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2020-03-23
Unnikrishnan, Grieshma, Mathew, Deepa, Jose, Bijoy A., Arvind, Raju.  2019.  Hybrid Route Recommender System for Smarter Logistics. 2019 IEEE 5th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS). :239–244.
The condition of road surface has a significant role in land transportation. Due to poor road conditions, the logistics and supply chain industry face a drastic loss in their business. Unmaintained roads can cause damage to goods and accidents. The existing routing techniques do not consider factors like shock, temperature and tilt of goods etc. but these factors have to be considered for the logistics and supply chain industry. This paper proposes a recommender system which target management of goods in logistics. A 3 axis accelerometer is used to measure the road surface conditions. The pothole location is obtained using Global Positioning System (GPS). Using these details a hybrid recommender system is built. Hybrid recommender system combines multiple recommendation techniques to develop an effective recommender system. Here content-based and collaborative-based techniques is combined to build a hybrid recommender system. One of the popular Multiple Criteria Decision Making (MCDM) method, The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is used for content based filtering and normalised Euclidean distance and KNN algorithm is used for collaborative filtering. The best route recommended by the system will be displayed to the user using a map application.
Naik, Nitin, Jenkins, Paul, Savage, Nick.  2019.  A Ransomware Detection Method Using Fuzzy Hashing for Mitigating the Risk of Occlusion of Information Systems. 2019 International Symposium on Systems Engineering (ISSE). :1–6.
Today, a significant threat to organisational information systems is ransomware that can completely occlude the information system by denying access to its data. To reduce this exposure and damage from ransomware attacks, organisations are obliged to concentrate explicitly on the threat of ransomware, alongside their malware prevention strategy. In attempting to prevent the escalation of ransomware attacks, it is important to account for their polymorphic behaviour and dispersion of inexhaustible versions. However, a number of ransomware samples possess similarity as they are created by similar groups of threat actors. A particular threat actor or group often adopts similar practices or codebase to create unlimited versions of their ransomware. As a result of these common traits and codebase, it is probable that new or unknown ransomware variants can be detected based on a comparison with their originating or existing samples. Therefore, this paper presents a detection method for ransomware by employing a similarity preserving hashing method called fuzzy hashing. This detection method is applied on the collected WannaCry or WannaCryptor ransomware corpus utilising three fuzzy hashing methods SSDEEP, SDHASH and mvHASH-B to evaluate the similarity detection success rate by each method. Moreover, their fuzzy similarity scores are utilised to cluster the collected ransomware corpus and its results are compared to determine the relative accuracy of the selected fuzzy hashing methods.
2020-03-16
Ullah, Faheem, Ali Babar, M..  2019.  QuickAdapt: Scalable Adaptation for Big Data Cyber Security Analytics. 2019 24th International Conference on Engineering of Complex Computer Systems (ICECCS). :81–86.
Big Data Cyber Security Analytics (BDCA) leverages big data technologies for collecting, storing, and analyzing a large volume of security events data to detect cyber-attacks. Accuracy and response time, being the most important quality concerns for BDCA, are impacted by changes in security events data. Whilst it is promising to adapt a BDCA system's architecture to the changes in security events data for optimizing accuracy and response time, it is important to consider large search space of architectural configurations. Searching a large space of configurations for potential adaptation incurs an overwhelming adaptation time, which may cancel the benefits of adaptation. We present an adaptation approach, QuickAdapt, to enable quick adaptation of a BDCA system. QuickAdapt uses descriptive statistics (e.g., mean and variance) of security events data and fuzzy rules to (re) compose a system with a set of components to ensure optimal accuracy and response time. We have evaluated QuickAdapt for a distributed BDCA system using four datasets. Our evaluation shows that on average QuickAdapt reduces adaptation time by 105× with a competitive adaptation accuracy of 70% as compared to an existing solution.
2020-03-09
Hettiarachchi, Charitha, Do, Hyunsook.  2019.  A Systematic Requirements and Risks-Based Test Case Prioritization Using a Fuzzy Expert System. 2019 IEEE 19th International Conference on Software Quality, Reliability and Security (QRS). :374–385.
The use of risk information can help software engineers identify software components that are likely vulnerable or require extra attention when testing. Some studies have shown that the requirements risk-based approaches can be effective in improving the effectiveness of regression testing techniques. However, the risk estimation processes used in such approaches can be subjective, time-consuming, and costly. In this research, we introduce a fuzzy expert system that emulates human thinking to address the subjectivity related issues in the risk estimation process in a systematic and an efficient way and thus further improve the effectiveness of test case prioritization. Further, the required data for our approach was gathered by employing a semi-automated process that made the risk estimation process less subjective. The empirical results indicate that the new prioritization approach can improve the rate of fault detection over several existing test case prioritization techniques, while reducing threats to subjective risk estimation.
2020-02-26
Naik, Nitin, Jenkins, Paul, Savage, Nick, Yang, Longzhi.  2019.  Cyberthreat Hunting - Part 2: Tracking Ransomware Threat Actors Using Fuzzy Hashing and Fuzzy C-Means Clustering. 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). :1–6.

