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Abaid, Z., Kaafar, M. A., Jha, S..  2017.  Early Detection of In-the-Wild Botnet Attacks by Exploiting Network Communication Uniformity: An Empirical Study. 2017 IFIP Networking Conference (IFIP Networking) and Workshops. :1–9.

Distributed attacks originating from botnet-infected machines (bots) such as large-scale malware propagation campaigns orchestrated via spam emails can quickly affect other network infrastructures. As these attacks are made successful only by the fact that hundreds of infected machines engage in them collectively, their damage can be avoided if machines infected with a common botnet can be detected early rather than after an attack is launched. Prior studies have suggested that outgoing bot attacks are often preceded by other ``tell-tale'' malicious behaviour, such as communication with botnet controllers (C&C servers) that command botnets to carry out attacks. We postulate that observing similar behaviour occuring in a synchronised manner across multiple machines is an early indicator of a widespread infection of a single botnet, leading potentially to a large-scale, distributed attack. Intuitively, if we can detect such synchronised behaviour early enough on a few machines in the network, we can quickly contain the threat before an attack does any serious damage. In this work we present a measurement-driven analysis to validate this intuition. We empirically analyse the various stages of malicious behaviour that are observed in real botnet traffic, and carry out the first systematic study of the network behaviour that typically precedes outgoing bot attacks and is synchronised across multiple infected machines. We then implement as a proof-of-concept a set of analysers that monitor synchronisation in botnet communication to generate early infection and attack alerts. We show that with this approach, we can quickly detect nearly 80% of real-world spamming and port scanning attacks, and even demonstrate a novel capability of preventing these attacks altogether by predicting them before they are launched.

Papakonstantinou, Nikolaos, Linnosmaa, Joonas, Alanen, Jarmo, Bashir, Ahmed Z., O'Halloran, Bryan, Van Bossuyt, Douglas L..  2019.  Early Hybrid Safety and Security Risk Assessment Based on Interdisciplinary Dependency Models. 2019 Annual Reliability and Maintainability Symposium (RAMS). :1–7.
Safety and security of complex critical infrastructures are very important for economic, environmental and social reasons. The complexity of these systems introduces difficulties in the identification of safety and security risks that emerge from interdisciplinary interactions and dependencies. The discovery of safety and security design weaknesses late in the design process and during system operation can lead to increased costs, additional system complexity, delays and possibly undesirable compromises to address safety and security weaknesses.
Pang, Y., Xue, X., Namin, A. S..  2016.  Early Identification of Vulnerable Software Components via Ensemble Learning. 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA). :476–481.

Software components, which are vulnerable to being exploited, need to be identified and patched. Employing any prevention techniques designed for the purpose of detecting vulnerable software components in early stages can reduce the expenses associated with the software testing process significantly and thus help building a more reliable and robust software system. Although previous studies have demonstrated the effectiveness of adapting prediction techniques in vulnerability detection, the feasibility of those techniques is limited mainly because of insufficient training data sets. This paper proposes a prediction technique targeting at early identification of potentially vulnerable software components. In the proposed scheme, the potentially vulnerable components are viewed as mislabeled data that may contain true but not yet observed vulnerabilities. The proposed hybrid technique combines the supports vector machine algorithm and ensemble learning strategy to better identify potential vulnerable components. The proposed vulnerability detection scheme is evaluated using some Java Android applications. The results demonstrated that the proposed hybrid technique could identify potentially vulnerable classes with high precision and relatively acceptable accuracy and recall.

