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Michalevsky, Yan, Winetraub, Yonatan.  2017.  WaC: SpaceTEE - Secure and Tamper-Proof Computing in Space Using CubeSats. Proceedings of the 2017 Workshop on Attacks and Solutions in Hardware Security. :27–32.
Sensitive computation often has to be performed in a trusted execution environment (TEE), which, in turn, requires tamper-proof hardware. If the computational fabric can be tampered with, we may no longer be able to trust the correctness of the computation. We study the (wild and crazy) idea of using computational platforms in space as a means to protect data from adversarial physical access. In this paper, we propose SpaceTEE - a practical implementation of this approach using low-cost nano-satellites called CubeSats. We study the constraints of such a platform, the cost of deployment, and discuss possible applications under those constraints. As a case study, we design a hardware security module solution (called SpaceHSM) and describe how it can be used to implement a root-of-trust for a certificate authority (CA).
Tekgul, Buse G. A., Xia, Yuxi, Marchal, Samuel, Asokan, N..  2021.  WAFFLE: Watermarking in Federated Learning. 2021 40th International Symposium on Reliable Distributed Systems (SRDS). :310–320.

Federated learning is a distributed learning technique where machine learning models are trained on client devices in which the local training data resides. The training is coordinated via a central server which is, typically, controlled by the intended owner of the resulting model. By avoiding the need to transport the training data to the central server, federated learning improves privacy and efficiency. But it raises the risk of model theft by clients because the resulting model is available on every client device. Even if the application software used for local training may attempt to prevent direct access to the model, a malicious client may bypass any such restrictions by reverse engineering the application software. Watermarking is a well-known deterrence method against model theft by providing the means for model owners to demonstrate ownership of their models. Several recent deep neural network (DNN) watermarking techniques use backdooring: training the models with additional mislabeled data. Backdooring requires full access to the training data and control of the training process. This is feasible when a single party trains the model in a centralized manner, but not in a federated learning setting where the training process and training data are distributed among several client devices. In this paper, we present WAFFLE, the first approach to watermark DNN models trained using federated learning. It introduces a retraining step at the server after each aggregation of local models into the global model. We show that WAFFLE efficiently embeds a resilient watermark into models incurring only negligible degradation in test accuracy (-0.17%), and does not require access to training data. We also introduce a novel technique to generate the backdoor used as a watermark. It outperforms prior techniques, imposing no communication, and low computational (+3.2%) overhead$^\textrm1$$^\textrm1$\$The research report version of this paper is also available in https://arxiv.org/abs/2008.07298, and the code for reproducing our work can be found at https://github.com/ssg-research/WAFFLE.

Sethi, Ricky J., Buell, Catherine A., Seeley, William P..  2018.  WAIVS: An Intelligent Interface for Visual Stylometry Using Semantic Workflows. Proceedings of the 23rd International Conference on Intelligent User Interfaces Companion. :54:1-54:2.

In this paper, we present initial work towards creating an intelligent interface that can act as an open access laboratory for visual stylometry called WAIVS, Workflows for Analysis of Images and Visual Stylometry. WAIVS allows scholars, students, and other interested parties to explore the nature of artistic style using cutting-edge research methods in visual stylometry. We create semantic workflows for this interface using various computer vision algorithms that not only facilitate artistically significant analyses but also impose intelligent semantic constraints on complex analyses. In the interface, we combine these workflows with a manually-curated dataset for analysis of artistic style based on either the school of art or the medium.

Jiang, Zhongyuan, Ma, Jianfeng, Yu, Philip S..  2019.  Walk2Privacy: Limiting target link privacy disclosure against the adversarial link prediction. 2019 IEEE International Conference on Big Data (Big Data). :1381—1388.