Threat actors are constantly seeking new attack surfaces, with ransomeware being one the most successful attack vectors that have been used for financial gain. This has been achieved through the dispersion of unlimited polymorphic samples of ransomware whilst those responsible evade detection and hide their identity. Nonetheless, every ransomware threat actor adopts some similar style or uses some common patterns in their malicious code writing, which can be significant evidence contributing to their identification. he first step in attempting to identify the source of the attack is to cluster a large number of ransomware samples based on very little or no information about the samples, accordingly, their traits and signatures can be analysed and identified. T herefore, this paper proposes an efficient fuzzy analysis approach to cluster ransomware samples based on the combination of two fuzzy techniques fuzzy hashing and fuzzy c-means (FCM) clustering. Unlike other clustering techniques, FCM can directly utilise similarity scores generated by a fuzzy hashing method and cluster them into similar groups without requiring additional transformational steps to obtain distance among objects for clustering. Thus, it reduces the computational overheads by utilising fuzzy similarity scores obtained at the time of initial triaging of whether the sample is known or unknown ransomware. The performance of the proposed fuzzy method is compared against k-means clustering and the two fuzzy hashing methods SSDEEP and SDHASH which are evaluated based on their FCM clustering results to understand how the similarity score affects the clustering results.

2020-02-17
Zheng-gang, He, Jing-ni, Guo.  2019.  Security Risk Assessment of Multimodal Transport Network Based on WBS-RBS and PFWA Operator. 2019 4th International Conference on Intelligent Transportation Engineering (ICITE). :203–206.
In order to effectively assess the security risks in multimodal transport networks, a security risk assessment method based on WBS-RBS and Pythagorean Fuzzy Weighted Average (PFWA) operator is proposed. The risk matrix 0-1 assignment of WBS-RBS is replaced by the Pythagorean Fuzzy Number (PFLN) scored by experts. The security risk ranking values of multimodal transport network are calculated from two processes of whole-stage and phased, respectively, and the security risk assessment results are obtained. Finally, an example of railway-highway-waterway intermodal transportation process of automobile parts is given to verify the validity of the method, the results show that the railway transportation is more stable than the waterway transportation, and the highway transportation has the greatest security risk, and for different security risk factors, personnel risk has the greatest impact. The risk of goods will change with the change of the attributes of goods, and the security risk of storage facilities is the smallest.
2020-01-27
Xue, Hong, Wang, Jingxuan, Zhang, Miao, Wu, Yue.  2019.  Emergency Severity Assessment Method for Cluster Supply Chain Based on Cloud Fuzzy Clustering Algorithm. 2019 Chinese Control Conference (CCC). :7108–7114.

Aiming at the composite uncertainty characteristics and high-dimensional data stream characteristics of the evaluation index with both ambiguity and randomness, this paper proposes a emergency severity assessment method for cluster supply chain based on cloud fuzzy clustering algorithm. The summary cloud model generation algorithm is created. And the multi-data fusion method is applied to the cloud model processing of the evaluation indexes for high-dimensional data stream with ambiguity and randomness. The synopsis data of the emergency severity assessment indexes are extracted. Based on time attenuation model and sliding window model, the data stream fuzzy clustering algorithm for emergency severity assessment is established. The evaluation results are rationally optimized according to the generalized Euclidean distances of the cluster centers and cluster microcluster weights, and the severity grade of cluster supply chain emergency is dynamically evaluated. The experimental results show that the proposed algorithm improves the clustering accuracy and reduces the operation time, as well as can provide more accurate theoretical support for the early warning decision of cluster supply chain emergency.

Salamai, Abdullah, Hussain, Omar, Saberi, Morteza.  2019.  Decision Support System for Risk Assessment Using Fuzzy Inference in Supply Chain Big Data. 2019 International Conference on High Performance Big Data and Intelligent Systems (HPBD IS). :248–253.

