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Conference Paper
Fraile, Francisco, Flores, José Luis, Anaya, Victor, Saiz, Eduardo, Poler, Raúl.  2018.  A Scaffolding Design Framework for Developing Secure Interoperability Components in Digital Manufacturing Platforms. 2018 International Conference on Intelligent Systems (IS). :564—569.
This paper presents the Virtual Open Operating System (vf-OS) Input / Output (IO) Toolkit Generator, which is a design tool to develop vf-OS IO components that interact with all kinds of manufacturing assets, either physical devices like Program Logic Controllers (PLCs), software applications like Enterprise Resource Planning Systems (ERPs) or legacy file formats like STEP. The vf-OS IO Toolkit Generator is based on software scaffolding, a code generation technique that allows a developer to create a working component to interact with a manufacturing asset from the vf-OS Platform without writing a line of code. As described in this paper, software scaffolding not only simplifies the development of interoperability components, but it also fosters system security and platform integration automation. Another contribution of this paper is to propose possible integrations between the IO Toolkit Generator and the vf-OS Security Command Centre in charge of platform security. Additionally, this paper describes how the concept can be extended to address other digital manufacturing platforms like Fi-Ware.
Nugroho, Yeremia Nikanor, Andika, Ferdi, Sari, Riri Fitri.  2019.  Scalability Evaluation of Aspen Tree and Fat Tree Using NS3. 2019 IEEE Conference on Application, Information and Network Security (AINS). :89–93.
When discussing data center networks (DCN), topology has a significant influence on the availability of data to the host. The performance of DCN is relative to the scale of the network. On a particular network scale, it can even cause a connection to the host to be disconnected due to the overhead of routing information. It takes a long time to get connected again so that the data packet that has been sent is lost. The length of time for updating routing information to all parts of the topology so that it can be reconnected or referred to as the time of convergence is the cause. Scalability of a network is proportional to the time of convergence. This article discusses Aspen Tree and Fat Tree, which is about the modification of multi-root trees that have been modified. In Fat Tree, a final set of hosts from a network can be disconnected from a network topology until there is an update of routing information that is disseminated to each switch on the network, due to a link failure. Aspen Tree is a reference topology because it is considered to reduce convergence time and control the overhead of network failure recovery. The DCN topology performance models are implemented using the open source NS-3 platform to support validation of performance evaluations.
Nguyen, Q. L., Sood, A..  2017.  Scalability of Cloud Based SCIT-MTD. 2017 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C). :581–582.

In order to support large volume of transactions and number of users, as estimated by the load demand modeling, a system needs to scale in order to continue to satisfy required quality attributes. In particular, for systems exposed to the Internet, scaling up may increase the attack surface susceptible to malicious intrusions. The new proactive approach based on the concept of Moving Target Defense (MTD) should be considered as a complement to current cybersecurity protection. In this paper, we analyze the scalability of the Self Cleansing Intrusion Tolerance (SCIT) MTD approach using Cloud infrastructure services. By applying the model of MTD with continuous rotation and diversity to a multi-node or multi-instance system, we argue that the effectiveness of the approach is dependent on the share-nothing architecture pattern of the large system. Furthermore, adding more resources to the MTD mechanism can compensate to achieve the desired level of secure availability.

Panetta, J., Filho, P. R. P. S., Laranjeira, L. A. F., Teixeira, C. A..  2017.  Scalability of CPU and GPU Solutions of the Prime Elliptic Curve Discrete Logarithm Problem. 2017 29th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD). :33–40.

Elliptic curve asymmetric cryptography has achieved increased popularity due to its capability of providing comparable levels of security as other existing cryptographic systems while requiring less computational work. Pollard Rho and Parallel Collision Search, the fastest known sequential and parallel algorithms for breaking this cryptographic system, have been successfully applied over time to break ever-increasing bit-length system instances using implementations heavily optimized for the available hardware. This work presents portable, general implementations of a Parallel Collision Search based solution for prime elliptic curve asymmetric cryptographic systems that use publicly available big integer libraries and make no assumption on prime curve properties. It investigates which bit-length keys can be broken in reasonable time by a user that has access to a state of the art, public HPC equipment with CPUs and GPUs. The final implementation breaks a 79-bit system in about two hours using 80 GPUs and 94-bits system in about 15 hours using 256 GPUs. Extensive experimentation investigates scalability of CPU, GPU and CPU+GPU runs. The discussed results indicate that speed-up is not a good metric for parallel scalability. This paper proposes and evaluates a new metric that is better suited for this task.

