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Boato, G., Dang-Nguyen, D., Natale, F. G. B. De.  2020.  Morphological Filter Detector for Image Forensics Applications. IEEE Access. 8:13549—13560.
Mathematical morphology provides a large set of powerful non-linear image operators, widely used for feature extraction, noise removal or image enhancement. Although morphological filters might be used to remove artifacts produced by image manipulations, both on binary and gray level documents, little effort has been spent towards their forensic identification. In this paper we propose a non-trivial extension of a deterministic approach originally detecting erosion and dilation of binary images. The proposed approach operates on grayscale images and is robust to image compression and other typical attacks. When the image is attacked the method looses its deterministic nature and uses a properly trained SVM classifier, using the original detector as a feature extractor. Extensive tests demonstrate that the proposed method guarantees very high accuracy in filtering detection, providing 100% accuracy in discriminating the presence and the type of morphological filter in raw images of three different datasets. The achieved accuracy is also good after JPEG compression, equal or above 76.8% on all datasets for quality factors above 80. The proposed approach is also able to determine the adopted structuring element for moderate compression factors. Finally, it is robust against noise addition and it can distinguish morphological filter from other filters.
Yu Li, Rui Dai, Junjie Zhang.  2014.  Morphing communications of Cyber-Physical Systems towards moving-target defense. Communications (ICC), 2014 IEEE International Conference on. :592-598.

Since the massive deployment of Cyber-Physical Systems (CPSs) calls for long-range and reliable communication services with manageable cost, it has been believed to be an inevitable trend to relay a significant portion of CPS traffic through existing networking infrastructures such as the Internet. Adversaries who have access to networking infrastructures can therefore eavesdrop network traffic and then perform traffic analysis attacks in order to identify CPS sessions and subsequently launch various attacks. As we can hardly prevent all adversaries from accessing network infrastructures, thwarting traffic analysis attacks becomes indispensable. Traffic morphing serves as an effective means towards this direction. In this paper, a novel traffic morphing algorithm, CPSMorph, is proposed to protect CPS sessions. CPSMorph maintains a number of network sessions whose distributions of inter-packet delays are statistically indistinguishable from those of typical network sessions. A CPS message will be sent through one of these sessions with assured satisfaction of its time constraint. CPSMorph strives to minimize the overhead by dynamically adjusting the morphing process. It is characterized by low complexity as well as high adaptivity to changing dynamics of CPS sessions. Experimental results have shown that CPSMorph can effectively performing traffic morphing for real-time CPS messages with moderate overhead.

Althamary, I. A., El-Alfy, E. S. M..  2017.  A more secure scheme for CAPTCHA-based authentication in cloud environment. 2017 8th International Conference on Information Technology (ICIT). :405–411.

Cloud computing is a remarkable model for permitting on-demand network access to an elastic collection of configurable adaptive resources and features including storage, software, infrastructure, and platform. However, there are major concerns about security-related issues. A very critical security function is user authentication using passwords. Although many flaws have been discovered in password-based authentication, it remains the most convenient approach that people continue to utilize. Several schemes have been proposed to strengthen its effectiveness such as salted hashes, one-time password (OTP), single-sign-on (SSO) and multi-factor authentication (MFA). This study proposes a new authentication mechanism by combining user's password and modified characters of CAPTCHA to generate a passkey. The modification of the CAPTCHA depends on a secret agreed upon between the cloud provider and the user to employ different characters for some characters in the CAPTCHA. This scheme prevents various attacks including short-password attack, dictionary attack, keylogger, phishing, and social engineering. Moreover, it can resolve the issue of password guessing and the use of a single password for different cloud providers.

