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Naik, N., Jenkins, P., Savage, N., Yang, L., Boongoen, T., Iam-On, N..  2020.  Fuzzy-Import Hashing: A Malware Analysis Approach. 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). :1–8.
Malware has remained a consistent threat since its emergence, growing into a plethora of types and in large numbers. In recent years, numerous new malware variants have enabled the identification of new attack surfaces and vectors, and have become a major challenge to security experts, driving the enhancement and development of new malware analysis techniques to contain the contagion. One of the preliminary steps of malware analysis is to remove the abundance of counterfeit malware samples from the large collection of suspicious samples. This process assists in the management of man and machine resources effectively in the analysis of both unknown and likely malware samples. Hashing techniques are one of the fastest and efficient techniques for performing this preliminary analysis such as fuzzy hashing and import hashing. However, both hashing methods have their limitations and they may not be effective on their own, instead the combination of two distinctive methods may assist in improving the detection accuracy and overall performance of the analysis. This paper proposes a Fuzzy-Import hashing technique which is the combination of fuzzy hashing and import hashing to improve the detection accuracy and overall performance of malware analysis. This proposed Fuzzy-Import hashing offers several benefits which are demonstrated through the experimentation performed on the collected malware samples and compared against stand-alone techniques of fuzzy hashing and import hashing.
Razaque, Abdul, Almiani, Muder, khan, Meer Jaro, Magableh, Basel, Al-Dmour, Ayman, Al-Rahayfeh, Amer.  2019.  Fuzzy-GRA Trust Model for Cloud Risk Management. 2019 Sixth International Conference on Software Defined Systems (SDS). :179–185.
Cloud computing is not adequately secure due to the currently used traditional trust methods such as global trust model and local trust model. These are prone to security vulnerabilities. This paper introduces a trust model based on the fuzzy mathematics and gray relational theory. Fuzzy mathematics and gray relational analysis (Fuzzy-GRA) aims to improve the poor dynamic adaptability of cloud computing. Fuzzy-GRA platform is used to test and validate the behavior of the model. Furthermore, our proposed model is compared to other known models. Based on the experimental results, we prove that our model has the edge over other existing models.
Selvi, M., Logambigai, R., Ganapathy, S., Ramesh, L. Sai, Nehemiah, H. Khanna, Arputharaj, Kannan.  2016.  Fuzzy Temporal Approach for Energy Efficient Routing in WSN. Proceedings of the International Conference on Informatics and Analytics. :117:1–117:5.

Wireless sensor networks (WSN) are useful in many practical applications including agriculture, military and health care systems. However, the nodes in a sensor network are constrained by energy and hence the lifespan of such sensor nodes are limited due to the energy problem. Temporal logics provide a facility to predict the lifetime of sensor nodes in a WSN using the past and present traffic and environmental conditions. Moreover, fuzzy logic helps to perform inference under uncertainty. When fuzzy logic is combined with temporal constraints, it increases the accuracy of decision making with qualitative information. Hence, a new data collection and cluster based energy efficient routing algorithm is proposed in this paper by extending the existing LEACH protocol. Extensions are provided in this work by including fuzzy temporal rules for making data collection and routing decisions. Moreover, this proposed work uses fuzzy temporal logic for forming clusters and to perform cluster based routing. The main difference between other cluster based routing protocols and the proposed protocol is that two types of cluster heads are used here, one for data collection and other for routing. In this research work we conducted an experiment and it is observed that the proposed fuzzy cluster based routing algorithm with temporal constrains enhances the network life time reduces the energy consumption and enhances the quality of service by increasing the packet delivery ratio by reducing the delay.

Sun, J., Ma, J., Quan, J., Zhu, X., I, C..  2019.  A Fuzzy String Matching Scheme Resistant to Statistical Attack. 2019 International Conference on Networking and Network Applications (NaNA). :396–402.
The fuzzy query scheme based on vector index uses Bloom filter to construct vector index for key words. Then the statistical attack based on the deviation of frequency distribution of the vector index brings out the sensitive information disclosure. Using the noise vector, a fuzzy query scheme resistant to the statistical attack serving for encrypted database, i.e. S-BF, is introduced. With the noise vector to clear up the deviation of frequency distribution of vector index, the statistical attacks to the vector index are resolved. Demonstrated by lab experiment, S-BF scheme can achieve the secure fuzzy query with the powerful privation protection capability for encrypted cloud database without the loss of fuzzy query efficiency.
Zabihimayvan, Mahdieh, Doran, Derek.  2019.  Fuzzy Rough Set Feature Selection to Enhance Phishing Attack Detection. 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). :1-6.