The disclosure of an important yet sensitive link may cause serious privacy crisis between two users of a social graph. Only deleting the sensitive link referred to as a target link which is often the attacked target of adversaries is not enough, because the adversarial link prediction can deeply forecast the existence of the missing target link. Thus, to defend some specific adversarial link prediction, a budget limited number of other non-target links should be optimally removed. We first propose a path-based dissimilarity function as the optimizing objective and prove that the greedy link deletion to preserve target link privacy referred to as the GLD2Privacy which has monotonicity and submodularity properties can achieve a near optimal solution. However, emulating all length limited paths between any pair of nodes for GLD2Privacy mechanism is impossible in large scale social graphs. Secondly, we propose a Walk2Privacy mechanism that uses self-avoiding random walk which can efficiently run in large scale graphs to sample the paths of given lengths between the two ends of any missing target link, and based on the sampled paths we select the alternative non-target links being deleted for privacy purpose. Finally, we compose experiments to demonstrate that the Walk2Privacy algorithm can remarkably reduce the time consumption and achieve a very near solution that is achieved by the GLD2Privacy.

Z. Zhu, M. B. Wakin.  2015.  "Wall clutter mitigation and target detection using Discrete Prolate Spheroidal Sequences". 2015 3rd International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing (CoSeRa). :41-45.

We present a new method for mitigating wall return and a new greedy algorithm for detecting stationary targets after wall clutter has been cancelled. Given limited measurements of a stepped-frequency radar signal consisting of both wall and target return, our objective is to detect and localize the potential targets. Modulated Discrete Prolate Spheroidal Sequences (DPSS's) form an efficient basis for sampled bandpass signals. We mitigate the wall clutter efficiently within the compressive measurements through the use of a bandpass modulated DPSS basis. Then, in each step of an iterative algorithm for detecting the target positions, we use a modulated DPSS basis to cancel nearly all of the target return corresponding to previously selected targets. With this basis, we improve upon the target detection sensitivity of a Fourier-based technique.

Kuai, Jun, He, Jiaji, Ma, Haocheng, Zhao, Yiqiang, Hou, Yumin, Jin, Yier.  2020.  WaLo: Security Primitive Generator for RT-Level Logic Locking and Watermarking. 2020 Asian Hardware Oriented Security and Trust Symposium (AsianHOST). :01—06.
Various hardware security solutions have been developed recently to help counter hardware level attacks such as hardware Trojan, integrated circuit (IC) counterfeiting and intellectual property (IP) clone/piracy. However, existing solutions often provide specific types of protections. While these solutions achieve great success in preventing even advanced hardware attacks, the compatibility of among these hardware security methods are rarely discussed. The inconsistency hampers with the development of a comprehensive solution for hardware IC and IP from various attacks. In this paper, we develop a security primitive generator to help solve the compatibility issue among different protection techniques. Specifically, we focus on two modern IC/IP protection methods, logic locking and watermarking. A combined locking and watermarking technique is developed based on enhanced finite state machines (FSMs). The security primitive generator will take user-specified constraints and automatically generate an FSM module to perform both logic locking and watermarking. The generated FSM can be integrated into any designs for protection. Our experimental results show that the generator can facilitate circuit protection and provide the flexibility for users to achieve a better tradeoff between security levels and design overheads.
Sudugala, A.U, Chanuka, W.H, Eshan, A.M.N, Bandara, U.C.S, Abeywardena, K.Y.  2020.  WANHEDA: A Machine Learning Based DDoS Detection System. 2020 2nd International Conference on Advancements in Computing (ICAC). 1:380–385.
In today's world computer communication is used almost everywhere and majority of them are connected to the world's largest network, the Internet. There is danger in using internet due to numerous cyber-attacks which are designed to attack Confidentiality, Integrity and Availability of systems connected to the internet. One of the most prominent threats to computer networking is Distributed Denial of Service (DDoS) Attack. They are designed to attack availability of the systems. Many users and ISPs are targeted and affected regularly by these attacks. Even though new protection technologies are continuously proposed, this immense threat continues to grow rapidly. Most of the DDoS attacks are undetectable because they act as legitimate traffic. This situation can be partially overcome by using Intrusion Detection Systems (IDSs). There are advanced attacks where there is no proper documented way to detect. In this paper authors present a Machine Learning (ML) based DDoS detection mechanism with improved accuracy and low false positive rates. The proposed approach gives inductions based on signatures previously extracted from samples of network traffic. Authors perform the experiments using four distinct benchmark datasets, four machine learning algorithms to address four of the most harmful DDoS attack vectors. Authors achieved maximum accuracy and compared the results with other applicable machine learning algorithms.
M. Grottke, A. Avritzer, D. S. Menasché, J. Alonso, L. Aguiar, S. G. Alvarez.  2015.  "WAP: Models and metrics for the assessment of critical-infrastructure-targeted malware campaigns". 2015 IEEE 26th International Symposium on Software Reliability Engineering (ISSRE). :330-335.