Zhang, Yunan, Xu, Aidong Xu, Jiang, Yixin.  2020.  Scalable and Accurate Binary Code Search Method Based on Simhash and Partial Trace. 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). :818—826.

Binary code search has received much attention recently due to its impactful applications, e.g., plagiarism detection, malware detection and software vulnerability auditing. However, developing an effective binary code search tool is challenging due to the gigantic syntax and structural differences in binaries resulted from different compilers, compiler options and malware family. In this paper, we propose a scalable and accurate binary search engine which performs syntactic matching by combining a set of key techniques to address the challenges above. The key contribution is binary code searching technique which combined function filtering and partial trace method to match the function code relatively quick and accurate. In addition, a simhash and basic information based function filtering is proposed to dramatically reduce the irrelevant target functions. Besides, we introduce a partial trace method for matching the shortlisted function accurately. The experimental results show that our method can find similar functions, even with the presence of program structure distortion, in a scalable manner.

Roukounaki, Aikaterini, Efremidis, Sofoklis, Soldatos, John, Neises, Juergen, Walloschke, Thomas, Kefalakis, Nikos.  2019.  Scalable and Configurable End-to-End Collection and Analysis of IoT Security Data : Towards End-to-End Security in IoT Systems. 2019 Global IoT Summit (GIoTS). :1–6.

In recent years, there is a surge of interest in approaches pertaining to security issues of Internet of Things deployments and applications that leverage machine learning and deep learning techniques. A key prerequisite for enabling such approaches is the development of scalable infrastructures for collecting and processing security-related datasets from IoT systems and devices. This paper introduces such a scalable and configurable data collection infrastructure for data-driven IoT security. It emphasizes the collection of (security) data from different elements of IoT systems, including individual devices and smart objects, edge nodes, IoT platforms, and entire clouds. The scalability of the introduced infrastructure stems from the integration of state of the art technologies for large scale data collection, streaming and storage, while its configurability relies on an extensible approach to modelling security data from a variety of IoT systems and devices. The approach enables the instantiation and deployment of security data collection systems over complex IoT deployments, which is a foundation for applying effective security analytics algorithms towards identifying threats, vulnerabilities and related attack patterns.

Rashidi, B., Fung, C., Rahman, M..  2018.  A scalable and flexible DDoS mitigation system using network function virtualization. NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium. :1–6.
Distributed Denial of Service (DDoS) attacks remain one of the top threats to enterprise networks and ISPs nowadays. It can cause tremendous damage by bringing down online websites or services. Existing DDoS defense solutions either brings high cost such as upgrading existing firewall or IPS, or bring excessive traffic delay by using third-party cloud-based DDoS filtering services. In this work, we propose a DDoS defense framework that utilizes Network Function Virtualization (NFV) architecture to provide low cost and highly flexible solutions for enterprises. In particular, the system uses virtual network agents to perform attack traffic filtering before they are forwarded to the target server. Agents are created on demand to verify the authenticity of the source of packets, and drop spoofed packets in order protect the target server. Furthermore, we design a scalable and flexible dispatcher to forward packets to corresponding agents for processing. A bucket-based forwarding mechanism is used to improve the scalability of the dispatcher through batching forwarding. The dispatcher can also adapt to agent addition and removal. Our simulation results demonstrate that the dispatcher can effectively serve a large volume of traffic with low dropping rate. The system can successfully mitigate SYN flood attack by introducing minimal performance degradation to legitimate traffic.
Cao, Gang, Chen, Chen, Jiang, Min.  2018.  A Scalable and Flexible Multi-User Semi-Quantum Secret Sharing. Proceedings of the 2Nd International Conference on Telecommunications and Communication Engineering. :28–32.

In this letter, we proposed a novel scheme for the realization of scalable and flexible semi-quantum secret sharing between a boss and multiple dynamic agent groups. In our scheme, the boss Alice can not only distribute her secret messages to multiple users, but also can dynamically adjust the number of users and user groups based on the actual situation. Furthermore, security analysis demonstrates that our protocol is secure against both external attack and participant attack. Compared with previous schemes, our protocol is more flexible and practical. In addition, since our protocol involving only single qubit measurement that greatly weakens the hardware requirements of each user.

