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.

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.

Hybrid Metaheuristics with Deep Learning Enabled Automated Deception Detection and Classification of Facial Expressions

Automatic deception recognition has received considerable atten-tion from the machine learning community due to recent research on its vast application to social media,interviews,law enforcement,and the mil-itary.Video analysis-based techniques for automated deception detection have received increasing interest.This study develops a new self-adaptive population-based firefly algorithm with a deep learning-enabled automated deception detection(SAPFF-DLADD)model for analyzing facial cues.Ini-tially,the input video is separated into a set of video frames.Then,the SAPFF-DLADD model applies the MobileNet-based feature extractor to produce a useful set of features.The long short-term memory(LSTM)model is exploited for deception detection and classification.In the final stage,the SAPFF technique is applied to optimally alter the hyperparameter values of the LSTM model,showing the novelty of the work.The experimental validation of the SAPFF-DLADD model is tested using the Miami University Deception Detection Database(MU3D),a database comprised of two classes,namely,truth and deception.An extensive comparative analysis reported a better performance of the SAPFF-DLADD model compared to recent approaches,with a higher accuracy of 99%.

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.

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.

Alicyclobacillus sp. strain CC2, a thermo-acidophilic bacterium isolated from Deception Island(Antarctica) containing a thermostable superoxide dismutase enzyme

A gram-positive, rod-shaped, aerobic, thermo-acidophilic bacterium CC2 （optimal temperature 55℃ and pH 4.0）, belonging to the genus Alicyclobacillus was isolated from geothermal soil collected from ＂Cerro Caliente＂, Deception Island, Antarctica. Owing to the harsh environmental conditions found in this territory, microorganisms are exposed to conditions that trigger the generation of reactive oxygen species （ROS）. They must have an effective antioxidant defense system to deal with this oxidative stress. We focused on one of the most important enzymes： superoxide dismutase, which was partially purified and characterized. This study presents the first report of a thermo-acidophilic bacterium isolated from Deception lsland with a thermostable superoxide dismutase （SOD）.

An online fast multi-track locating algorithm for high-resolution single-event effect test platform

To improve the efficiency and accuracy of single-event effect(SEE)research at the Heavy Ion Research Facility at Lanzhou,Hi’Beam-SEE must precisely localize the position at which each heavy ion hitting the integrated circuit(IC)causes SEE.In this study,we propose a fast multi-track location(FML)method based on deep learning to locate the position of each particle track with high speed and accuracy.FML can process a vast amount of data supplied by Hi’Beam-SEE online,revealing sensitive areas in real time.FML is a slot-based object-centric encoder-decoder structure in which each slot can learn the location information of each track in the image.To make the method more accurate for real data,we designed an algorithm to generate a simulated dataset with a distribution similar to that of the real data,which was then used to train the model.Extensive comparison experiments demonstrated that the FML method,which has the best performance on simulated datasets,has high accuracy on real datasets as well.In particular,FML can reach 238 fps and a standard error of 1.6237μm.This study discusses the design and performance of FML.

A NEW DYNAMIC DEFENSE MODEL BASED ON ACTIVE DECEPTION

Aiming at the traditional passive deception models,this paper constructs a Decoy Platform based on Intelligent Agent(DPIA) to realize dynamic defense.The paper explores a new dynamic defense model based on active deception,introduces its architecture,and expatiates on communication methods and security guarantee in information transference.Simulation results show that the DPIA can attract hacker agility and activity,lead abnormal traffic into it,distribute a large number of attack data,and ensure real network security.

Cutting Edge Trends in Deception Based Intrusion Detection Systems—A Survey

Cyber criminals have become a formidable treat in today’s world. This present reality has placed cloud computing platforms under constant treats of cyber-attacks at all levels, with an ever-evolving treat landscape. It has been observed that the number of threats faced in cloud computing is rising exponentially mainly due to its widespread adoption, rapid expansion and a vast attack surface. One of the front-line tools employed in defense against cyber-attacks is the Intrusion Detection Systems (IDSs). In recent times, an increasing number of researchers and cyber security practitioners alike have advocated the use of deception-based techniques in IDS and other cyber security defenses as against the use of traditional methods. This paper presents an extensive overview of the deception technology environment, as well as a review of current trends and implementation models in deception-based Intrusion Detection Systems. Issues mitigating the implementation of deception based cyber security defenses are also investigated.

Recognition and interfere deceptive behavior based on inverse reinforcement learning and game theory

In real-time strategy(RTS)games,the ability of recognizing other players’goals is important for creating artifical intelligence(AI)players.However,most current goal recognition methods do not take the player’s deceptive behavior into account which often occurs in RTS game scenarios,resulting in poor recognition results.In order to solve this problem,this paper proposes goal recognition for deceptive agent,which is an extended goal recognition method applying the deductive reason method(from general to special)to model the deceptive agent’s behavioral strategy.First of all,the general deceptive behavior model is proposed to abstract features of deception,and then these features are applied to construct a behavior strategy that best matches the deceiver’s historical behavior data by the inverse reinforcement learning(IRL)method.Final,to interfere with the deceptive behavior implementation,we construct a game model to describe the confrontation scenario and the most effective interference measures.