Ensuring system survivability in the wake of advanced persistent threats is a big challenge that the security community is facing to ensure critical infrastructure protection. In this paper, we define metrics and models for the assessment of coordinated massive malware campaigns targeting critical infrastructure sectors. First, we develop an analytical model that allows us to capture the effect of neighborhood on different metrics (infection probability and contagion probability). Then, we assess the impact of putting operational but possibly infected nodes into quarantine. Finally, we study the implications of scanning nodes for early detection of malware (e.g., worms), accounting for false positives and false negatives. Evaluating our methodology using a small four-node topology, we find that malware infections can be effectively contained by using quarantine and appropriate rates of scanning for soft impacts.

Querel, Louis-Philippe, Rigby, Peter C..  2018.  WarningsGuru: Integrating Statistical Bug Models with Static Analysis to Provide Timely and Specific Bug Warnings. Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. :892-895.

The detection of bugs in software systems has been divided into two research areas: static code analysis and statistical modeling of historical data. Static analysis indicates precise problems on line numbers but has the disadvantage of suggesting many warning which are often false positives. In contrast, statistical models use the history of the system to suggest which files or commits are likely to contain bugs. These course-grained predictions do not indicate to the developer the precise reasons for the bug prediction. We combine static analysis with statistical bug models to limit the number of warnings and provide specific warnings information at the line level. Previous research was able to process only a limited number of releases, our tool, WarningsGuru, can analyze all commits in a source code repository and we currently have processed thousands of commits and warnings. Since we process every commit, we present developers with more precise information about when a warning is introduced allowing us to show recent warnings that are introduced in statistically risky commits. Results from two OSS projects show that CommitGuru's statistical model flags 25% and 29% of all commits as risky. When we combine this with static analysis in WarningsGuru the number of risky commits with warnings is 20% for both projects and the number commits with new warnings is only 3% and 6%. We can drastically reduce the number of commits and warnings developers have to examine. The tool, source code, and demo is available at https://github.com/louisq/warningsguru.

Hoyle, Roberto, Das, Srijita, Kapadia, Apu, Lee, Adam J., Vaniea, Kami.  2017.  Was My Message Read?: Privacy and Signaling on Facebook Messenger Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. :3838–3842.

Major online messaging services such as Facebook Messenger and WhatsApp are starting to provide users with real-time information about when people read their messages, while useful, the feature has the potential to negatively impact privacy as well as cause concern over access to self. We report on two surveys using Mechanical Turk which looked at senders' (N=402\vphantom\\ use of and reactions to the `message seen' feature, and recipients' (N=316) privacy and signaling behaviors in the face of such visibility. Our findings indicate that senders experience a range of emotions when their message is not read, or is read but not answered immediately. Recipients also engage in various signaling behaviors in the face of visibility by both replying or not replying immediately.

Li, Bingzhe, Du, David.  2021.  WAS-Deletion: Workload-Aware Secure Deletion Scheme for Solid-State Drives. 2021 IEEE 39th International Conference on Computer Design (ICCD). :244–247.
Due to the intrinsic properties of Solid-State Drives (SSDs), invalid data remain in SSDs before erased by a garbage collection process, which increases the risk of being attacked by adversaries. Previous studies use erase and cryptography based schemes to purposely delete target data but face extremely large overhead. In this paper, we propose a Workload-Aware Secure Deletion scheme, called WAS-Deletion, to reduce the overhead of secure deletion by three major components. First, the WAS-Deletion scheme efficiently splits invalid and valid data into different blocks based on workload characteristics. Second, the WAS-Deletion scheme uses a new encryption allocation scheme, making the encryption follow the same direction as the write on multiple blocks and vertically encrypts pages with the same key in one block. Finally, a new adaptive scheduling scheme can dynamically change the configurations of different regions to further reduce secure deletion overhead based on the current workload. The experimental results indicate that the newly proposed WAS-Deletion scheme can reduce the secure deletion cost by about 1.2x to 12.9x compared to previous studies.
Quinn, Ren, Holguin, Nico, Poster, Ben, Roach, Corey, Merwe, Jacobus Kobus Van der.  2019.  WASPP: Workflow Automation for Security Policy Procedures. 2019 15th International Conference on Network and Service Management (CNSM). :1–5.