Currently, organisations find it difficult to design a Decision Support System (DSS) that can predict various operational risks, such as financial and quality issues, with operational risks responsible for significant economic losses and damage to an organisation's reputation in the market. This paper proposes a new DSS for risk assessment, called the Fuzzy Inference DSS (FIDSS) mechanism, which uses fuzzy inference methods based on an organisation's big data collection. It includes the Emerging Association Patterns (EAP) technique that identifies the important features of each risk event. Then, the Mamdani fuzzy inference technique and several membership functions are evaluated using the firm's data sources. The FIDSS mechanism can enhance an organisation's decision-making processes by quantifying the severity of a risk as low, medium or high. When it automatically predicts a medium or high level, it assists organisations in taking further actions that reduce this severity level.

Fuchs, Caro, Spolaor, Simone, Nobile, Marco S., Kaymak, Uzay.  2019.  A Swarm Intelligence Approach to Avoid Local Optima in Fuzzy C-Means Clustering. 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). :1–6.
Clustering analysis is an important computational task that has applications in many domains. One of the most popular algorithms to solve the clustering problem is fuzzy c-means, which exploits notions from fuzzy logic to provide a smooth partitioning of the data into classes, allowing the possibility of multiple membership for each data sample. The fuzzy c-means algorithm is based on the optimization of a partitioning function, which minimizes inter-cluster similarity. This optimization problem is known to be NP-hard and it is generally tackled using a hill climbing method, a local optimizer that provides acceptable but sub-optimal solutions, since it is sensitive to initialization and tends to get stuck in local optima. In this work we propose an alternative approach based on the swarm intelligence global optimization method Fuzzy Self-Tuning Particle Swarm Optimization (FST-PSO). We solve the fuzzy clustering task by optimizing fuzzy c-means' partitioning function using FST-PSO. We show that this population-based metaheuristics is more effective than hill climbing, providing high quality solutions with the cost of an additional computational complexity. It is noteworthy that, since this particle swarm optimization algorithm is self-tuning, the user does not have to specify additional hyperparameters for the optimization process.
2020-01-02
\v Siser, Anton, Mari\v s, Ladislav, \v Rehák, David, Pellowski, Witalis.  2018.  The Use of Expert Judgement as the Method to Obtain Delay Time Values of Passive Barriers in the Context of the Physical Protection System. 2018 International Carnahan Conference on Security Technology (ICCST). :1–5.
Due to its costly and time-consuming nature and a wide range of passive barrier elements and tools for their breaching, testing the delay time of passive barriers is only possible as an experimental tool to verify expert judgements of said delay times. The article focuses on the possibility of creating and utilizing a new method of acquiring values of delay time for various passive barrier elements using expert judgements which could add to the creation of charts where interactions between the used elements of mechanical barriers and the potential tools for their bypassing would be assigned a temporal value. The article consists of basic description of methods of expert judgements previously applied for making prognoses of socio-economic development and in other societal areas, which are called soft system. In terms of the problem of delay time, this method needed to be modified in such a way that the prospective output would be expressible by a specific quantitative value. To achieve this goal, each stage of the expert judgements was adjusted to the use of suitable scientific methods to select appropriate experts and then to achieve and process the expert data. High emphasis was placed on evaluation of quality and reliability of the expert judgements, which takes into account the specifics of expert selection such as their low numbers, specialization and practical experience.
2019-12-09
Li, Wenjuan, Cao, Jian, Hu, Keyong, Xu, Jie, Buyya, Rajkumar.  2019.  A Trust-Based Agent Learning Model for Service Composition in Mobile Cloud Computing Environments. IEEE Access. 7:34207–34226.
Mobile cloud computing has the features of resource constraints, openness, and uncertainty which leads to the high uncertainty on its quality of service (QoS) provision and serious security risks. Therefore, when faced with complex service requirements, an efficient and reliable service composition approach is extremely important. In addition, preference learning is also a key factor to improve user experiences. In order to address them, this paper introduces a three-layered trust-enabled service composition model for the mobile cloud computing systems. Based on the fuzzy comprehensive evaluation method, we design a novel and integrated trust management model. Service brokers are equipped with a learning module enabling them to better analyze customers' service preferences, especially in cases when the details of a service request are not totally disclosed. Because traditional methods cannot totally reflect the autonomous collaboration between the mobile cloud entities, a prototype system based on the multi-agent platform JADE is implemented to evaluate the efficiency of the proposed strategies. The experimental results show that our approach improves the transaction success rate and user satisfaction.
2019-11-26
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.