Halawa, Hassan, Ripeanu, Matei, Beznosov, Konstantin, Coskun, Baris, Liu, Meizhu.  2017.  An Early Warning System for Suspicious Accounts. Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security. :51–52.
In the face of large-scale automated cyber-attacks to large online services, fast detection and remediation of compromised accounts are crucial to limit the spread of new attacks and to mitigate the overall damage to users, companies, and the public at large. We advocate a fully automated approach based on machine learning to enable large-scale online service providers to quickly identify potentially compromised accounts. We develop an early warning system for the detection of suspicious account activity with the goal of quick identification and remediation of compromised accounts. We demonstrate the feasibility and applicability of our proposed system in a four month experiment at a large-scale online service provider using real-world production data encompassing hundreds of millions of users. We show that - even using only login data, features with low computational cost, and a basic model selection approach - around one out of five accounts later flagged as suspicious are correctly predicted a month in advance based on one week's worth of their login activity.
Yuxi Liu, Hatzinakos, D..  2014.  Earprint: Transient Evoked Otoacoustic Emission for Biometrics. Information Forensics and Security, IEEE Transactions on. 9:2291-2301.

Biometrics is attracting increasing attention in privacy and security concerned issues, such as access control and remote financial transaction. However, advanced forgery and spoofing techniques are threatening the reliability of conventional biometric modalities. This has been motivating our investigation of a novel yet promising modality transient evoked otoacoustic emission (TEOAE), which is an acoustic response generated from cochlea after a click stimulus. Unlike conventional modalities that are easily accessible or captured, TEOAE is naturally immune to replay and falsification attacks as a physiological outcome from human auditory system. In this paper, we resort to wavelet analysis to derive the time-frequency representation of such nonstationary signal, which reveals individual uniqueness and long-term reproducibility. A machine learning technique linear discriminant analysis is subsequently utilized to reduce intrasubject variability and further capture intersubject differentiation features. Considering practical application, we also introduce a complete framework of the biometric system in both verification and identification modes. Comparative experiments on a TEOAE data set of biometric setting show the merits of the proposed method. Performance is further improved with fusion of information from both ears.

Dong, B., Wang, H.(.  2017.  EARRING: Efficient Authentication of Outsourced Record Matching. 2017 IEEE International Conference on Information Reuse and Integration (IRI). :225–234.

Cloud computing enables the outsourcing of big data analytics, where a third-party server is responsible for data management and processing. In this paper, we consider the outsourcing model in which a third-party server provides record matching as a service. In particular, given a target record, the service provider returns all records from the outsourced dataset that match the target according to specific distance metrics. Identifying matching records in databases plays an important role in information integration and entity resolution. A major security concern of this outsourcing paradigm is whether the service provider returns the correct record matching results. To solve the problem, we design EARRING, an Efficient Authentication of outsouRced Record matchING framework. EARRING requires the service provider to construct the verification object (VO) of the record matching results. From the VO, the client is able to catch any incorrect result with cheap computational cost. Experiment results on real-world datasets demonstrate the efficiency of EARRING.

Reinbrecht, Cezar, Forlin, Bruno, Zankl, Andreas, Sepulveda, Johanna.  2018.  Earthquake — A NoC-based optimized differential cache-collision attack for MPSoCs. 2018 Design, Automation Test in Europe Conference Exhibition (DATE). :648—653.
Multi-Processor Systems-on-Chips (MPSoCs) are a platform for a wide variety of applications and use-cases. The high on-chip connectivity, the programming flexibility, and the reuse of IPs, however, also introduce security concerns. Problems arise when applications with different trust and protection levels share resources of the MPSoC, such as processing units, cache memories and the Network-on-Chip (NoC) communication structure. If a program gets compromised, an adversary can observe the use of these resources and infer (potentially secret) information from other applications. In this work, we explore the cache-based attack by Bogdanov et al., which infers the cache activity of a target program through timing measurements and exploits collisions that occur when the same cache location is accessed for different program inputs. We implement this differential cache-collision attack on the MPSoC Glass and introduce an optimized variant of it, the Earthquake Attack, which leverages the NoC-based communication to increase attack efficiency. Our results show that Earthquake performs well under different cache line and MPSoC configurations, illustrating that cache-collision attacks are considerable threats on MPSoCs.
Ivanova, M., Durcheva, M., Baneres, D., Rodríguez, M. E..  2018.  eAssessment by Using a Trustworthy System in Blended and Online Institutions. 2018 17th International Conference on Information Technology Based Higher Education and Training (ITHET). :1-7.

eAssessment uses technology to support online evaluation of students' knowledge and skills. However, challenging problems must be addressed such as trustworthiness among students and teachers in blended and online settings. The TeSLA system proposes an innovative solution to guarantee correct authentication of students and to prove the authorship of their assessment tasks. Technologically, the system is based on the integration of five instruments: face recognition, voice recognition, keystroke dynamics, forensic analysis, and plagiarism. The paper aims to analyze and compare the results achieved after the second pilot performed in an online and a blended university revealing the realization of trust-driven solutions for eAssessment.