Phishing as one of the most well-known cybercrime activities is a deception of online users to steal their personal or confidential information by impersonating a legitimate website. Several machine learning-based strategies have been proposed to detect phishing websites. These techniques are dependent on the features extracted from the website samples. However, few studies have actually considered efficient feature selection for detecting phishing attacks. In this work, we investigate an agreement on the definitive features which should be used in phishing detection. We apply Fuzzy Rough Set (FRS) theory as a tool to select most effective features from three benchmarked data sets. The selected features are fed into three often used classifiers for phishing detection. To evaluate the FRS feature selection in developing a generalizable phishing detection, the classifiers are trained by a separate out-of-sample data set of 14,000 website samples. The maximum F-measure gained by FRS feature selection is 95% using Random Forest classification. Also, there are 9 universal features selected by FRS over all the three data sets. The F-measure value using this universal feature set is approximately 93% which is a comparable result in contrast to the FRS performance. Since the universal feature set contains no features from third-part services, this finding implies that with no inquiry from external sources, we can gain a faster phishing detection which is also robust toward zero-day attacks.

Dai, Z., Li, Z. Y..  2015.  Fuzzy Optimization of Automobile Supply Chain Network of Considering Risks. 2015 Seventh International Symposium on Parallel Architectures Algorithms and Programming (PAAP). :134–138.

In this paper, an optimization model of automobile supply chain network with risks under fuzzy price is put forward. The supply chain network is composed of component suppliers, plants, and distribution centers. The total costs of automobile supply chain consist of variable costs, fixed costs, and transportation costs. The objective of this study is to minimize the risks of total profits. In order to deal with this model, this paper puts forward an approximation method to transform a continuous fuzzy problem into discrete fuzzy problem. The model is solved using Cplex 12.6. The results show that Cplex 12.6 can perfectly solve this model, the expected value and lower semi-variance of total profits converge with the increasing number of discretization points, the structure of automobile supply chain network keeps unchanged with the increasing number of discretization points.

Salaou, Allassane Issa, Ghomari, Abdelghani.  2021.  Fuzzy ontology-based complex and uncertain video surveillance events recognition. 2021 International Conference on Information Systems and Advanced Technologies (ICISAT). :1–5.

Nowadays, video surveillance systems are part of our daily life, because of their role in ensuring the security of goods and people this generates a huge amount of video data. Thus, several research works based on the ontology paradigm have tried to develop an efficient system to index and search precisely a very large volume of videos. Due to their semantic expressiveness, ontologies are undoubtedly very much in demand in recent years in the field of video surveillance to overcome the problem of the semantic gap between the interpretation of the data extracted from the low level and the high-level semantics of the video. Despite its good expressiveness of semantics, a classical ontology may not be sufficient for good handling of uncertainty, which is however commonly present in the video surveillance domain, hence the need to consider a new ontological approach that will better represent uncertainty. Fuzzy logic is recognized as a powerful tool for dealing with vague, incomplete, imperfect, or uncertain data or information. In this work, we develop a new ontological approach based on fuzzy logic. All the relevant fuzzy concepts such as Video\_Objects, Video\_Events, Video\_Sequences, that could appear in a video surveillance domain are well represented with their fuzzy Ontology DataProperty and the fuzzy relations between them (Ontology ObjectProperty). To achieve this goal, the new fuzzy video surveillance ontology is implemented using the fuzzy ontology web language 2 (fuzzy owl2) which is an extension of the standard semantic web language, ontology web language 2 (owl2).

El Halaby, Mohamed, Abdalla, Areeg.  2016.  Fuzzy Maximum Satisfiability. Proceedings of the 10th International Conference on Informatics and Systems. :50–55.