Shukla, Anjali, Rakshit, Arnab, Konar, Amit, Ghosh, Lidia, Nagar, Atulya K..  2018.  Decoding of Mind-Generated Pattern Locks for Security Checking Using Type-2 Fuzzy Classifier. 2018 IEEE Symposium Series on Computational Intelligence (SSCI). :1976-1981.
Brain Computer Interface (BCI) aims at providing a better quality of life to people suffering from neuromuscular disability. This paper establishes a BCI paradigm to provide a biometric security option, used for locking and unlocking personal computers or mobile phones. Although it is primarily meant for the people with neurological disorder, its application can safely be extended for the use of normal people. The proposed scheme decodes the electroencephalogram signals liberated by the brain of the subjects, when they are engaged in selecting a sequence of dots in(6×6)2-dimensional array, representing a pattern lock. The subject, while selecting the right dot in a row, would yield a P300 signal, which is decoded later by the brain-computer interface system to understand the subject's intention. In case the right dots in all the 6 rows are correctly selected, the subject would yield P300 signals six times, which on being decoded by a BCI system would allow the subject to access the system. Because of intra-subjective variation in the amplitude and wave-shape of the P300 signal, a type 2 fuzzy classifier has been employed to classify the presence/absence of the P300 signal in the desired window. A comparison of performances of the proposed classifier with others is also included. The functionality of the proposed system has been validated using the training instances generated for 30 subjects. Experimental results confirm that the classification accuracy for the present scheme is above 90% irrespective of subjects.
2019-05-01
Pratama, R. F., Suwastika, N. A., Nugroho, M. A..  2018.  Design and Implementation Adaptive Intrusion Prevention System (IPS) for Attack Prevention in Software-Defined Network (SDN) Architecture. 2018 6th International Conference on Information and Communication Technology (ICoICT). :299-304.

Intrusion Prevention System (IPS) is a tool for securing networks from any malicious packet that could be sent from specific host. IPS can be installed on SDN network that has centralized logic architecture, so that IPS doesnt need to be installed on lots of nodes instead it has to be installed alongside the controller as center of logic network. IPS still has a flaw and that is the block duration would remain the same no matter how often a specific host attacks. For this reason, writer would like to make a system that not only integrates IPS on the SDN, but also designs an adaptive IPS by utilizing a fuzzy logic that can decide how long blocks are based on the frequency variable and type of attacks. From the results of tests that have been done, SDN network that has been equipped with adaptive IPS has the ability to detect attacks and can block the attacker host with the duration based on the frequency and type of attacks. The final result obtained is to make the SDN network safer by adding 0.228 milliseconds as the execute time required for the fuzzy algorithm in one process.

Douzi, S., Benchaji, I., ElOuahidi, B..  2018.  Hybrid Approach for Intrusion Detection Using Fuzzy Association Rules. 2018 2nd Cyber Security in Networking Conference (CSNet). :1-3.

Rapid development of internet and network technologies has led to considerable increase in number of attacks. Intrusion detection system is one of the important ways to achieve high security in computer networks. However, it have curse of dimensionality which tends to increase time complexity and decrease resource utilization. To improve the ability of detecting anomaly intrusions, a combined algorithm is proposed based on Weighted Fuzzy C-Mean Clustering Algorithm (WFCM) and Fuzzy logic. Decision making is performed in two stages. In the first stage, WFCM algorithm is applied to reduce the input data space. The reduced dataset is then fed to Fuzzy Logic scheme to build the fuzzy sets, membership function and the rules that decide whether an instance represents an anomaly or not.

2019-04-01
Alibadi, S. H., Sadkhan, S. B..  2018.  A Proposed Security Evaluation Method for Bluetooth E0Based on Fuzzy Logic. 2018 International Conference on Advanced Science and Engineering (ICOASE). :324–329.

The security level is very important in Bluetooth, because the network or devices using secure communication, are susceptible to many attacks against the transmitted data received through eavesdropping. The cryptosystem designers needs to know the complexity of the designed Bluetooth E0. And what the advantages given by any development performed on any known Bluetooth E0Encryption method. The most important criteria can be used in evaluation method is considered as an important aspect. This paper introduce a proposed fuzzy logic technique to evaluate the complexity of Bluetooth E0Encryption system by choosing two parameters, which are entropy and correlation rate, as inputs to proposed fuzzy logic based Evaluator, which can be applied with MATLAB system.