Lu, Yiqin, Wang, Meng.  2016.  An Easy Defense Mechanism Against Botnet-based DDoS Flooding Attack Originated in SDN Environment Using sFlow. Proceedings of the 11th International Conference on Future Internet Technologies. :14–20.

As today's networks become larger and more complex, the Distributed Denial of Service (DDoS) flooding attack threats may not only come from the outside of networks but also from inside, such as cloud computing network where exists multiple tenants possibly containing malicious tenants. So, the need of source-based defense mechanism against such attacks is pressing. In this paper, we mainly focus on the source-based defense mechanism against Botnet-based DDoS flooding attack through combining the power of Software-Defined Networking (SDN) and sample flow (sFlow) technology. Firstly, we defined a metric to measure the essential features of this kind attack which means distribution and collaboration. Then we designed a simple detection algorithm based on statistical inference model and response scheme through the abilities of SDN. Finally, we developed an application to realize our idea and also tested its effect on emulation network with real network traffic. The result shows that our mechanism could effectively detect DDoS flooding attack originated in SDN environment and identify attack flows for avoiding the harm of attack spreading to target or outside. We advocate the advantages of SDN in the area of defending DDoS attacks, because it is difficult and laborious to organize selfish and undisciplined traditional distributed network to confront well collaborative DDoS flooding attacks.

Pulungan, Farid Fajriana, Sudiharto, Dodi Wisaksono, Brotoharsono, Tri.  2018.  Easy Secure Login Implementation Using Pattern Locking and Environmental Context Recognition. 2018 International Conference on Applied Engineering (ICAE). :1-6.

Smartphone has become the tool which is used daily in modern human life. Some activities in human life, according to the usage of the smartphone can be related to the information which has a high privilege and needs a privacy. It causes the owners of the smartphone needs a system which can protect their privacy. Unfortunately, the secure the system, the unease of the usage. Hence, the system which has an invulnerable environment but also gives the ease of use is very needful. The aspect which is related to the ease of use is an authentication mechanism. Sometimes, this aspect correspondence to the effectiveness and the efficiency. This study is going to analyze the application related to this aspect which is a lock screen application. This lock screen application uses the context data based on the environment condition around the user. The context data used are GPS location and Mac Address of Wi-Fi. The system is going to detect the context and is going to determine if the smartphone needs to run the authentication mechanism or to bypass it based on the analysis of the context data. Hopefully, the smartphone application which is developed still can provide mobility and usability features, and also can protect the user privacy even though it is located in the environment which its context data is unknown.

Gao, Jianbo, Liu, Han, Liu, Chao, Li, Qingshan, Guan, Zhi, Chen, Zhong.  2019.  EasyFlow: keep ethereum away from overflow. Proceedings of the 41st International Conference on Software Engineering: Companion Proceedings. :23–26.
While Ethereum smart contracts enabled a wide range of blockchain applications, they are extremely vulnerable to different forms of security attacks. Due to the fact that transactions to smart contracts commonly involve cryptocurrency transfer, any successful attacks can lead to money loss or even financial disorder. In this paper, we focus on the overflow attacks in Ethereum, mainly because they widely rooted in many smart contracts and comparatively easy to exploit. We have developed EasyFlow, an overflow detector at Ethereum Virtual Machine level. The key insight behind EasyFlow is a taint analysis based tracking technique to analyze the propagation of involved taints. Specifically, EasyFlow can not only divide smart contracts into safe contracts, manifested overflows, well-protected overflows and potential overflows, but also automatically generate transactions to trigger potential overflows. In our preliminary evaluation, EasyFlow managed to find potentially vulnerable Ethereum contracts with little runtime overhead. A demo video of EasyFlow is at
Canetti, Ran, Stoughton, Alley, Varia, Mayank.  2019.  EasyUC: Using EasyCrypt to Mechanize Proofs of Universally Composable Security. 2019 IEEE 32nd Computer Security Foundations Symposium (CSF). :167–16716.