Wang, Junjue, Amos, Brandon, Das, Anupam, Pillai, Padmanabhan, Sadeh, Norman, Satyanarayanan, Mahadev.  2017.  A Scalable and Privacy-Aware IoT Service for Live Video Analytics. Proceedings of the 8th ACM on Multimedia Systems Conference. :38–49.

We present OpenFace, our new open-source face recognition system that approaches state-of-the-art accuracy. Integrating OpenFace with inter-frame tracking, we build RTFace, a mechanism for denaturing video streams that selectively blurs faces according to specified policies at full frame rates. This enables privacy management for live video analytics while providing a secure approach for handling retrospective policy exceptions. Finally, we present a scalable, privacy-aware architecture for large camera networks using RTFace.

Tangade, S., Manvi, S. S..  2016.  Scalable and privacy-preserving authentication protocol for secure vehicular communications. 2016 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS). :1–6.

Most of the existing authentication protocols are based on either asymmetric cryptography like public-key infrastructure (PKI) or symmetric cryptography. The PKI-based authentication protocols are strongly recommended for solving security issues in VANETs. However, they have following shortcomings: (1) lengthy certificates lead to transmission and computation overheads, and (2) lack of privacy-preservation due to revealing of vehicle identity, communicated in broadcasting safety-message. Symmetric cryptography based protocols are faster because of a single secret key and simplicity; however, it does not ensure non-repudiation. In this paper, we present an Efficient, Scalable and Privacy-preserving Authentication (ESPA) protocol for secure vehicular ad hoc networks (VANETs). The protocol employs hybrid cryptography. In ESPA, the asymmetric PKI based pre-authentication and the symmetric hash message authentication code (HMAC) based authentication are adopted during vehicle to infrastructure (V2I) and vehicle to vehicle (V2V) communications, respectively. Extensive simulations are conducted to validate proposed ESPA protocol and compared with the existing work based on PKI and HMAC. The performance analysis showed that ESPA is more efficient, scalable and privacy-preserving secured protocol than the existing work.

Lokananta, F., Hartono, D., Tang, C. M..  2017.  A Scalable and Reconfigurable Verification and Benchmark Environment for Network on Chip Architecture. 2017 4th International Conference on New Media Studies (CONMEDIA). :6–10.

To reduce the complex communication problem that arise as the number of on-chip component increases, the use of Network-on-Chip (NoC) as interconnection architectures have become more promising to solve complex on-chip communication problems. However, providing a suitable test base to measure and verify functionality of any NoC is a compulsory. Universal Verification Methodology (UVM) is introduced as a standardized and reusable methodology for verifying integrated circuit design. In this research, a scalable and reconfigurable verification and benchmark environment for NoC is proposed.

Lim, K., Tuladhar, K. M., Wang, X., Liu, W..  2017.  A scalable and secure key distribution scheme for group signature based authentication in VANET. 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON). :478–483.

Security issues in vehicular communication have become a huge concern to safeguard increasing applications. A group signature is one of the popular authentication approaches for VANETs (Vehicular ad hoc networks) which can be implemented to secure the vehicular communication. However, securely distributing group keys to fast-moving vehicular nodes is still a challenging problem. In this paper, we propose an efficient key management protocol for group signature based authentication, where a group is extended to a domain with multiple road side units. Our scheme not only provides a secure way to deliver group keys to vehicular nodes, but also ensures security features. The experiment results show that our key distribution scheme is a scalable, efficient and secure solution to vehicular networking.

Aono, Yoshinori, Hayashi, Takuya, Trieu Phong, Le, Wang, Lihua.  2016.  Scalable and Secure Logistic Regression via Homomorphic Encryption. Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy. :142–144.

Logistic regression is a powerful machine learning tool to classify data. When dealing with sensitive data such as private or medical information, cares are necessary. In this paper, we propose a secure system for protecting the training data in logistic regression via homomorphic encryption. Perhaps surprisingly, despite the non-polynomial tasks of training in logistic regression, we show that only additively homomorphic encryption is needed to build our system. Our system is secure and scalable with the dataset size.

Cook, Kyle, Shaw, Thomas, Hawrylak, Peter, Hale, John.  2016.  Scalable Attack Graph Generation. Proceedings of the 11th Annual Cyber and Information Security Research Conference. :21:1–21:4.