Every day, university networks are bombarded with attempts to steal the sensitive data of the various disparate domains and organizations they serve. For this reason, universities form teams of information security specialists called a Security Operations Center (SOC) to manage the complex operations involved in monitoring and mitigating such attacks. When a suspicious event is identified, members of the SOC are tasked to understand the nature of the event in order to respond to any damage the attack might have caused. This process is defined by administrative policies which are often very high-level and rarely systematically defined. This impedes the implementation of generalized and automated event response solutions, leading to specific ad hoc solutions based primarily on human intuition and experience as well as immediate administrative priorities. These solutions are often fragile, highly specific, and more difficult to reuse in other scenarios.

Han, Yi, Etigowni, Sriharsha, Liu, Hua, Zonouz, Saman, Petropulu, Athina.  2017.  Watch Me, but Don'T Touch Me! Contactless Control Flow Monitoring via Electromagnetic Emanations. Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. :1095–1108.

Trustworthy operation of industrial control systems depends on secure and real-time code execution on the embedded programmable logic controllers (PLCs). The controllers monitor and control the critical infrastructures, such as electric power grids and healthcare platforms, and continuously report back the system status to human operators. We present Zeus, a contactless embedded controller security monitor to ensure its execution control flow integrity. Zeus leverages the electromagnetic emission by the PLC circuitry during the execution of the controller programs. Zeus's contactless execution tracking enables non-intrusive monitoring of security-critical controllers with tight real-time constraints. Those devices often cannot tolerate the cost and performance overhead that comes with additional traditional hardware or software monitoring modules. Furthermore, Zeus provides an air-gap between the monitor (trusted computing base) and the target (potentially compromised) PLC. This eliminates the possibility of the monitor infection by the same attack vectors. Zeus monitors for control flow integrity of the PLC program execution. Zeus monitors the communications between the human machine interface and the PLC, and captures the control logic binary uploads to the PLC. Zeus exercises its feasible execution paths, and fingerprints their emissions using an external electromagnetic sensor. Zeus trains a neural network for legitimate PLC executions, and uses it at runtime to identify the control flow based on PLC's electromagnetic emissions. We implemented Zeus on a commercial Allen Bradley PLC, which is widely used in industry, and evaluated it on real-world control program executions. Zeus was able to distinguish between different legitimate and malicious executions with 98.9% accuracy and with zero overhead on PLC execution by design.

Saifuddin, K. M., Ali, A. J. B., Ahmed, A. S., Alam, S. S., Ahmad, A. S..  2018.  Watchdog and Pathrater based Intrusion Detection System for MANET. 2018 4th International Conference on Electrical Engineering and Information Communication Technology (iCEEiCT). :168–173.

Mobile Ad Hoc Network (MANET) is pretty vulnerable to attacks because of its broad distribution and open nodes. Hence, an effective Intrusion Detection System (IDS) is vital in MANET to deter unwanted malicious attacks. An IDS has been proposed in this paper based on watchdog and pathrater method as well as evaluation of its performance has been presented using Dynamic Source Routing (DSR) and Ad-hoc On-demand Distance Vector (AODV) routing protocols with and without considering the effect of the sinkhole attack. The results obtained justify that the proposed IDS is capable of detecting suspicious activities and identifying the malicious nodes. Moreover, it replaces the fake route with a real one in the routing table in order to mitigate the security risks. The performance appraisal also suggests that the AODV protocol has a capacity of sending more packets than DSR and yields more throughput.