We present a methodology for using the EasyCrypt proof assistant (originally designed for mechanizing the generation of proofs of game-based security of cryptographic schemes and protocols) to mechanize proofs of security of cryptographic protocols within the universally composable (UC) security framework. This allows, for the first time, the mechanization and formal verification of the entire sequence of steps needed for proving simulation-based security in a modular way: Specifying a protocol and the desired ideal functionality; Constructing a simulator and demonstrating its validity, via reduction to hard computational problems; Invoking the universal composition operation and demonstrating that it indeed preserves security. We demonstrate our methodology on a simple example: stating and proving the security of secure message communication via a one-time pad, where the key comes from a Diffie-Hellman key-exchange, assuming ideally authenticated communication. We first put together EasyCrypt-verified proofs that: (a) the Diffie-Hellman protocol UC-realizes an ideal key-exchange functionality, assuming hardness of the Decisional Diffie-Hellman problem, and (b) one-time-pad encryption, with a key obtained using ideal key-exchange, UC-realizes an ideal secure-communication functionality. We then mechanically combine the two proofs into an EasyCrypt-verified proof that the composed protocol realizes the same ideal secure-communication functionality. Although formulating a methodology that is both sound and workable has proven to be a complex task, we are hopeful that it will prove to be the basis for mechanized UC security analyses for significantly more complex protocols and tasks.

Chakraborty, Supriyo, Tripp, Omer.  2016.  Eavesdropping and Obfuscation Techniques for Smartphones. Proceedings of the International Conference on Mobile Software Engineering and Systems. :291–292.

Mobile apps often collect and share personal data with untrustworthy third-party apps, which may lead to data misuse and privacy violations. Most of the collected data originates from sensors built into the mobile device, where some of the sensors are treated as sensitive by the mobile platform while others permit unconditional access. Examples of privacy-prone sensors are the microphone, camera and GPS system. Access to these sensors is always mediated by protected function calls. On the other hand, the light sensor, accelerometer and gyroscope are considered innocuous. All apps have unrestricted access to their data. Unfortunately, this gap is not always justified. State-of-the-art privacy mechanisms on Android provide inadequate access control and do not address the vulnerabilities that arise due to unmediated access to so-called innocuous sensors on smartphones. We have developed techniques to demonstrate these threats. As part of our demonstration, we illustrate possible attacks using the innocuous sensors on the phone. As a solution, we present ipShield, a framework that provides users with greater control over their resources at runtime so as to protect against such attacks. We have implemented ipShield by modifying the AOSP.

Genkin, Daniel, Pachmanov, Lev, Pipman, Itamar, Tromer, Eran, Yarom, Yuval.  2016.  ECDSA Key Extraction from Mobile Devices via Nonintrusive Physical Side Channels. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. :1626–1638.

We show that elliptic-curve cryptography implementations on mobile devices are vulnerable to electromagnetic and power side-channel attacks. We demonstrate full extraction of ECDSA secret signing keys from OpenSSL and CoreBitcoin running on iOS devices, and partial key leakage from OpenSSL running on Android and from iOS's CommonCrypto. These non-intrusive attacks use a simple magnetic probe placed in proximity to the device, or a power probe on the phone's USB cable. They use a bandwidth of merely a few hundred kHz, and can be performed cheaply using an audio card and an improvised magnetic probe.

Zhou, Bing, Lohokare, Jay, Gao, Ruipeng, Ye, Fan.  2018.  EchoPrint: Two-Factor Authentication Using Acoustics and Vision on Smartphones. Proceedings of the 24th Annual International Conference on Mobile Computing and Networking. :321-336.