In this paper, we extend the Maximum Satisfiability (MaxSAT) problem to Łukasiewicz logic. The MaxSAT problem for a set of formulae Φ is the problem of finding an assignment to the variables in Φ that satisfies the maximum number of formulae. Three possible solutions (encodings) are proposed to the new problem: (1) Disjunctive Linear Relations (DLRs), (2)Mixed Integer Linear Programming (MILP) and (3)Weighted Constraint Satisfaction Problem (WCSP). Like its Boolean counterpart, the extended fuzzy MaxSAT will have numerous applications in optimization problems that involve vagueness.

Hasan, Md. Mahmudul, Jahan, Mosarrat, Kabir, Shaily, Wagner, Christian.  2021.  A Fuzzy Logic-Based Trust Estimation in Edge-Enabled Vehicular Ad Hoc Networks. 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). :1–8.

Trust estimation of vehicles is vital for the correct functioning of Vehicular Ad Hoc Networks (VANETs) as it enhances their security by identifying reliable vehicles. However, accurate trust estimation still remains distant as existing works do not consider all malicious features of vehicles, such as dropping or delaying packets, altering content, and injecting false information. Moreover, data consistency of messages is not guaranteed here as they pass through multiple paths and can easily be altered by malicious relay vehicles. This leads to difficulty in measuring the effect of content tampering in trust calculation. Further, unreliable wireless communication of VANETs and unpredictable vehicle behavior may introduce uncertainty in the trust estimation and hence its accuracy. In this view, we put forward three trust factors - captured by fuzzy sets to adequately model malicious properties of a vehicle and apply a fuzzy logic-based algorithm to estimate its trust. We also introduce a parameter to evaluate the impact of content modification in trust calculation. Experimental results reveal that the proposed scheme detects malicious vehicles with high precision and recall and makes decisions with higher accuracy compared to the state-of-the-art.

Singh, Neeraj Kumar, Mahajan, Vasundhara.  2019.  Fuzzy Logic for Reducing Data Loss during Cyber Intrusion in Smart Grid Wireless Network. 2019 IEEE Student Conference on Research and Development (SCOReD). :192–197.
Smart grid consists of smart devices to control, record and analyze the grid power flow. All these devices belong to the latest technology, which is used to interact through the wireless network making the grid communication network vulnerable to cyber attack. This paper deals with a novel approach using altering the Internet Protocol (IP) address of the smart grid communication network using fuzzy logic according to the degree of node. Through graph theory approach Wireless Communication Network (WCN) is designed by considering each node of the system as a smart sensor. In this each node communicates with other nearby nodes for exchange of data. Whenever there is cyber intrusion the WCN change its IP using proposed fuzzy rules, where higher degree nodes are given the preference to change first with extreme IP available in the system. Using the proposed algorithm, different IEEE test systems are simulated and compared with existing Dynamic Host Configuration Protocol (DHCP). The fuzzy logic approach reduces the data loss and improves the system response time.
Bolshakov, Alexander, Zhila, Anastasia.  2021.  Fuzzy Logic Data Protection Management. 2021 28th Conference of Open Innovations Association (FRUCT). :35—40.
This article discusses the problem of information security management in computer systems and describes the process of developing an algorithm that allows to determine measures to protect personal data. The organizational and technical measures formulated by the FSTEC are used as measures.
Johanyák, Z. C..  2020.  Fuzzy Logic based Network Intrusion Detection Systems. 2020 IEEE 18th World Symposium on Applied Machine Intelligence and Informatics (SAMI). :15—16.

Plenary Talk Our everyday life is more and more dependent on electronic communication and network connectivity. However, the threats of attacks and different types of misuse increase exponentially with the expansion of computer networks. In order to alleviate the problem and to identify malicious activities as early as possible Network Intrusion Detection Systems (NIDSs) have been developed and intensively investigated. Several approaches have been proposed and applied so far for these systems. It is a common challenge in this field that often there are no crisp boundaries between normal and abnormal network traffic, there are noisy or inaccurate data and therefore the investigated traffic could represent both attack and normal communication. Fuzzy logic based solutions could be advantageous owing to their capability to define membership levels in different classes and to do different operations with results ensuring reduced false positive and false negative classification compared to other approaches. In this presentation, after a short introduction of NIDSs a survey will be done on typical fuzzy logic based solutions followed by a detailed description of a fuzzy rule interpolation based IDS. The whole development process, i.e. data preprocessing, feature extraction, rule base generation steps are covered as well.