Attack graphs are a powerful modeling technique with which to explore the attack surface of a system. However, they can be difficult to generate due to the exponential growth of the state space, often times making exhaustive search impractical. This paper discusses an approach for generating large attack graphs with an emphasis on scalable generation over a distributed system. First, a serial algorithm is presented, highlighting bottlenecks and opportunities to exploit inherent concurrency in the generation process. Then a strategy to parallelize this process is presented. Finally, we discuss plans for future work to implement the parallel algorithm using a hybrid distributed/shared memory programming model on a heterogeneous compute node cluster.

Mendiboure, L., Chalouf, M. A., Krief, F..  2020.  A Scalable Blockchain-based Approach for Authentication and Access Control in Software Defined Vehicular Networks. 2020 29th International Conference on Computer Communications and Networks (ICCCN). :1—11.
Software Defined Vehicular Networking (SDVN) could be the future of the vehicular networks, enabling interoperability between heterogeneous networks and mobility management. Thus, the deployment of large SDVN is considered. However, SDVN is facing major security issues, in particular, authentication and access control issues. Indeed, an unauthorized SDN controller could modify the behavior of switches (packet redirection, packet drops) and an unauthorized switch could disrupt the operation of the network (reconnaissance attack, malicious feedback). Due to the SDVN features (decentralization, mobility) and the SDVN requirements (flexibility, scalability), the Blockchain technology appears to be an efficient way to solve these authentication and access control issues. Therefore, many Blockchain-based approaches have already been proposed. However, two key challenges have not been addressed: authentication and access control for SDN controllers and high scalability for the underlying Blockchain network. That is why in this paper we propose an innovative and scalable architecture, based on a set of interconnected Blockchain sub-networks. Moreover, an efficient access control mechanism and a cross-sub-networks authentication/revocation mechanism are proposed for all SDVN devices (vehicles, roadside equipment, SDN controllers). To demonstrate the benefits of our approach, its performances are compared with existing solutions in terms of throughput, latency, CPU usage and read/write access to the Blockchain ledger. In addition, we determine an optimal number of Blockchain sub-networks according to different parameters such as the number of certificates to store and the number of requests to process.
Block, Matthew, Barcaskey, Benjamin, Nimmo, Andrew, Alnaeli, Saleh, Gilbert, Ian, Altahat, Zaid.  2020.  Scalable Cloud-Based Tool to Empirically Detect Vulnerable Code Patterns in Large-Scale System. 2020 IEEE International Conference on Electro Information Technology (EIT). :588—592.
Open-source development is a well-accepted model by software development communities from both academia and industry. Many companies and corporations adopt and use open source systems daily as a core component in their business activities. One of the most important factors that will determine the success of this model is security. The security of software systems is a combination of source code quality, stability, and vulnerabilities. Software vulnerabilities can be introduced by many factors, some of which are the way that programmers write their programs, their background on security standards, and safe programming practices. This paper describes a cloud-based software tool developed by the authors that can help our computing communities in both academia and research to evaluate their software systems on the source code level to help them identify and detect some of the well-known source code vulnerability patterns that can cause security issues if maliciously exploited. The paper also presents an empirical study on the prevalence of vulnerable C/C++ coding patterns inside three large-scale open-source systems comprising more than 42 million lines of source code. The historical data for the studied systems is presented over five years to uncover some historical trends to highlight the changes in the system analyzed over time concerning the presence of some of the source code vulnerabilities patterns. The majority of results show the continued usage of known unsafe functions.
K. F. Hong, C. C. Chen, Y. T. Chiu, K. S. Chou.  2015.  "Scalable command and control detection in log data through UF-ICF analysis". 2015 International Carnahan Conference on Security Technology (ICCST). :293-298.

During an advanced persistent threat (APT), an attacker group usually establish more than one C&C server and these C&C servers will change their domain names and corresponding IP addresses over time to be unseen by anti-virus software or intrusion prevention systems. For this reason, discovering and catching C&C sites becomes a big challenge in information security. Based on our observations and deductions, a malware tends to contain a fixed user agent string, and the connection behaviors generated by a malware is different from that by a benign service or a normal user. This paper proposed a new method comprising filtering and clustering methods to detect C&C servers with a relatively higher coverage rate. The experiments revealed that the proposed method can successfully detect C&C Servers, and the can provide an important clue for detecting APT.