User authentication on smartphones must satisfy both security and convenience, an inherently difficult balancing art. Apple's FaceID is arguably the latest of such efforts, at the cost of additional hardware (e.g., dot projector, flood illuminator and infrared camera). We propose a novel user authentication system EchoPrint, which leverages acoustics and vision for secure and convenient user authentication, without requiring any special hardware. EchoPrint actively emits almost inaudible acoustic signals from the earpiece speaker to "illuminate" the user's face and authenticates the user by the unique features extracted from the echoes bouncing off the 3D facial contour. To combat changes in phone-holding poses thus echoes, a Convolutional Neural Network (CNN) is trained to extract reliable acoustic features, which are further combined with visual facial landmark locations to feed a binary Support Vector Machine (SVM) classifier for final authentication. Because the echo features depend on 3D facial geometries, EchoPrint is not easily spoofed by images or videos like 2D visual face recognition systems. It needs only commodity hardware, thus avoiding the extra costs of special sensors in solutions like FaceID. Experiments with 62 volunteers and non-human objects such as images, photos, and sculptures show that EchoPrint achieves 93.75% balanced accuracy and 93.50% F-score, while the average precision is 98.05%, and no image/video based attack is observed to succeed in spoofing.

Kauffmann, David, Carmi, Golan.  2017.  E-collaboration of Virtual Teams: The Mediating Effect of Interpersonal Trust. Proceedings of the 2017 International Conference on E-Business and Internet. :45–49.
This study examines the relationship between task communication and relationship communication, and collaboration by exploring the mediating effect of interpersonal trust in a virtual team environment. A theoretical model was developed to examine this relationship where cognitive trust and affective trust are defined as mediation variables between communication and collaboration. The main results of this study show that firstly, there is a significant correlation with a large effect size between communication, trust, and collaboration. Secondly, interpersonal trust plays an important role as a mediator in the relationship between communication and collaboration, especially in relationship communication within virtual teams.
Li, Zhen, Liao, Qi.  2016.  An Economic Alternative to Improve Cybersecurity of E-government and Smart Cities. Proceedings of the 17th International Digital Government Research Conference on Digital Government Research. :455–464.

While the rapid progress in smart city technologies are changing cities and the lifestyle of the people, there are increasingly enormous challenges in terms of the safety and security of smart cities. The potential vulnerabilities of e-government products and imminent attacks on smart city infrastructure and services will have catastrophic consequences on the governments and can cause substantial economic and noneconomic losses, even chaos, to the cities and their residents. This paper aims to explore alternative economic solutions ranging from incentive mechanisms to market-based solutions to motivate smart city product vendors, governments, and vulnerability researchers and finders to improve the cybersecurity of smart cities.

Hafeez, Azeem, Topolovec, Kenneth, Awad, Selim.  2019.  ECU Fingerprinting through Parametric Signal Modeling and Artificial Neural Networks for In-vehicle Security against Spoofing Attacks. 2019 15th International Computer Engineering Conference (ICENCO). :29—38.
Fully connected autonomous vehicles are more vulnerable than ever to hacking and data theft. The controller area network (CAN) protocol is used for communication between in-vehicle control networks (IVN). The absence of basic security features of this protocol, like message authentication, makes it quite vulnerable to a wide range of attacks including spoofing attacks. As traditional cybersecurity methods impose limitations in ensuring confidentiality and integrity of transmitted messages via CAN, a new technique has emerged among others to approve its reliability in fully authenticating the CAN messages. At the physical layer of the communication system, the method of fingerprinting the messages is implemented to link the received signal to the transmitting electronic control unit (ECU). This paper introduces a new method to implement the security of modern electric vehicles. The lumped element model is used to characterize the channel-specific step response. ECU and channel imperfections lead to a unique transfer function for each transmitter. Due to the unique transfer function, the step response for each transmitter is unique. In this paper, we use control system parameters as a feature-set, afterward, a neural network is used transmitting node identification for message authentication. A dataset collected from a CAN network with eight-channel lengths and eight ECUs to evaluate the performance of the suggested method. Detection results show that the proposed method achieves an accuracy of 97.4% of transmitter detection.
Jin, Yier.  2014.  EDA Tools Trust Evaluation Through Security Property Proofs. Proceedings of the Conference on Design, Automation & Test in Europe. :247:1–247:4.