Gao, Yang, Li, Borui, Wang, Wei, Xu, Wenyao, Zhou, Chi, Jin, Zhanpeng.  2018.  Watching and Safeguarding Your 3D Printer: Online Process Monitoring Against Cyber-Physical Attacks. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.. 2:108:1–108:27.

The increasing adoption of 3D printing in many safety and mission critical applications exposes 3D printers to a variety of cyber attacks that may result in catastrophic consequences if the printing process is compromised. For example, the mechanical properties (e.g., physical strength, thermal resistance, dimensional stability) of 3D printed objects could be significantly affected and degraded if a simple printing setting is maliciously changed. To address this challenge, this study proposes a model-free real-time online process monitoring approach that is capable of detecting and defending against the cyber-physical attacks on the firmwares of 3D printers. Specifically, we explore the potential attacks and consequences of four key printing attributes (including infill path, printing speed, layer thickness, and fan speed) and then formulate the attack models. Based on the intrinsic relation between the printing attributes and the physical observations, our defense model is established by systematically analyzing the multi-faceted, real-time measurement collected from the accelerometer, magnetometer and camera. The Kalman filter and Canny filter are used to map and estimate three aforementioned critical toolpath information that might affect the printing quality. Mel-frequency Cepstrum Coefficients are used to extract features for fan speed estimation. Experimental results show that, for a complex 3D printed design, our method can achieve 4% Hausdorff distance compared with the model dimension for infill path estimate, 6.07% Mean Absolute Percentage Error (MAPE) for speed estimate, 9.57% MAPE for layer thickness estimate, and 96.8% accuracy for fan speed identification. Our study demonstrates that, this new approach can effectively defend against the cyber-physical attacks on 3D printers and 3D printing process.

Shalev, Noam, Keidar, Idit, Moatti, Yosef, Weinsberg, Yaron.  2016.  WatchIT: Who Watches Your IT Guy? Proceedings of the 8th ACM CCS International Workshop on Managing Insider Security Threats. :93–96.

System administrators have unlimited access to system resources. As the Snowden case shows, these permissions can be exploited to steal valuable personal, classified, or commercial data. In this work we propose a strategy that increases the organizational information security by constraining IT personnel's view of the system and monitoring their actions. To this end, we introduce the abstraction of perforated containers – while regular Linux containers are too restrictive to be used by system administrators, by "punching holes" in them, we strike a balance between information security and required administrative needs. Our system predicts which system resources should be accessible for handling each IT issue, creates a perforated container with the corresponding isolation, and deploys it in the corresponding machines as needed for fixing the problem. Under this approach, the system administrator retains his superuser privileges, while he can only operate within the container limits. We further provide means for the administrator to bypass the isolation, and perform operations beyond her boundaries. However, such operations are monitored and logged for later analysis and anomaly detection. We provide a proof-of-concept implementation of our strategy, along with a case study on the IT database of IBM Research in Israel.