George, Chinnu Mary, Luke Babu, Sharon.  2019.  A Scalable Correlation Clustering strategy in Location Privacy for Wireless Sensor Networks against a Universal Adversary. 2019 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE). :1–3.
Wireless network sensors are outsized number of pocket sized sensors deployed in the area under surveillance. The sensor network is very sensitive to unattended and remote Environment with a wide variety of applications in the agriculture, health, industry there a lot of challenges being faced with respect to the energy, mobility, security. The paper presents with regard to the context based surrounding information which has location privacy to the source node against an adversary who sees the network at a whole so a correlation strategy is proposed for providing the privacy.
Ślezak, D., Chadzyńska-Krasowska, A., Holland, J., Synak, P., Glick, R., Perkowski, M..  2017.  Scalable cyber-security analytics with a new summary-based approximate query engine. 2017 IEEE International Conference on Big Data (Big Data). :1840–1849.

A growing need for scalable solutions for both machine learning and interactive analytics exists in the area of cyber-security. Machine learning aims at segmentation and classification of log events, which leads towards optimization of the threat monitoring processes. The tools for interactive analytics are required to resolve the uncertain cases, whereby machine learning algorithms are not able to provide a convincing outcome and human expertise is necessary. In this paper we focus on a case study of a security operations platform, whereby typical layers of information processing are integrated with a new database engine dedicated to approximate analytics. The engine makes it possible for the security experts to query massive log event data sets in a standard relational style. The query outputs are received orders of magnitude faster than any of the existing database solutions running with comparable resources and, in addition, they are sufficiently accurate to make the right decisions about suspicious corner cases. The engine internals are driven by the principles of information granulation and summary-based processing. They also refer to the ideas of data quantization, approximate computing, rough sets and probability propagation. In the paper we study how the engine's parameters can influence its performance within the considered environment. In addition to the results of experiments conducted on large data sets, we also discuss some of our high level design decisions including the choice of an approximate query result accuracy measure that should reflect the specifics of the considered threat monitoring operations.

Blazek, Petr, Gerlich, Tomas, Martinasek, Zdenek.  2019.  Scalable DDoS Mitigation System. 2019 42nd International Conference on Telecommunications and Signal Processing (TSP). :617–620.
Distributed Denial of Service attacks (DDoS) are used by attackers for their effectiveness. This type of attack is one of the most devastating attacks in the Internet. Every year, the intensity of DDoS attacks increases and attackers use sophisticated multi-target DDoS attacks. In this paper, a modular system that allows to increase the filtering capacity linearly and allows to protect against the combination of DDoS attacks is designed and implemented. The main motivation for development of the modular filtering system was to find a cheap solution for filtering DDoS attacks with possibility to increase filtering capacity. The proposed system is based on open-source detection and filtration tools.
Nguyen-Van, Thanh, Nguyen-Anh, Tuan, Le, Tien-Dat, Nguyen-Ho, Minh-Phuoc, Nguyen-Van, Tuong, Le, Nhat-Quang, Nguyen-An, Khuong.  2019.  Scalable Distributed Random Number Generation Based on Homomorphic Encryption. 2019 IEEE International Conference on Blockchain (Blockchain). :572–579.

Generating a secure source of publicly-verifiable randomness could be the single most fundamental technical challenge on a distributed network, especially in the blockchain context. Many current proposals face serious problems of scalability and security issues. We present a protocol which can be implemented on a blockchain that ensures unpredictable, tamper-resistant, scalable and publicly-verifiable outcomes. The main building blocks of our protocol are homomorphic encryption (HE) and verifiable random functions (VRF). The use of homomorphic encryption enables mathematical operations to be performed on encrypted data, to ensure no one knows the outcome prior to being generated. The protocol requires O(n) elliptic curve multiplications and additions as well as O(n) signature signing and verification operations, which permits great scalability. We present a comparison between recent approaches to the generation of random beacons.