The security concerns of EDA tools have long been ignored because IC designers and integrators only focus on their functionality and performance. This lack of trusted EDA tools hampers hardware security researchers' efforts to design trusted integrated circuits. To address this concern, a novel EDA tools trust evaluation framework has been proposed to ensure the trustworthiness of EDA tools through its functional operation, rather than scrutinizing the software code. As a result, the newly proposed framework lowers the evaluation cost and is a better fit for hardware security researchers. To support the EDA tools evaluation framework, a new gate-level information assurance scheme is developed for security property checking on any gate-level netlist. Helped by the gate-level scheme, we expand the territory of proof-carrying based IP protection from RT-level designs to gate-level netlist, so that most of the commercially trading third-party IP cores are under the protection of proof-carrying based security properties. Using a sample AES encryption core, we successfully prove the trustworthiness of Synopsys Design Compiler in generating a synthesized netlist.

Nawaz, A., Gia, T. N., Queralta, J. Peña, Westerlund, T..  2019.  Edge AI and Blockchain for Privacy-Critical and Data-Sensitive Applications. 2019 Twelfth International Conference on Mobile Computing and Ubiquitous Network (ICMU). :1—2.
The edge and fog computing paradigms enable more responsive and smarter systems without relying on cloud servers for data processing and storage. This reduces network load as well as latency. Nonetheless, the addition of new layers in the network architecture increases the number of security vulnerabilities. In privacy-critical systems, the appearance of new vulnerabilities is more significant. To cope with this issue, we propose and implement an Ethereum Blockchain based architecture with edge artificial intelligence to analyze data at the edge of the network and keep track of the parties that access the results of the analysis, which are stored in distributed databases.
Tedeschi, Pietro, Sciancalepore, Savio.  2019.  Edge and Fog Computing in Critical Infrastructures: Analysis, Security Threats, and Research Challenges. 2019 IEEE European Symposium on Security and Privacy Workshops (EuroS PW). :1–10.

The increasing integration of information and communication technologies has undoubtedly boosted the efficiency of Critical Infrastructures (CI). However, the first wave of IoT devices, together with the management of enormous amount of data generated by modern CIs, has created serious architectural issues. While the emerging Fog and Multi-Access Edge Computing (FMEC) paradigms can provide a viable solution, they also bring inherent security issues, that can cause dire consequences in the context of CIs. In this paper, we analyze the applications of FMEC solutions in the context of CIs, with a specific focus on related security issues and threats for the specific while broad scenarios: a smart airport, a smart port, and a smart offshore oil and gas extraction field. Leveraging these scenarios, a set of general security requirements for FMEC is derived, together with crucial research challenges whose further investigation is cornerstone for a successful adoption of FMEC in CIs.

Rashid, Rasber Dh., Majeed, Taban F..  2019.  Edge Based Image Steganography: Problems and Solution. 2019 International Conference on Communications, Signal Processing, and Their Applications (ICCSPA). :1–5.

Steganography means hiding secrete message in cover object in a way that no suspicious from the attackers, the most popular steganography schemes is image steganography. A very common questions that asked in the field are: 1- what is the embedding scheme used?, 2- where is (location) the secrete messages are embedded?, and 3- how the sender will tell the receiver about the locations of the secrete message?. Here in this paper we are deal with and aimed to answer questions number 2 and 3. We used the popular scheme in image steganography which is least significant bits for embedding in edges positions in color images. After we separate the color images into its components Red, Green, and Blue, then we used one of the components as an index to find the edges, while other one or two components used for embedding purpose. Using this technique we will guarantee the same number and positions of edges before and after embedding scheme, therefore we are guaranteed extracting the secrete message as it's without any loss of secrete messages bits.