2019-03-22
Bentahar, A., Meraoumia, A., Bendjenna, H., Zeroual, A..  2018.  IoT Securing System Using Fuzzy Commitment for DCT-Based Fingerprint Recognition. 2018 3rd International Conference on Pattern Analysis and Intelligent Systems (PAIS). :1-5.

Internet of Things refers to a paradigm consisting of a variety of uniquely identifiable day to day things communicating with one another to form a large scale dynamic network. Securing access to this network is a current challenging issue. This paper proposes an encryption system suitable to IoT features. In this system we integrated the fuzzy commitment scheme in DCT-based recognition method for fingerprint. To demonstrate the efficiency of our scheme, the obtained results are analyzed and compared with direct matching (without encryption) according to the most used criteria; FAR and FRR.

2019-03-11
Xie, X. L., Xue, W. X..  2018.  An Empirical Study of Web Software Trustworthiness Measurement. 2018 2nd IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC). :1474–1481.

The aim of this paper is to present a fresh methodology of improved evidence synthesis for assessing software trustworthiness, which can unwind collisions stemming from proofs and these proofs' own uncertainties. To achieve this end, the paper, on the ground of ISO/IEC 9126 and web software attributes, models the indicator framework by factor analysis. Then, the paper conducts an calculation of the weight for each indicator via the technique of structural entropy and makes a fuzzy judgment matrix concerning specialists' comments. This study performs a computation of scoring and grade regarding software trustworthiness by using of the criterion concerning confidence degree discernment and comes up with countermeasures to promote trustworthiness. Relying on online accounting software, this study makes an empirical analysis to further confirm validity and robustness. This paper concludes with pointing out limitations.