Naik, N., Jenkins, P., Kerby, B., Sloane, J., Yang, L..  2018.  Fuzzy Logic Aided Intelligent Threat Detection in Cisco Adaptive Security Appliance 5500 Series Firewalls. 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). :1-8.

Cisco Adaptive Security Appliance (ASA) 5500 Series Firewall is amongst the most popular and technically advanced for securing organisational networks and systems. One of its most valuable features is its threat detection function which is available on every version of the firewall running a software version of 8.0(2) or higher. Threat detection operates at layers 3 and 4 to determine a baseline for network traffic, analysing packet drop statistics and generating threat reports based on traffic patterns. Despite producing a large volume of statistical information relating to several security events, further effort is required to mine and visually report more significant information and conclude the security status of the network. There are several commercial off-the-shelf tools available to undertake this task, however, they are expensive and may require a cloud subscription. Furthermore, if the information transmitted over the network is sensitive or requires confidentiality, the involvement of a third party or a third-party tool may place organisational security at risk. Therefore, this paper presents a fuzzy logic aided intelligent threat detection solution, which is a cost-free, intuitive and comprehensible solution, enhancing and simplifying the threat detection process for all. In particular, it employs a fuzzy reasoning system based on the threat detection statistics, and presents results/threats through a developed dashboard user interface, for ease of understanding for administrators and users. The paper further demonstrates the successful utilisation of a fuzzy reasoning system for selected and prioritised security events in basic threat detection, although it can be extended to encompass more complex situations, such as complete basic threat detection, advanced threat detection, scanning threat detection, and customised feature based threat detection.

Korenda, Ashwija Reddy, Afghah, Fatemeh, Razi, Abolfazl, Cambou, Bertrand, Begay, Taylor.  2021.  Fuzzy Key Generator Design using ReRAM-Based Physically Unclonable Functions. 2021 IEEE Physical Assurance and Inspection of Electronics (PAINE). :1—7.
Physical unclonable functions (PUFs) are used to create unique device identifiers from their inherent fabrication variability. Unstable readings and variation of the PUF response over time are key issues that limit the applicability of PUFs in real-world systems. In this project, we developed a fuzzy extractor (FE) to generate robust cryptographic keys from ReRAM-based PUFs. We tested the efficiency of the proposed FE using BCH and Polar error correction codes. We use ReRAM-based PUFs operating in pre-forming range to generate binary cryptographic keys at ultra-low power with an objective of tamper sensitivity. We investigate the performance of the proposed FE with real data using the reading of the resistance of pre-formed ReRAM cells under various noise conditions. The results show a bit error rate (BER) in the range of 10−5 for the Polar-codes based method when 10% of the ReRAM cell array is erroneous at Signal to Noise Ratio (SNR) of 20dB.This error rate is achieved by using helper data length of 512 bits for a 256 bit cryptographic key. Our method uses a 2:1 ratio for helper data and key, much lower than the majority of previously reported methods. This property makes our method more robust against helper data attacks.
Alamaniotis, Miltiadis.  2021.  Fuzzy Integration of Kernel-Based Gaussian Processes Applied to Anomaly Detection in Nuclear Security. 2021 12th International Conference on Information, Intelligence, Systems Applications (IISA). :1–4.
Advances in artificial intelligence (AI) have provided a variety of solutions in several real-world complex problems. One of the current trends contains the integration of various AI tools to improve the proposed solutions. The question that has to be revisited is how tools may be put together to form efficient systems suitable for the problem at hand. This paper frames itself in the area of nuclear security where an agent uses a radiation sensor to survey an area for radiological threats. The main goal of this application is to identify anomalies in the measured data that designate the presence of nuclear material that may consist of a threat. To that end, we propose the integration of two kernel modeled Gaussian processes (GP) by using a fuzzy inference system. The GP models utilize different types of information to make predictions of the background radiation contribution that will be used to identify an anomaly. The integration of the prediction of the two GP models is performed with means of fuzzy rules that provide the degree of existence of anomalous data. The proposed system is tested on a set of real-world gamma-ray spectra taken with a low-resolution portable radiation spectrometer.
Bui, Dinh-Mao, Huynh-The, Thien, Lee, Sungyoung.  2016.  Fuzzy Fault Detection in IaaS Cloud Computing. Proceedings of the 10th International Conference on Ubiquitous Information Management and Communication. :65:1–65:6.

