADGD) method is adopted to calculate the spoofing direction according to the single-difference clock offset in the positioning solution. The direction angle is optimized through the Interacting Multiple Model based on the volume Kalman Filter. The relationship between several parameters and the direction accuracy of spoofing is analyzed through simulation tests. Experimental results show that this method can effectively detect and orient spoofing. In addition, the detection and orientation method of spoofing has the advantage of fast response speed, which can realize fast spoofing detection and direction finding in a deception environment.

Guaranteed performance impulsive tracking control of multi-agents systems under discrete-time deception attacks

This paper is concerned with the impulsive consensus tracking problem of Lipschitz nonlinear multi-agent systems subject to deception attacks. The proposed distributed impulsive tracking algorithm allows only partial state variables of every agent to be governed by consensus impulses. The considered deception attacks are supposed to take place in the controller–actuator channel, and can be modelled by a set of Bernoulli processes. By considering the discrete-time deception signal as bounded external disturbance, the consensus tracking problem reduces to the exponential input-to-state stable (EISS) problem of the tracking error system. The EISS-gain is used as an assessment index in measuring the capacity of consensus control laws for attenuating attacks. Specifically, a novel EISS analysis method combined with the use of a weighted discontinuous Lyapunov function is proposed to establish the consensus tracking criterion. A convex optimization problem with linear matrix inequalities-based constraints is formulated to design the guaranteed performance distributed impulsive controller, which makes the EISS-gain as small as possible. Two illustrated examples quantify the effectiveness of the proposed analysis and design approach.

The deception effect of FDA counteracting amplitude-comparison monopulse reconnaissance

Conventional phased array radar can direct the high-gain beam, resulting in high visibility under the reconnaissance system and even direct exposure to the enemy. As an amplitude-based direction-finding system, the amplitude monopulse system can recognize the traditional radar signals. However, there are still some issues with the countermeasures of the monopulse system, so it is necessary to find better styles of jamming. Since the beam of the frequency diverse array (FDA) signal at a certain angle varies with the range, we studied the deception effect of the FDA against the amplitude-comparison monopulse direction-finding system. By analyzing the established FDA-based direction-of-arrival location deception model against the amplitude-comparison monopulse system, we found that the introduction of the frequency increment might cause the FDA signals to deceit the monopulse system in principle. Then, to indicate the superiority of our deception method, we obtain the instantaneous signal-to-noise ratio, Cramér–Rao lower bound, root-means-square error, and variation. Numerical simulation results demonstrate that the FDA can generate deceptive signals to counteract the amplitude-comparison monopulse direction-finding system.

Deceptive signal generating optimization based on frequency diverse array

The direction and position of the radar signals are essential information in the radar countermeasure, and with the development of the reconnaissance system, the radar is facing more severe threats. However, the high-gain beam and even-distribute phase of the phased-array radar restrict the further development of radar protection measures. Therefore, considering the angle-range-time-coupled beam and phase of the frequency diverse array (FDA), we proposed a deceptive signal generating method based on the FDA. Firstly, the amplitude- and phase-based reconnaissance countermeasures were established, respectively. Moreover, the regulation ability of the frequency increment sequence was studied. Therefore, by finding the appreciated frequency increment sequence that can regulate the FDA signals, the proposed FDA can generate deceptive signals that counter both the amplitude- and phase-based reconnaissance system. The instantaneous signal-to-noise ratio and Cramér–Rao lower bound were considered to verify the method. Numerical examples and simulations showed that the proposed countermeasure could generate deceptive signals with good error tolerance, whether in a noisy or non-noise environment.

Synthetic Aperture Radar Active Decoy

Synthetic aperture radar (SAR) has been widely involved in military reconnaissance and detection of moving targets. SAR active decoy can generate deceptive jamming signal and it can severely affect the content of SAR focused image. SAR range Doppler algorithm (RDA) is considered as one of the most common algorithms utilized in the image formation processor (IFP) in SAR sensors. In this paper, the inverse RDA (IRDA) deception jamming technique and its evaluation criteria on SAR are demonstrated. Mathematical formulations for SAR RDA with and without deception jamming are presented. Matlab simulation and results of SAR RDA under the proposed deception scenario are discussed. SAR point target simulation of the proposed deceptive jamming and false target insertion into real large scene are also introduced. Comparison of the proposed deceptive jamming to signal power ratio (JSR) required to counter SAR with other SAR jamming techniques is analyzed.

The broken-wing display across birds and the conditions for its evolution.

The broken-wing display is a well-known and conspicuous deceptive signal used to protect birds' broods against diurnal terrestrial predators. Although commonly associated with shorebirds, it remains unknown how common the behaviour is across birds and what forces are associated with the evolution of the display. Here, we use the broken-wing display as a paradigmatic example to study the evolution of a behaviour across Aves. We show that the display is widespread: it has been described in 52 families spread throughout the phylogeny, suggesting that it independently evolved multiple times. Further, we evaluated the association with 16 ecological and life-history variables hypothesized to be related to the evolution of the broken-wing display. Eight variables were associated with the display. We found that species breeding farther from the equator, in more dense environments, with shorter incubation periods, and relatively little nest cover were more likely to perform the display, as were those in which only one parent incubates eggs, species that mob nest predators and species that are altricial or multi-brooded. Collectively, our comprehensive approach identified forces associated with the repeated evolution of this conspicuous display, thereby providing new insights into how deceptive behaviours evolve in the context of predator–prey interactions.

The Role of Deception in a Game of ‘Hide and Seek’

This paper introduces the problem of active deception into the literature on search theory. We consider a game of ‘hide and seek’ in which a hider chooses to hide in one of many cells. Our hider also wishes to remain active which runs the risk of signaling his location. The searcher can look in any cell within the hider’s feasible operating environment but must prioritize his search. To do so, he looks for and has the option of following any indicator of the hider’s possible whereabouts. In conducting his search, however, he also risks signaling where he is about to look next. This gives the hider an opportunity to evade. Each player can employ ‘deceptive signals’, at some cost, to improve their respective chances of success. We examine the nature of the tradeoffs involved in deciding whether or not to use deceptive tactics under different assumptions about the nature of the players’ common operating environment.

Self-attention-based multi-agent continuous control method in cooperative environments

Cooperative problems under continuous control have always been the focus of multi-agent reinforcement learning. Existing algorithms suffer from the problem of uneven learning degree with the increase of the number of agents. In this paper, a new structure for a multi-agent actor critic is proposed, and the self-attention mechanism is applied in the critic network and the value decomposition method used to solve the uneven problem. The proposed algorithm makes full use of the samples in the replay memory buffer to learn the behavior of a class of agents. First, a new update method is proposed for policy networks that promotes learning efficiency. Second, the utilization of samples is improved, at the same time reflecting the ability of perspective-taking among groups. Finally, the “deceptive signal” in training is eliminated and the learning degree among agents is more uniform than in the existing methods. Multiple experiments were conducted in two typical scenarios of a multi-agent particle environment. Experimental results show that the proposed algorithm can perform better than the state-of-the-art ones, and that it exhibits higher learning efficiency with an increasing number of agents.