Ge, Jingquan, Gao, Neng, Tu, Chenyang, Xiang, Ji, Liu, Zeyi.  2019.  More Secure Collaborative APIs Resistant to Flush+Reload and Flush+Flush Attacks on ARMv8-A. 2019 26th Asia-Pacific Software Engineering Conference (APSEC). :410—417.
With the popularity of smart devices such as mobile phones and tablets, the security problem of the widely used ARMv8-A processor has received more and more attention. Flush+Reload and Flush+Flush cache attacks have become two of the most important security threats due to their low noise and high resolution. In order to resist Flush+Reload and Flush+Flush attacks, researchers proposed many defense methods. However, these existing methods have various shortcomings. The runtime defense methods using hardware performance counters cannot detect attacks fast enough, effectively detect Flush+Flush or avoid a high false positive rate. Static code analysis schemes are powerless for obfuscation techniques. The approaches of permanently reducing the resolution can only be utilized on browser products and cannot be applied in the system. In this paper, we design two more secure collaborative APIs-flush operation API and high resolution time API-which can resist Flush+Reload and Flush+Flush attacks. When the flush operation API is called, the high resolution time API temporarily reduces its resolution and automatically restores. Moreover, the flush operation API also has the ability to detect and handle suspected Flush+Reload and Flush+Flush attacks. The attack and performance comparison experiments prove that the two APIs we designed are safer and the performance losses are acceptable.
Lu, Wen-jie, Sakuma, Jun.  2018.  More Practical Privacy-Preserving Machine Learning As A Service via Efficient Secure Matrix Multiplication. Proceedings of the 6th Workshop on Encrypted Computing & Applied Homomorphic Cryptography. :25-36.

An efficient secure two-party computation protocol of matrix multiplication allows privacy-preserving cloud-aid machine learning services such as face recognition and traffic-aware navigation. We use homomorphic encryption to construct a secure matrix multiplication protocol with a small communication overhead and computation overhead on the client's side, which works particularly well when a large number of clients access to the server simultaneously. The fastest secure matrix multiplication protocols have been constructed using tools such as oblivious transfer, but a potential limitation of these methods is the needs of using a wide network bandwidth between the client and the server, e.g., 10\textasciitildeGbps. This is of particular concern when thousands of clients interact with the server concurrently. Under this setting, the performance oblivious transfer-based methods will decrease significantly, since the server can only allocate a small ratio of its outgoing bandwidth for each client. With three proposed optimizations, our matrix multiplication protocol can run very fast even under the high concurrent setting. Our benchmarks show that it takes an Amazon instance (i.e., 72 CPUs and 25 Gbps outgoing bandwidth) less than 50 seconds to complete 1000 concurrent secure matrix multiplications with \$128\textbackslashtimes 128\$ entries. In addition, our method reduces more than \$74% - 97%\$ of the precomputation time of two privacy-preserving machine learning frameworks, SecureML (S&P'17) and MiniONN (CCS'17).

Dotzler, Georg, Kamp, Marius, Kreutzer, Patrick, Philippsen, Michael.  2017.  More Accurate Recommendations for Method-Level Changes. Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering. :798–808.

During the life span of large software projects, developers often apply the same code changes to different code locations in slight variations. Since the application of these changes to all locations is time-consuming and error-prone, tools exist that learn change patterns from input examples, search for possible pattern applications, and generate corresponding recommendations. In many cases, the generated recommendations are syntactically or semantically wrong due to code movements in the input examples. Thus, they are of low accuracy and developers cannot directly copy them into their projects without adjustments. We present the Accurate REcommendation System (ARES) that achieves a higher accuracy than other tools because its algorithms take care of code movements when creating patterns and recommendations. On average, the recommendations by ARES have an accuracy of 96% with respect to code changes that developers have manually performed in commits of source code archives. At the same time ARES achieves precision and recall values that are on par with other tools.

Lan Zhang, Kebin Liu, Yonghang Jiang, Xiang-Yang Li, Yunhao Liu, Panlong Yang.  2014.  Montage: Combine frames with movement continuity for realtime multi-user tracking. INFOCOM, 2014 Proceedings IEEE. :799-807.

In this work we design and develop Montage for real-time multi-user formation tracking and localization by off-the-shelf smartphones. Montage achieves submeter-level tracking accuracy by integrating temporal and spatial constraints from user movement vector estimation and distance measuring. In Montage we designed a suite of novel techniques to surmount a variety of challenges in real-time tracking, without infrastructure and fingerprints, and without any a priori user-specific (e.g., stride-length and phone-placement) or site-specific (e.g., digitalized map) knowledge. We implemented, deployed and evaluated Montage in both outdoor and indoor environment. Our experimental results (847 traces from 15 users) show that the stride-length estimated by Montage over all users has error within 9cm, and the moving-direction estimated by Montage is within 20°. For realtime tracking, Montage provides meter-second-level formation tracking accuracy with off-the-shelf mobile phones.