Availability is one of the most important requirements in the production system. Keeping the level of high availability in Infrastructure-as-a-Service (IaaS) cloud computing is a challenge task because of the complexity of service providing. By definition, the availability can be maintain by using fault tolerance approaches. Recently, many fault tolerance methods have been developed, but few of them focus on the fault detection aspect. In this paper, after a rigorous analysis on the nature of failures, we would like to introduce a technique to identified the failures occurring in IaaS system. By using fuzzy logic algorithm, this proposed technique can provide better performance in terms of accuracy and detection speed, which is critical for the cloud system.

Bentahar, A., Meraoumia, A., Bendjenna, H., Chitroub, S., Zeroual, A..  2020.  Fuzzy Extractor-Based Key Agreement for Internet of Things. 020 1st International Conference on Communications, Control Systems and Signal Processing (CCSSP). :25–29.
The emergence of the Internet of Things with its constraints obliges researchers in this field to find light and accurate solutions to secure the data exchange. This document presents secure authentication using biometrics coupled with an effective key agreement scheme to save time and energy. In our scheme, the agreed key is used to encrypt transmission data between different IoT actors. While the fuzzy extractor based on the fuzzy vault principle, is used as authentication and as key agreement scheme. Besides, our system incorporates the Reed Solomon and Hamming codes to give some tolerance to errors. The experimental results have been discussed according to several recognition rates and computation times. Indeed, the recognition rate results have been compared to other works to validate our system. Also, we clarify how our system resists to specific transmission attacks without affecting lightness and accuracy.
Singh, G., Garg, S..  2020.  Fuzzy Elliptic Curve Cryptography based Cipher Text Policy Attribute based Encryption for Cloud Security. 2020 International Conference on Intelligent Engineering and Management (ICIEM). :327–330.

Cipher Text Policy Attribute Based Encryption which is a form of Public Key Encryption has become a renowned approach as a Data access control scheme for data security and confidentiality. It not only provides the flexibility and scalability in the access control mechanisms but also enhances security by fuzzy fined-grained access control. However, schemes are there which for more security increases the key size which ultimately leads to high encryption and decryption time. Also, there is no provision for handling the middle man attacks during data transfer. In this paper, a light-weight and more scalable encryption mechanism is provided which not only uses fewer resources for encoding and decoding but also improves the security along with faster encryption and decryption time. Moreover, this scheme provides an efficient key sharing mechanism for providing secure transfer to avoid any man-in-the-middle attacks. Also, due to fuzzy policies inclusion, chances are there to get approximation of user attributes available which makes the process fast and reliable and improves the performance of legitimate users.

Xia, D., Zhang, Y..  2017.  The fuzzy control of trust establishment. 2017 4th International Conference on Systems and Informatics (ICSAI). :655–659.

In the open network environment, the strange entities can establish the mutual trust through Automated Trust Negotiation (ATN) that is based on exchanging digital credentials. In traditional ATN, the attribute certificate required to either satisfied or not, and in the strategy, the importance of the certificate is same, it may cause some unnecessary negotiation failure. And in the actual situation, the properties is not just 0 or 1, it is likely to between 0 and 1, so the satisfaction degree is different, and the negotiation strategy need to be quantified. This paper analyzes the fuzzy negotiation process, in order to improve the trust establishment in high efficiency and accuracy further.

Saoud, Z., Faci, N., Maamar, Z., Benslimane, D..  2014.  A Fuzzy Clustering-Based Credibility Model for Trust Assessment in a Service-Oriented Architecture. WETICE Conference (WETICE), 2014 IEEE 23rd International. :56-61.

This paper presents a credibility model to assess trust of Web services. The model relies on consumers' ratings whose accuracy can be questioned due to different biases. A category of consumers known as strict are usually excluded from the process of reaching a majority consensus. We demonstrated that this exclusion should not be. The proposed model reduces the gap between these consumers' ratings and the current majority rating. Fuzzy clustering is used to compute consumers' credibility. To validate this model a set of experiments are carried out.