Aslanyan, H., Avetisyan, A., Arutunian, M., Keropyan, G., Kurmangaleev, S., Vardanyan, V..  2017.  Scalable Framework for Accurate Binary Code Comparison. 2017 Ivannikov ISPRAS Open Conference (ISPRAS). :34–38.
Comparison of two binary files has many practical applications: the ability to detect programmatic changes between two versions, the ability to find old versions of statically linked libraries to prevent the use of well-known bugs, malware analysis, etc. In this article, a framework for comparison of binary files is presented. Framework uses IdaPro [1] disassembler and Binnavi [2] platform to recover structure of the target program and represent it as a call graph (CG). A program dependence graph (PDG) corresponds to each vertex of the CG. The proposed comparison algorithm consists of two main stages. At the first stage, several heuristics are applied to find the exact matches. Two functions are matched if at least one of the calculated heuristics is the same and unique in both binaries. At the second stage, backward and forward slicing is applied on matched vertices of CG to find further matches. According to empiric results heuristic method is effective and has high matching quality for unchanged or slightly modified functions. As a contradiction, to match heavily modified functions, binary code clone detection is used and it is based on finding maximum common subgraph for pair of PDGs. To achieve high performance on extensive binaries, the whole matching process is parallelized. The framework is tested on the number of real world libraries, such as python, openssh, openssl, libxml2, rsync, php, etc. Results show that in most cases more than 95% functions are truly matched. The tool is scalable due to parallelization of functions matching process and generation of PDGs and CGs.
Hassen, Mehadi, Chan, Philip K..  2017.  Scalable Function Call Graph-based Malware Classification. Proceedings of the Seventh ACM on Conference on Data and Application Security and Privacy. :239–248.
In an attempt to preserve the structural information in malware binaries during feature extraction, function call graph-based features have been used in various research works in malware classification. However, the approach usually employed when performing classification on these graphs, is based on computing graph similarity using computationally intensive techniques. Due to this, much of the previous work in this area incurred large performance overhead and does not scale well. In this paper, we propose a linear time function call graph (FCG) vector representation based on function clustering that has significant performance gains in addition to improved classification accuracy. We also show how this representation can enable using graph features together with other non-graph features.
Feng, Qian, Zhou, Rundong, Xu, Chengcheng, Cheng, Yao, Testa, Brian, Yin, Heng.  2016.  Scalable Graph-based Bug Search for Firmware Images. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. :480–491.

Because of rampant security breaches in IoT devices, searching vulnerabilities in massive IoT ecosystems is more crucial than ever. Recent studies have demonstrated that control-flow graph (CFG) based bug search techniques can be effective and accurate in IoT devices across different architectures. However, these CFG-based bug search approaches are far from being scalable to handle an enormous amount of IoT devices in the wild, due to their expensive graph matching overhead. Inspired by rich experience in image and video search, we propose a new bug search scheme which addresses the scalability challenge in existing cross-platform bug search techniques and further improves search accuracy. Unlike existing techniques that directly conduct searches based upon raw features (CFGs) from the binary code, we convert the CFGs into high-level numeric feature vectors. Compared with the CFG feature, high-level numeric feature vectors are more robust to code variation across different architectures, and can easily achieve realtime search by using state-of-the-art hashing techniques. We have implemented a bug search engine, Genius, and compared it with state-of-art bug search approaches. Experimental results show that Genius outperforms baseline approaches for various query loads in terms of speed and accuracy. We also evaluated Genius on a real-world dataset of 33,045 devices which was collected from public sources and our system. The experiment showed that Genius can finish a search within 1 second on average when performed over 8,126 firmware images of 420,558,702 functions. By only looking at the top 50 candidates in the search result, we found 38 potentially vulnerable firmware images across 5 vendors, and confirmed 23 of them by our manual analysis. We also found that it took only 0.1 seconds on average to finish searching for all 154 vulnerabilities in two latest commercial firmware images from D-LINK. 103 of them are potentially vulnerable in these images, and 16 of them were confirmed.

Mattmann, Chris A., Sharan, Madhav.  2017.  Scalable Hadoop-Based Pooled Time Series of Big Video Data from the Deep Web. Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval. :117–120.

We contribute a scalable, open source implementation of the Pooled Time Series (PoT) algorithm from CVPR 2015. The algorithm is evaluated on approximately 6800 human trafficking (HT) videos collected from the deep and dark web, and on an open dataset: the Human Motion Database (HMDB). We describe PoT and our motivation for using it on larger data and the issues we encountered. Our new solution reimagines PoT as an Apache Hadoop-based algorithm. We demonstrate that our new Hadoop-based algorithm successfully identifies similar videos in the HT and HMDB datasets and we evaluate the algorithm qualitatively and quantitatively.