Youngjung Ahn, Yongsuk Lee, Jin-Young Choi, Gyungho Lee, Dongkyun Ahn.  2014.  Monitoring Translation Lookahead Buffers to Detect Code Injection Attacks. Computer. 47:66-72.

By identifying memory pages that external I/O operations have modified, a proposed scheme blocks malicious injected code activation, accurately distinguishing an attack from legitimate code injection with negligible performance impact and no changes to the user application.

Nika Haghtalab, Aron Laszka, Ariel D. Procaccia, Yevgeniy Vorobeychik, Xenofon D. Koutsoukos.  2015.  Monitoring Stealthy Diffusion. SIAM International Conference on Data Mining.

Starting with the seminal work by Kempe et al., a broad variety of problems, such as targeted marketing and the spread of viruses and malware, have been modeled as selecting
a subset of nodes to maximize diffusion through a network. In
cyber-security applications, however, a key consideration largely ignored in this literature is stealth. In particular, an attacker often has a specific target in mind, but succeeds only if the target is reached (e.g., by malware) before the malicious payload is detected and corresponding countermeasures deployed. The dual side of this problem is deployment of a limited number of monitoring units, such as cyber-forensics specialists, so as to limit the likelihood of such targeted and stealthy diffusion processes reaching their intended targets. We investigate the problem of optimal monitoring of targeted stealthy diffusion processes, and show that a number of natural variants of this problem are NP-hard to approximate. On the positive side, we show that if stealthy diffusion starts from randomly selected nodes, the defender’s objective is submodular, and a fast greedy algorithm has provable approximation guarantees. In addition, we present approximation algorithms for the setting in which an attacker optimally responds to the placement of monitoring nodes by adaptively selecting the starting nodes for the diffusion process. Our experimental results show that the proposed algorithms are highly effective and scalable.

Rieke, R., Repp, J., Zhdanova, M., Eichler, J..  2014.  Monitoring Security Compliance of Critical Processes. Parallel, Distributed and Network-Based Processing (PDP), 2014 22nd Euromicro International Conference on. :552-560.

Enforcing security in process-aware information systems at runtime requires the monitoring of systems' operation using process information. Analysis of this information with respect to security and compliance aspects is growing in complexity with the increase in functionality, connectivity, and dynamics of process evolution. To tackle this complexity, the application of models is becoming standard practice. Considering today's frequent changes to processes, model-based support for security and compliance analysis is not only needed in pre-operational phases but also at runtime. This paper presents an approach to support evaluation of the security status of processes at runtime. The approach is based on operational formal models derived from process specifications and security policies comprising technical, organizational, regulatory and cross-layer aspects. A process behavior model is synchronized by events from the running process and utilizes prediction of expected close-future states to find possible security violations and allow early decisions on countermeasures. The applicability of the approach is exemplified by a misuse case scenario from a hydroelectric power plant.

Lescisin, M., Mahmoud, Q. H..  2020.  A Monitoring Framework for Side-Channel Information Leaks. 2020 IEEE International Conference on Consumer Electronics (ICCE). :1—6.

Security and privacy in computer systems has always been an important aspect of computer engineering and will continue to grow in importance as computer systems become entrusted to handle an ever increasing amount of sensitive information. Classical exploitation techniques such as memory corruption or shell command injection have been well researched and thus there exists known design patterns to avoid and penetration testing tools for testing the robustness of programs against these types of attacks. When it comes to the notion of program security requirements being violated through indirect means referred to as side-channels, testing frameworks of quality comparable to popular memory safety or command injection tools are not available. Recent computer security research has shown that private information may be indirectly leaked through side-channels such as patterns of encrypted network traffic, CPU and motherboard noise, and monitor ambient light. This paper presents the design and evaluation of a side-channel detection and exploitation framework that follows a machine learning based plugin oriented architecture thus allowing side-channel research to be conducted on a wide-variety of side-channel sources.

Fernando, R., Ranchal, R., Bhargava, B., Angin, P..  2017.  A Monitoring Approach for Policy Enforcement in Cloud Services. 2017 IEEE 10th International Conference on Cloud Computing (CLOUD). :600–607.