空射诱饵弹对地面防空装备作战使用影响研究

空射诱饵弹(miniature air launched decoy,MALD)可诱骗地面防空雷达开机,消耗防空弹药,降低地面防空装备作战效能,提升空中编队突防能力。在梳理空射诱饵弹发展概况基础上,分析空射诱饵弹目标特性,构建典型作战运用场景,开展MALD对制导雷达探测跟踪性能和拦截效能影响分析,采用理论分析和动态仿真的方法研究了空射诱饵弹实施远距离欺骗、抵近干扰对制导雷达探测跟踪性能的影响,采用排队论方法分析MALD对空中编队突防效能的影响。研究结论可为空射诱饵弹战术运用提供参考。

基于主动欺骗的反勒索软件方法

考虑到勒索软件对数据安全构成的严重威胁及其攻击手段的日益智能化和复杂化,针对传统防御方法的局限性,提出了一种基于主动欺骗的反勒索软件方法。结合静态启发式算法和动态启发式算法对欺骗文件进行动态部署,在此基础上建立了基于主动欺骗的动态文件安全模型。针对不同风险级别的勒索软件,采用不同的策略生成动态欺骗文件,通过模拟真实数据的特征来迷惑勒索软件,使其无法区分真实数据和欺骗数据,从而保护用户的真实数据不被加密或破坏。实验结果表明,所提方法有效增加了文件的动态性、多样性和欺骗性,大幅扩展了数据攻击面的转换空间,能够有效地抵御勒索软件攻击。

欺骗谋略在网络空间防御行动中运用

军事欺骗是谋略运用的重要内容。结合网络空间攻防行动特点,剖析了网络欺骗的原理;借鉴运用博弈理论,分析了欺骗谋略在网络空间防御行动中的制胜机理;总结了支撑网络欺骗实施的蜜罐蜜网、拟态防御和移动目标防御等3种主要技术手段的原理及研究重点;给出了欺骗谋略在网络空间防御行动中的组织运用方法。研究成果对于提升网络空间防御效能具有理论价值和实践指导意义。

注册商标无效宣告绝对事由的正解与修订

注册商标无效宣告的绝对事由需要在立法上进行必要的完善,回归宣告无效针对不正当注册的原意,确保商标核准注册与无效宣告程序中绝对事由的对应性和同一性,重点是尽快去除“欺骗手段或者其他不正当手段”的独立无效事由。在既有法律规定下,绝对无效事由的适用应当保持必要的谦抑,避免适用上的扩大化或者扭曲化。当前的商标确权实践应当突出强调实际使用的优越地位,避免过于强调商标申请注册时的“原罪”而无视实际使用的现实,导致无效宣告“舍本逐末”“缘木求鱼”;要真正认真对待两种事由的法律界限,尊重其区分价值,避免动辄以绝对事由变相发挥保护在先权利的相对事由功能,防止以绝对事由为名变相扩展在先权利,维护权利保护与商标秩序的平衡。

竞争情境下奖赏动机对自我欺骗的影响:眼动证据

为了探讨奖赏动机和竞争情境对自我欺骗的影响,实验1采用前瞻范式探索自我欺骗效应及奖赏动机对自我欺骗的影响,实验2探讨奖赏动机和竞争情境对自我欺骗的影响,实验3采用眼动技术探讨两者对自我欺骗的视觉认知机制。结果发现:(1)相比较无答案组,有答案组在金钱奖赏下的预测成绩更加接近真实水平;(2)竞争和奖赏产生的合力增加了自我欺骗;(3)相比较仅被启动奖赏动机或竞争情境的被试,奖赏-竞争组在True和False兴趣区的平均注视时间更短,自我欺骗行为增加。这些结果表明金钱激励诱发的奖赏动机减少自我欺骗程度,但竞争情境能促进自我欺骗发生。

基于相对拟牛顿法的自卫式欺骗干扰抑制算法

自卫式欺骗干扰与目标信号高度相似,且二者的到达角完全相同,传统的主瓣干扰抑制算法难以对其进行抑制。针对该问题,文中在极化单输入多输出(PSIMO)雷达系统下,提出一种基于相对拟牛顿法的盲源分离算法。该算法利用干扰和目标的极化特性差异,通过构建重叠子阵结构计算出联合自相关矩阵,并采用相对拟牛顿法估计出分离矩阵,从而将目标和干扰信号分离在不同的通道上,实现干扰抑制作用。仿真实验结果表明,该算法能够有效抑制自卫式欺骗干扰,且在低信噪比(SNR)和密集干扰场景下依然具有良好的干扰抑制性能,当输入SNR为-10 dB时,输出的目标检测概率仍可以达到51.6%,拥有较强的鲁棒性。