2019-02-08
Naik, N., Jenkins, P., Cooke, R., Yang, L..  2018.  Honeypots That Bite Back: A Fuzzy Technique for Identifying and Inhibiting Fingerprinting Attacks on Low Interaction Honeypots. 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). :1-8.
The development of a robust strategy for network security is reliant upon a combination of in-house expertise and for completeness attack vectors used by attackers. A honeypot is one of the most popular mechanisms used to gather information about attacks and attackers. However, low-interaction honeypots only emulate an operating system and services, and are more prone to a fingerprinting attack, resulting in severe consequences such as revealing the identity of the honeypot and thus ending the usefulness of the honeypot forever, or worse, enabling it to be converted into a bot used to attack others. A number of tools and techniques are available both to fingerprint low-interaction honeypots and to defend against such fingerprinting; however, there is an absence of fingerprinting techniques to identify the characteristics and behaviours that indicate fingerprinting is occurring. Therefore, this paper proposes a fuzzy technique to correlate the attack actions and predict the probability that an attack is a fingerprinting attack on the honeypot. Initially, an experimental assessment of the fingerprinting attack on the low- interaction honeypot is performed, and a fingerprinting detection mechanism is proposed that includes the underlying principles of popular fingerprinting attack tools. This implementation is based on a popular and commercially available low-interaction honeypot for Windows - KFSensor. However, the proposed fuzzy technique is a general technique and can be used with any low-interaction honeypot to aid in the identification of the fingerprinting attack whilst it is occurring; thus protecting the honeypot from the fingerprinting attack and extending its life.
2018-11-14
Teoh, T. T., Zhang, Y., Nguwi, Y. Y., Elovici, Y., Ng, W. L..  2017.  Analyst Intuition Inspired High Velocity Big Data Analysis Using PCA Ranked Fuzzy K-Means Clustering with Multi-Layer Perceptron (MLP) to Obviate Cyber Security Risk. 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). :1790–1793.
The growing prevalence of cyber threats in the world are affecting every network user. Numerous security monitoring systems are being employed to protect computer networks and resources from falling victim to cyber-attacks. There is a pressing need to have an efficient security monitoring system to monitor the large network datasets generated in this process. A large network datasets representing Malware attacks have been used in this work to establish an expert system. The characteristics of attacker's IP addresses can be extracted from our integrated datasets to generate statistical data. The cyber security expert provides to the weight of each attribute and forms a scoring system by annotating the log history. We adopted a special semi supervise method to classify cyber security log into attack, unsure and no attack by first breaking the data into 3 cluster using Fuzzy K mean (FKM), then manually label a small data (Analyst Intuition) and finally train the neural network classifier multilayer perceptron (MLP) base on the manually labelled data. By doing so, our results is very encouraging as compare to finding anomaly in a cyber security log, which generally results in creating huge amount of false detection. The method of including Artificial Intelligence (AI) and Analyst Intuition (AI) is also known as AI2. The classification results are encouraging in segregating the types of attacks.
Teoh, T. T., Nguwi, Y. Y., Elovici, Y., Cheung, N. M., Ng, W. L..  2017.  Analyst Intuition Based Hidden Markov Model on High Speed, Temporal Cyber Security Big Data. 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). :2080–2083.
Hidden Markov Models (HMM) are probabilistic models that can be used for forecasting time series data. It has seen success in various domains like finance [1-5], bioinformatics [6-8], healthcare [9-11], agriculture [12-14], artificial intelligence[15-17]. However, the use of HMM in cyber security found to date is numbered. We believe the properties of HMM being predictive, probabilistic, and its ability to model different naturally occurring states form a good basis to model cyber security data. It is hence the motivation of this work to provide the initial results of our attempts to predict security attacks using HMM. A large network datasets representing cyber security attacks have been used in this work to establish an expert system. The characteristics of attacker's IP addresses can be extracted from our integrated datasets to generate statistical data. The cyber security expert provides the weight of each attribute and forms a scoring system by annotating the log history. We applied HMM to distinguish between a cyber security attack, unsure and no attack by first breaking the data into 3 cluster using Fuzzy K mean (FKM), then manually label a small data (Analyst Intuition) and finally use HMM state-based approach. By doing so, our results are very encouraging as compare to finding anomaly in a cyber security log, which generally results in creating huge amount of false detection.
2018-09-12
Rahayuda, I. G. S., Santiari, N. P. L..  2017.  Crawling and cluster hidden web using crawler framework and fuzzy-KNN. 2017 5th International Conference on Cyber and IT Service Management (CITSM). :1–7.
Today almost everyone is using internet for daily activities. Whether it's for social, academic, work or business. But only a few of us are aware that internet generally we access only a small part of the overall of internet access. The Internet or the world wide web is divided into several levels, such as web surfaces, deep web or dark web. Accessing internet into deep or dark web is a dangerous thing. This research will be conducted with research on web content and deep content. For a faster and safer search, in this research will be use crawler framework. From the search process will be obtained various kinds of data to be stored into the database. The database classification process will be implemented to know the level of the website. The classification process is done by using the fuzzy-KNN method. The fuzzy-KNN method classifies the results of the crawling framework that contained in the database. Crawling framework will generate data in the form of url address, page info and other. Crawling data will be compared with predefined sample data. The classification result of fuzzy-KNN will result in the data of the web level based on the value of the word specified in the sample data. From the research conducted on several data tests that found there are as much as 20% of the web surface, 7.5% web bergie, 20% deep web, 22.5% charter and 30% dark web. Research is only done on some test data, it is necessary to add some data in order to get better result. Better crawler frameworks can speed up crawling results, especially at certain web levels because not all crawler frameworks can work at a particular web level, the tor browser's can be used but the crawler framework sometimes can not work.
2018-08-23
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.

2018-05-30
Li, F., Chen, J., Shu, F., Zhang, J., Qing, S., Guo, W..  2017.  Research of Security Risk in Electric Power Information Network. 2017 6th International Conference on Computer Science and Network Technology (ICCSNT). :361–365.

The factors that threaten electric power information network are analyzed. Aiming at the weakness of being unable to provide numerical value of risk, this paper presents the evaluation index system, the evaluation model and method of network security based on multilevel fuzzy comprehensive judgment. The steps and method of security evaluation by the synthesis evaluation model are provided. The results show that this method is effective to evaluate the risk of electric power information network.

2018-04-04
Ran, L., Lu, L., Lin, H., Han, M., Zhao, D., Xiang, J., Yu, H., Ma, X..  2017.  An Experimental Study of Four Methods for Homology Analysis of Firmware Vulnerability. 2017 International Conference on Dependable Systems and Their Applications (DSA). :42–50.

In the production process of embedded device, due to the frequent reuse of third-party libraries or development kits, there are large number of same vulnerabilities that appear in more than one firmware. Homology analysis is often used in detecting this kind of vulnerabilities caused by code reuse or third-party reuse and in the homology analysis, the widely used methods are mainly Binary difference analysis, Normalized compression distance, String feature matching and Fuzz hash. But when we use these methods for homology analysis, we found that the detection result is not ideal and there is a high false positive rate. Focusing on this problem, we analyzed the application scenarios of these four methods and their limitations by combining different methods and different types of files and the experiments show that the combination of methods and files have a better performance in homology analysis.