When clients interact with a cloud-based service, they expect certain levels of quality of service guarantees. These are expressed as security and privacy policies, interaction authorization policies, and service performance policies among others. The main security challenge in a cloud-based service environment, typically modeled using service-oriented architecture (SOA), is that it is difficult to trust all services in a service composition. In addition, the details of the services involved in an end-to-end service invocation chain are usually not exposed to the clients. The complexity of the SOA services and multi-tenancy in the cloud environment leads to a large attack surface. In this paper we propose a novel approach for end-to-end security and privacy in cloud-based service orchestrations, which uses a service activity monitor to audit activities of services in a domain. The service monitor intercepts interactions between a client and services, as well as among services, and provides a pluggable interface for different modules to analyze service interactions and make dynamic decisions based on security policies defined over the service domain. Experiments with a real-world service composition scenario demonstrate that the overhead of monitoring is acceptable for real-time operation of Web services.

Robert, Jr., Lionel P..  2016.  Monitoring and Trust in Virtual Teams. Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing. :245–259.

This study was conducted to determine whether monitoring moderated the impact of trust on the project performance of 57 virtual teams. Two sources of monitoring were examined: internal monitoring done by team members and external monitoring done by someone outside of the team. Two types of trust were also examined: affective-based trust, or trust based on emotion; and cognitive trust, or trust based on competency. Results indicate that when internal monitoring was high, affective trust was associated with increases in performance. However, affective trust was associated with decreases in performance when external monitoring was high. Both types of monitoring reduced the strong positive relationship between cognitive trust and the performance of virtual teams. Results of this study provide new insights about monitoring and trust in virtual teams and inform both theory and design.

Padmapriya, S., Valli, R., Jayekumar, M..  2020.  Monitoring Algorithm in Malicious Vehicular Adhoc Networks. 2020 International Conference on System, Computation, Automation and Networking (ICSCAN). :1—6.

Vehicular Adhoc Networks (VANETs) ensures road safety by communicating with a set of smart vehicles. VANET is a subset of Mobile Adhoc Networks (MANETs). VANET enabled vehicles helps in establishing communication services among one another or with the Road Side Unit (RSU). Information transmitted in VANET is distributed in an open access environment and hence security is one of the most critical issues related to VANET. Although each vehicle is not a source of all communications, most contact depends on the information that other vehicles receive from it. That vehicle must be able to assess, determine and respond locally on the information obtained from other vehicles to protect VANET from malicious act. Of this reason, message verification in VANET is more difficult due to the protection and privacy issues of the participating vehicles. To overcome security threats, we propose Monitoring Algorithm that detects malicious nodes based on the pre-selected threshold value. The threshold value is compared with the distrust value which is inherently tagged with each vehicle. The proposed Monitoring Algorithm not only detects malicious vehicles, but also isolates the malicious vehicles from the network. The proposed technique is simulated using Network Simulator2 (NS2) tool. The simulation result illustrated that the proposed Monitoring Algorithm outperforms the existing algorithms in terms of malicious node detection, network delay, packet delivery ratio and throughput, thereby uplifting the overall performance of the network.

Huang, Wei-Chiao, Yeh, Lo-Yao, Huang, Jiun-Long.  2019.  A Monitorable Peer-to-Peer File Sharing Mechanism. 2019 20th Asia-Pacific Network Operations and Management Symposium (APNOMS). :1–4.
With the rise of blockchain technology, peer-to-peer network system has once again caught people's attention. Peer-to-peer (P2P) is currently being implemented on various kind of decentralized systems such as InterPlanetary File System (IPFS). However, P2P file sharing network systems is not without its flaws. Data stored in the other nodes cannot be deleted by the owner and can only be deleted by other nodes themselves. Ensuring that personal data can be completely removed is an important issue to comply with the European Union's General Data Protection Regulation (GDPR) criteria. To improve P2Ps privacy and security, we propose a monitorable peer-to-peer file sharing mechanism that synchronizes with other nodes to perform file deletion and to generate the File Authentication Code (FAC) of each IPFS nodes in order to make sure the system synchronized correctly. The proposed mechanism can integrate with a consortium Blockchain to comply with GDPR.
Kurilova, Darya, Potanin, Alex, Aldrich, Jonathan.  2016.  Modules in Wyvern: Advanced Control over Security and Privacy. Proceedings of the Symposium and Bootcamp on the Science of Security. :68–68.