R. Mishra, A. Mishra, P. Bhanodiya.  2015.  "An edge based image steganography with compression and encryption". 2015 International Conference on Computer, Communication and Control (IC4). :1-4.

Security of secret data has been a major issue of concern from ancient time. Steganography and cryptography are the two techniques which are used to reduce the security threat. Cryptography is an art of converting secret message in other than human readable form. Steganography is an art of hiding the existence of secret message. These techniques are required to protect the data theft over rapidly growing network. To achieve this there is a need of such a system which is very less susceptible to human visual system. In this paper a new technique is going to be introducing for data transmission over an unsecure channel. In this paper secret data is compressed first using LZW algorithm before embedding it behind any cover media. Data is compressed to reduce its size. After compression data encryption is performed to increase the security. Encryption is performed with the help of a key which make it difficult to get the secret message even if the existence of the secret message is reveled. Now the edge of secret message is detected by using canny edge detector and then embedded secret data is stored there with the help of a hash function. Proposed technique is implemented in MATLAB and key strength of this project is its huge data hiding capacity and least distortion in Stego image. This technique is applied over various images and the results show least distortion in altered image.

Uddin, M. Y. S., Venkatasubramanian, N..  2018.  Edge Caching for Enriched Notifications Delivery in Big Active Data. 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS). :696–705.
In this paper, we propose a set of caching strategies for big active data (BAD) systems. BAD is a data management paradigm that allows ingestion of massive amount of data from heterogeneous sources, such as sensor data, social networks, web and crowdsourced data in a large data cluster consisting of many computing and storage nodes, and enables a very large number of end users to subscribe to those data items through declarative subscriptions. A set of distributed broker nodes connect these end users to the backend data cluster, manage their subscriptions and deliver the subscription results to the end users. Unlike the most traditional publish-subscribe systems that match subscriptions against a single stream of publications to generate notifications, BAD can match subscriptions across multiple publications (by leveraging storage in the backend) and thus can enrich notifications with a rich set of diverse contents. As the matched results are delivered to the end users through the brokers, the broker node caches the results for a while so that the subscribers can retrieve them with reduced latency. Interesting research questions arise in this context so as to determine which result objects to cache or drop when the cache becomes full (eviction-based caching) or to admit objects with an explicit expiration time indicating how much time they should reside in the cache (TTL based caching). To this end, we propose a set of caching strategies for the brokers and show that the schemes achieve varying degree of efficiency in terms of notification delivery in the BAD system. We evaluate our schemes via a prototype implementation and through detailed simulation studies.
ahmad, sahan, Zobaed, SM, Gottumukkala, Raju, Salehi, Mohsen Amini.  2019.  Edge Computing for User-Centric Secure Search on Cloud-Based Encrypted Big Data. 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS). :662–669.

Cloud service providers offer a low-cost and convenient solution to host unstructured data. However, cloud services act as third-party solutions and do not provide control of the data to users. This has raised security and privacy concerns for many organizations (users) with sensitive data to utilize cloud-based solutions. User-side encryption can potentially address these concerns by establishing user-centric cloud services and granting data control to the user. Nonetheless, user-side encryption limits the ability to process (e.g., search) encrypted data on the cloud. Accordingly, in this research, we provide a framework that enables processing (in particular, searching) of encrypted multiorganizational (i.e., multi-source) big data without revealing the data to cloud provider. Our framework leverages locality feature of edge computing to offer a user-centric search ability in a realtime manner. In particular, the edge system intelligently predicts the user's search pattern and prunes the multi-source big data search space to reduce the search time. The pruning system is based on efficient sampling from the clustered big dataset on the cloud. For each cluster, the pruning system dynamically samples appropriate number of terms based on the user's search tendency, so that the cluster is optimally represented. We developed a prototype of a user-centric search system and evaluated it against multiple datasets. Experimental results demonstrate 27% improvement in the pruning quality and search accuracy.