Zhang, L., Li, B., Zhang, L., Li, D..  2015.  Fuzzy clustering of incomplete data based on missing attribute interval size. 2015 IEEE 9th International Conference on Anti-counterfeiting, Security, and Identification (ASID). :101–104.

Fuzzy c-means algorithm is used to identity clusters of similar objects within a data set, while it is not directly applied to incomplete data. In this paper, we proposed a novel fuzzy c-means algorithm based on missing attribute interval size for the clustering of incomplete data. In the new algorithm, incomplete data set was transformed to interval data set according to the nearest neighbor rule. The missing attribute value was replaced by the corresponding interval median and the interval size was set as the additional property for the incomplete data to control the effect of interval size in clustering. Experiments on standard UCI data set show that our approach outperforms other clustering methods for incomplete data.

Samriya, Jitendra Kumar, Kumar, Narander.  2020.  Fuzzy Ant Bee Colony For Security And Resource Optimization In Cloud Computing. 2020 5th International Conference on Computing, Communication and Security (ICCCS). :1—5.

Cloud computing (CC) systems prevail to be the widespread computational paradigms for offering immense scalable and elastic services. Computing resources in cloud environment should be scheduled to facilitate the providers to utilize the resources moreover the users could get low cost applications. The most prominent need in job scheduling is to ensure Quality of service (QoS) to the user. In the boundary of the third party the scheduling takes place hence it is a significant condition for assuring its security. The main objective of our work is to offer QoS i.e. cost, makespan, minimized migration of task with security enforcement moreover the proposed algorithm guarantees that the admitted requests are executed without violating service level agreement (SLA). These objectives are attained by the proposed Fuzzy Ant Bee Colony algorithm. The experimental outcome confirms that secured job scheduling objective with assured QoS is attained by the proposed algorithm.

Simjanović, Dušan J., Milošević, Dušan M., Milošević, Mimica R..  2021.  Fuzzy AHP based Ranking of Cryptography Indicators. 2021 15th International Conference on Advanced Technologies, Systems and Services in Telecommunications (℡SIKS). :237—240.
The progression of cryptographic attacks in the ICT era doubtless leads to the development of new cryptographic algorithms and assessment, and evaluation of the existing ones. In this paper, the artificial intelligence application, through the fuzzy analytic hierarchy process (FAHP) implementation, is used to rank criteria and sub-criteria on which the algorithms are based to determine the most promising criteria and optimize their use. Out of fifteen criteria, security soundness, robustness and hardware failure distinguished as significant ones.
Verma, Richa, Chandra, Shalini.  2020.  A Fuzzy AHP Approach for Ranking Security Attributes in Fog-IoT Environment. 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT). :1–5.
The advent of Internet and recent technological developments have paved the way for IoT devices in different sectors. The demand for real-time response led to the development of fog computing which is now a popular computing technique. It provides processing, computing and storage at the network edge for latency-sensitive applications such as banking transactions, healthcare etc. This has further led to the pool of user's sensitive data across the web that needs to be secured. In order to find an efficient security solution, it is mandatory to prioritize amongst different fog-level security factors. The authors have therefore, adopted a fuzzy-based Analytical Hierarchy Approach (AHP) for ranking the security attributes in fog-driven IoT environment. The results have also been compared to the ones obtained from classical-AHP and are found to be correlated.
Merrill, Nick, Curran, Max T., Chuang, John.  2017.  Is the Future of Authenticity All In Our Heads?: Moving Passthoughts From the Lab to the World Proceedings of the 2017 New Security Paradigms Workshop. :70–79.

Passthoughts, in which a user thinks a secret thought to log in to services or devices, provides two factors of authentication (knowledge and inherence) in a single step. Since its proposal in 2005, passthoughts enjoyed a number of successful empirical studies. In this paper, we renew the promise of passthoughts authentication, outlining the main challenges that passthoughts must overcome in order to move from the lab to the real world. We propose two studies, which seek different angles at the fundamental questions we pose. Further, we propose it as a fruitful case study for thinking about what authentication can, and should, be expected to do, as it pushes up against questions of what sorts of "selves" authentication systems must be tasked with recognizing. Through this discussion, we raise novel possibilities for authentication broadly, such as "organic passwords" that change naturally over time, or systems that reject users who are not acting quite "like themselves."