In today's systems, restricting the authority of untrusted code is difficult because, by default, code has the same authority as the user running it. Object capabilities are a promising way to implement the principle of least authority, but being too low-level and fine-grained, take away many conveniences provided by module systems. We present a module system design that is capability-safe, yet preserves most of the convenience of conventional module systems. We demonstrate how to ensure key security and privacy properties of a program as a mode of use of our module system. Our authority safety result formally captures the role of mutable state in capability-based systems and uses a novel non-transitive notion of authority, which allows us to reason about authority restriction: the encapsulation of a stronger capability inside a weaker one.

Shimakawa, Masaya, Osari, Kenji, Hagihara, Shigeki, Yonezaki, Naoki.  2017.  Modularization of Formal Specifications or Efficient Synthesis of Reactive Systems. Proceedings of the 6th International Conference on Software and Computer Applications. :208–213.
Reactive systems respond to requests from an environment with appropriate timing. Because reactive systems are used widely in infrastructure, it is necessary that they are developed without flaws. Automatic synthesis of reactive systems from particular specifications is an ideal technique for ensuring development without flaws. Several tools for synthesis have been proposed, e.g., Lily, AcaciaPlus and Unbeast. Among them, AcaciaPlus can synthesize systems compositionally, and enables synthesis from large-scale specifications that could not previously be treated. However, the modularization of specifications depends largely on the computation time required for synthesis; this is not a trivial problem. In this paper, we discuss the modularization of specifications to enable efficient synthesis of reactive systems.
Egert, Rolf, Grube, Tim, Born, Dustin, Mühlhäuser, Max.  2019.  Modular Vulnerability Indication for the IoT in IP-Based Networks. 2019 IEEE Globecom Workshops (GC Wkshps). :1—6.

With the rapidly increasing number of Internet of Things (IoT) devices and their extensive integration into peoples' daily lives, the security of those devices is of primary importance. Nonetheless, many IoT devices suffer from the absence, or the bad application, of security concepts, which leads to severe vulnerabilities in those devices. To achieve early detection of potential vulnerabilities, network scanner tools are frequently used. However, most of those tools are highly specialized; thus, multiple tools and a meaningful correlation of their results are required to obtain an adequate listing of identified network vulnerabilities. To simplify this process, we propose a modular framework for automated network reconnaissance and vulnerability indication in IP-based networks. It allows integrating a diverse set of tools as either, scanning tools or analysis tools. Moreover, the framework enables result aggregation of different modules and allows information sharing between modules facilitating the development of advanced analysis modules. Additionally, intermediate scanning and analysis data is stored, enabling a historical view of derived information and also allowing users to retrace decision-making processes. We show the framework's modular capabilities by implementing one scanner module and three analysis modules. The automated process is then evaluated using an exemplary scenario with common IP-based IoT components.

Mundra, Saloni, Sujata, Mitra, Suman K..  2019.  Modular Facial Expression Recognition on Noisy Data Using Robust PCA. 2019 IEEE 16th India Council International Conference (INDICON). :1—4.
Ricketts, Daniel, Malecha, Gregory, Lerner, Sorin.  2016.  Modular Deductive Verification of Sampled-data Systems. Proceedings of the 13th International Conference on Embedded Software. :17:1–17:10.

Unsafe behavior of cyber-physical systems can have disastrous consequences, motivating the need for formal verification of these kinds of systems. Deductive verification in a proof assistant such as Coq is a promising technique for this verification because it (1) justifies all verification from first principles, (2) is not limited to classes of systems for which full automation is possible, and (3) provides a platform for proving powerful, higher-order modularity theorems that are crucial for scaling verification to complex systems. In this paper, we demonstrate the practicality, utility, and scalability of this approach by developing in Coq sound and powerful rules for modular construction and verification of sampled-data cyber-physical systems. We evaluate these rules by using them to verify a number of non-trivial controllers enforcing safety properties of a quadcopter, e.g. a geo-fence. We show that our controllers are realistic by running them on a real, flying quadcopter.

Swetha, K., Kalyan, S. P., Pavan, V., Roshini, A..  2020.  A Modified Tiny Asymmetric Encryption for Secure Ftp to Network. 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS). :1176–1180.
The target of this venture is to give the protected correspondence among the associated frameworks in the system. It gives the vital validation to the record moving in the system transmission. It comprises of 3 modules in particular encryption and unscrambling module, secret key verification to the information that needs to transmit through system. In this system, File Transfer Protocol can be used to execute Server-client innovation and the document can be scrambled and unscrambled by sending the end client through attachment programming of the end client.
Vazirian, Samane, Zahedi, Morteza.  2016.  A modified language modeling method for authorship attribution. :32–37.