Wang, Y., Wen, M., Liu, Y., Wang, Y., Li, Z., Wang, C., Yu, H., Cheung, S.-C., Xu, C., Zhu, Z..  2020.  Watchman: Monitoring Dependency Conflicts for Python Library Ecosystem. 2020 IEEE/ACM 42nd International Conference on Software Engineering (ICSE). :125–135.
The PyPI ecosystem has indexed millions of Python libraries to allow developers to automatically download and install dependencies of their projects based on the specified version constraints. Despite the convenience brought by automation, version constraints in Python projects can easily conflict, resulting in build failures. We refer to such conflicts as Dependency Conflict (DC) issues. Although DC issues are common in Python projects, developers lack tool support to gain a comprehensive knowledge for diagnosing the root causes of these issues. In this paper, we conducted an empirical study on 235 real-world DC issues. We studied the manifestation patterns and fixing strategies of these issues and found several key factors that can lead to DC issues and their regressions. Based on our findings, we designed and implemented Watchman, a technique to continuously monitor dependency conflicts for the PyPI ecosystem. In our evaluation, Watchman analyzed PyPI snapshots between 11 Jul 2019 and 16 Aug 2019, and found 117 potential DC issues. We reported these issues to the developers of the corresponding projects. So far, 63 issues have been confirmed, 38 of which have been quickly fixed by applying our suggested patches.
Gong, Peiyong, Zheng, Kai, Jiang, Yi, Liu, Jia.  2021.  Water Surface Object Detection Based on Neural Style Learning Algorithm. 2021 40th Chinese Control Conference (CCC). :8539—8543.
In order to detect the objects on the water surface, a neural style learning algorithm is proposed in this paper. The algorithm uses the Gram matrix of a pre-trained convolutional neural network to represent the style of the texture in the image, which is originally used for image style transfer. The objects on the water surface can be easily distinguished by the difference in their styles of the image texture. The algorithm is tested on the dataset of the Airbus Ship Detection Challenge on Kaggle. Compared to the other water surface object detection algorithms, the proposed algorithm has a good precision of 0.925 with recall equals to 0.86.
K. Cavalleri, B. Brinkman.  2015.  "Water treatment in context: resources and African religion". 2015 Systems and Information Engineering Design Symposium. :19-23.

Drinking water availability is a crucial problem that must be addressed in order to improve the quality of life of individuals living developing nations. Improving water supply availability is important for public health, as it is the third highest risk factor for poor health in developing nations with high mortality rates. This project researched drinking water filtration for areas of Sub-Saharan Africa near existing bodies of water, where the populations are completely reliant on collecting from surface water sources: the most contaminated water source type. Water filtration methods that can be completely created by the consumer would alleviate aid organization dependence in developing nations, put the consumers in control, and improve public health. Filtration processes pass water through a medium that will catch contaminants through physical entrapment or absorption and thus yield a cleaner effluent. When exploring different materials for filtration, removal of contaminants and hydraulic conductivity are the two most important components. Not only does the method have to treat the water, but also it has to do so in a timeframe that is quick enough to produce potable water at a rate that keeps up with everyday needs. Cement is easily accessible in Sub- Saharan regions. Most concrete mixtures are not meant to be pervious, as it is a construction material used for its compressive strength, however, reduced water content in a cement mixture gives it higher permeability. Several different concrete samples of varying thicknesses and water concentrations were created. Bacterial count tests were performed on both pre-filtered and filtered water samples. Concrete filtration does remove bacteria from drinking water, however, the method can still be improved upon.

Nasr, Milad, Zolfaghari, Hadi, Houmansadr, Amir.  2017.  The Waterfall of Liberty: Decoy Routing Circumvention That Resists Routing Attacks. Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. :2037–2052.

Decoy routing is an emerging approach for censorship circumvention in which circumvention is implemented with help from a number of volunteer Internet autonomous systems, called decoy ASes. Recent studies on decoy routing consider all decoy routing systems to be susceptible to a fundamental attack – regardless of their specific designs–in which the censors re-route traffic around decoy ASes, thereby preventing censored users from using such systems. In this paper, we propose a new architecture for decoy routing that, by design, is significantly stronger to rerouting attacks compared to all previous designs. Unlike previous designs, our new architecture operates decoy routers only on the downstream traffic of the censored users; therefore we call it downstream-only decoy routing. As we demonstrate through Internet-scale BGP simulations, downstream-only decoy routing offers significantly stronger resistance to rerouting attacks, which is intuitively because a (censoring) ISP has much less control on the downstream BGP routes of its traffic. Designing a downstream-only decoy routing system is a challenging engineering problem since decoy routers do not intercept the upstream traffic of censored users. We design the first downstream-only decoy routing system, called Waterfall, by devising unique covert communication mechanisms. We also use various techniques to make our Waterfall implementation resistant to traffic analysis attacks. We believe that downstream-only decoy routing is a significant step towards making decoy routing systems practical. This is because a downstream-only decoy routing system can be deployed using a significantly smaller number of volunteer ASes, given a target resistance to rerouting attacks. For instance, we show that a Waterfall implementation with only a single decoy AS is as resistant to routing attacks (against China) as a traditional decoy system (e.g., Telex) with 53 decoy ASes.