This paper presents an approach to a closed-class authorship attribution (AA) problem. It is based on language modeling for classification and called modified language modeling. Modified language modeling aims to offer a solution for AA problem by Combinations of both bigram words weighting and Unigram words weighting. It makes the relation between unseen text and training documents clearer with giving extra reward of training documents; training document including bigram word as well as unigram words. Moreover, IDF value multiplied by related word probability has been used, instead of removing stop words which are provided by Stop words list. we evaluate Experimental results by four approaches; unigram, bigram, trigram and modified language modeling by using two Persian poem corpora as WMPR-AA2016-A Dataset and WMPR-AA2016-B Dataset. Results show that modified language modeling attributes authors better than other approaches. The result on WMPR-AA2016-B, which is bigger dataset, is much better than another dataset for all approaches. This may indicate that if adequate data is provided to train language modeling the modified language modeling can be a good solution to AA problem.

Majhi, D., Rao, M., Sahoo, S., Dash, S. P., Mohapatra, D. P..  2020.  Modified Grey Wolf Optimization(GWO) based Accident Deterrence in Internet of Things (IoT) enabled Mining Industry. 2020 International Conference on Computer Science, Engineering and Applications (ICCSEA). :1–4.
The occurrences of accidents in mining industries owing to the fragile health conditions of mine workers are reportedly increasing. Health conditions measured as heart rate or pulse, glycemic index, and blood pressure are often crucial parameters that lead to failure in proper reasoning when not within acceptable ranges. These parameters, such as heartbeat rate can be measured continuously using sensors. The data can be monitored remotely and, when found to be of concern, can send necessary alarms to the mine manager. The early alarm notification enables the mine manager with better preparedness for managing the reach of first aid to the accident spot and thereby reduce mine fatalities drastically. This paper presents a framework for deterring accidents in mines with the help of the Grey Wolf Optimization approach.
Anju, J., Shreelekshmi, R..  2019.  Modified Feature Descriptors to enhance Secure Content-based Image Retrieval in Cloud. 2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT). 1:674–680.
With the emergence of cloud, content-based image retrieval (CBIR) on encrypted domain gain enormous importance due to the ever increasing need for ensuring confidentiality, authentication, integrity and privacy of data. CBIR on outsourced encrypted images can be done by extracting features from unencrypted images and generating searchable encrypted index based on it. Visual descriptors like color descriptors, shape and texture descriptors, etc. are employed for similarity search. Since visual descriptors used to represent an image have crucial role in retrieving most similar results, an attempt to combine them has been made in this paper. The effect of combining different visual descriptors on retrieval precision in secure CBIR scheme proposed by Xia et al. is analyzed. Experimental results show that combining visual descriptors can significantly enhance retrieval precision of the secure CBIR scheme.
Zhen Jiang, Shihong Miao, Pei Liu.  2014.  A Modified Empirical Mode Decomposition Filtering-Based Adaptive Phasor Estimation Algorithm for Removal of Exponentially Decaying DC Offset. Power Delivery, IEEE Transactions on. 29:1326-1334.

This paper proposes a modified empirical-mode decomposition (EMD) filtering-based adaptive dynamic phasor estimation algorithm for the removal of exponentially decaying dc offset. Discrete Fourier transform does not have the ability to attain the accurate phasor of the fundamental frequency component in digital protective relays under dynamic system fault conditions because the characteristic of exponentially decaying dc offset is not consistent. EMD is a fully data-driven, not model-based, adaptive filtering procedure for extracting signal components. But the original EMD technique has high computational complexity and requires a large data series. In this paper, a short data series-based EMD filtering procedure is proposed and an optimum hermite polynomial fitting (OHPF) method is used in this modified procedure. The proposed filtering technique has high accuracy and convergent speed, and is greatly appropriate for relay applications. This paper illustrates the characteristics of the proposed technique and evaluates its performance by computer-simulated signals, PSCAD/EMTDC-generated signals, and real power system fault signals.