Wang, Yahui, Cui, Qiushi, Tang, Xinlu, Li, Dongdong, Chen, Tao.  2021.  Waveform Vector Embedding for Incipient Fault Detection in Distribution Systems. 2021 IEEE Sustainable Power and Energy Conference (iSPEC). :3873–3879.
Incipient faults are faults at their initial stages and occur before permanent faults occur. It is very important to detect incipient faults timely and accurately for the safe and stable operation of the power system. At present, most of the detection methods for incipient faults are designed for the detection of a single device’s incipient fault, but a unified detection for multiple devices cannot be achieved. In order to increase the fault detection capability and enable detection expandability, this paper proposes a waveform vector embedding (WVE) method to embed incipient fault waveforms of different devices into waveform vectors. Then, we utilize the waveform vectors and formulate them into a waveform dictionary. To improve the efficiency of embedding the waveform signature into the learning process, we build a loss function that prevents overflow and overfitting of softmax function during when learning power system waveforms. We use the real data collected from an IEEE Power & Energy Society technical report to verify the feasibility of this method. For the result verification, we compare the superiority of this method with Logistic Regression and Support Vector Machine in different scenarios.
Feng, W., Yan, W., Wu, S., Liu, N..  2017.  Wavelet transform and unsupervised machine learning to detect insider threat on cloud file-sharing. 2017 IEEE International Conference on Intelligence and Security Informatics (ISI). :155–157.

As increasingly more enterprises are deploying cloud file-sharing services, this adds a new channel for potential insider threats to company data and IPs. In this paper, we introduce a two-stage machine learning system to detect anomalies. In the first stage, we project the access logs of cloud file-sharing services onto relationship graphs and use three complementary graph-based unsupervised learning methods: OddBall, PageRank and Local Outlier Factor (LOF) to generate outlier indicators. In the second stage, we ensemble the outlier indicators and introduce the discrete wavelet transform (DWT) method, and propose a procedure to use wavelet coefficients with the Haar wavelet function to identify outliers for insider threat. The proposed system has been deployed in a real business environment, and demonstrated effectiveness by selected case studies.

Goel, N., Sharma, A., Goswami, S..  2017.  A way to secure a QR code: SQR. 2017 International Conference on Computing, Communication and Automation (ICCCA). :494–497.

Now a day, need for fast accessing of data is increasing with the exponential increase in the security field. QR codes have served as a useful tool for fast and convenient sharing of data. But with increased usage of QR Codes have become vulnerable to attacks such as phishing, pharming, manipulation and exploitation. These security flaws could pose a danger to an average user. In this paper we have proposed a way, called Secured QR (SQR) to fix all these issues. In this approach we secure a QR code with the help of a key in generator side and the same key is used to get the original information at scanner side. We have used AES algorithm for this purpose. SQR approach is applicable when we want to share/use sensitive information in the organization such as sharing of profile details, exchange of payment information, business cards, generation of electronic tickets etc.

Nathezhtha, T., Sangeetha, D., Vaidehi, V..  2019.  WC-PAD: Web Crawling based Phishing Attack Detection. 2019 International Carnahan Conference on Security Technology (ICCST). :1–6.
Phishing is a criminal offense which involves theft of user's sensitive data. The phishing websites target individuals, organizations, the cloud storage hosting sites and government websites. Currently, hardware based approaches for anti-phishing is widely used but due to the cost and operational factors software based approaches are preferred. The existing phishing detection approaches fails to provide solution to problem like zero-day phishing website attacks. To overcome these issues and precisely detect phishing occurrence a three phase attack detection named as Web Crawler based Phishing Attack Detector(WC-PAD) has been proposed. It takes the web traffics, web content and Uniform Resource Locator(URL) as input features, based on these features classification of phishing and non phishing websites are done. The experimental analysis of the proposed WC-PAD is done with datasets collected from real phishing cases. From the experimental results, it is found that the proposed WC-PAD gives 98.9% accuracy in both phishing and zero-day phishing attack detection.