Visible to the public Biblio

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2021-04-09
Bhattacharya, M. P., Zavarsky, P., Butakov, S..  2020.  Enhancing the Security and Privacy of Self-Sovereign Identities on Hyperledger Indy Blockchain. 2020 International Symposium on Networks, Computers and Communications (ISNCC). :1—7.
Self-sovereign identities provide user autonomy and immutability to individual identities and full control to their identity owners. The immutability and control are possible by implementing identities in a decentralized manner on blockchains that are specially designed for identity operations such as Hyperledger Indy. As with any type of identity, self-sovereign identities too deal with Personally Identifiable Information (PII) of the identity holders and comes with the usual risks of privacy and security. This study examined certain scenarios of personal data disclosure via credential exchanges between such identities and risks of man-in-the-middle attacks in the blockchain based identity system Hyperledger Indy. On the basis of the findings, the paper proposes the following enhancements: 1) A novel attribute sensitivity score model for self-sovereign identity agents to ascertain the sensitivity of attributes shared in credential exchanges 2) A method of mitigating man-in-the-middle attacks between peer self-sovereign identities and 3) A novel quantitative model for determining a credential issuer's reputation based on the number of issued credentials in a window period, which is then utilized to calculate an overall confidence level score for the issuer.
2021-03-29
Malek, Z. S., Trivedi, B., Shah, A..  2020.  User behavior Pattern -Signature based Intrusion Detection. 2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4). :549—552.

Technology advancement also increases the risk of a computer's security. As we can have various mechanisms to ensure safety but still there have flaws. The main concerned area is user authentication. For authentication, various biometric applications are used but once authentication is done in the begging there was no guarantee that the computer system is used by the authentic user or not. The intrusion detection system (IDS) is a particular procedure that is used to identify intruders by analyzing user behavior in the system after the user logged in. Host-based IDS monitors user behavior in the computer and identify user suspicious behavior as an intrusion or normal behavior. This paper discusses how an expert system detects intrusions using a set of rules as a pattern recognized engine. We propose a PIDE (Pattern Based Intrusion Detection) model, which is verified previously implemented SBID (Statistical Based Intrusion Detection) model. Experiment results indicate that integration of SBID and PBID approach provides an extensive system to detect intrusion.

Pieper, P., Herdt, V., Große, D., Drechsler, R..  2020.  Dynamic Information Flow Tracking for Embedded Binaries using SystemC-based Virtual Prototypes. 2020 57th ACM/IEEE Design Automation Conference (DAC). :1—6.

Avoiding security vulnerabilities is very important for embedded systems. Dynamic Information Flow Tracking (DIFT) is a powerful technique to analyze SW with respect to security policies in order to protect the system against a broad range of security related exploits. However, existing DIFT approaches either do not exist for Virtual Prototypes (VPs) or fail to model complex hardware/software interactions.In this paper, we present a novel approach that enables early and accurate DIFT of binaries targeting embedded systems with custom peripherals. Leveraging the SystemC framework, our DIFT engine tracks accurate data flow information alongside the program execution to detect violations of security policies at run-time. We demonstrate the effectiveness and applicability of our approach by extensive experiments.

2021-01-11
Chekashev, A., Demianiuk, V., Kogan, K..  2020.  Poster: Novel Opportunities in Design of Efficient Deep Packet Inspection Engines. 2020 IEEE 28th International Conference on Network Protocols (ICNP). :1–2.
Deep Packet Inspection (DPI) is an essential building block implementing various services on data plane [5]. Usually, DPI engines are centered around efficient implementation of regular expressions both from the required memory and lookup time perspectives. In this paper, we explore and generalize original approaches used for packet classifiers [7] to regular expressions. Our preliminary results establish a promising direction for the efficient implementation of DPI engines.
Papadogiannaki, E., Deyannis, D., Ioannidis, S..  2020.  Head(er)Hunter: Fast Intrusion Detection using Packet Metadata Signatures. 2020 IEEE 25th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD). :1–6.
More than 75% of the Internet traffic is now encrypted, while this percentage is constantly increasing. The majority of communications are secured using common encryption protocols such as SSL/TLS and IPsec to ensure security and protect the privacy of Internet users. Yet, encryption can be exploited to hide malicious activities. Traditionally, network traffic inspection is based on techniques like deep packet inspection (DPI). Common applications for DPI include but are not limited to firewalls, intrusion detection and prevention systems, L7 filtering and packet forwarding. The core functionality of such DPI implementations is based on pattern matching that enables searching for specific strings or regular expressions inside the packet contents. With the widespread adoption of network encryption though, DPI tools that rely on packet payload content are becoming less effective, demanding the development of more sophisticated techniques in order to adapt to current network encryption trends. In this work, we present HeaderHunter, a fast signature-based intrusion detection system even in encrypted network traffic. We generate signatures using only network packet metadata extracted from packet headers. Also, to cope with the ever increasing network speeds, we accelerate the inner computations of our proposed system using off-the-shelf GPUs.
2020-11-20
Moghaddam, F. F., Wieder, P., Yahyapour, R., Khodadadi, T..  2018.  A Reliable Ring Analysis Engine for Establishment of Multi-Level Security Management in Clouds. 2018 41st International Conference on Telecommunications and Signal Processing (TSP). :1—5.
Security and Privacy challenges are the most obstacles for the advancement of cloud computing and the erosion of trust boundaries already happening in organizations is amplified and accelerated by this emerging technology. Policy Management Frameworks are the most proper solutions to create dedicated security levels based on the sensitivity of resources and according to the mapping process between requirements cloud customers and capabilities of service providers. The most concerning issue in these frameworks is the rate of perfect matches between capabilities and requirements. In this paper, a reliable ring analysis engine has been introduced to efficiently map the security requirements of cloud customers to the capabilities of service provider and to enhance the rate of perfect matches between them for establishment of different security levels in clouds. In the suggested model a structural index has been introduced to receive the requirement and efficiently map them to the most proper security mechanism of the service provider. Our results show that this index-based engine enhances the rate of perfect matches considerably and decreases the detected conflicts in syntactic and semantic analysis.
2020-11-04
Khurana, N., Mittal, S., Piplai, A., Joshi, A..  2019.  Preventing Poisoning Attacks On AI Based Threat Intelligence Systems. 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP). :1—6.

As AI systems become more ubiquitous, securing them becomes an emerging challenge. Over the years, with the surge in online social media use and the data available for analysis, AI systems have been built to extract, represent and use this information. The credibility of this information extracted from open sources, however, can often be questionable. Malicious or incorrect information can cause a loss of money, reputation, and resources; and in certain situations, pose a threat to human life. In this paper, we use an ensembled semi-supervised approach to determine the credibility of Reddit posts by estimating their reputation score to ensure the validity of information ingested by AI systems. We demonstrate our approach in the cybersecurity domain, where security analysts utilize these systems to determine possible threats by analyzing the data scattered on social media websites, forums, blogs, etc.

2020-10-05
Zhou, Ziqiang, Sun, Changhua, Lu, Jiazhong, Lv, Fengmao.  2018.  Research and Implementation of Mobile Application Security Detection Combining Static and Dynamic. 2018 10th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA). :243–247.
With the popularity of the Internet and mobile intelligent terminals, the number of mobile applications is exploding. Mobile intelligent terminals trend to be the mainstream way of people's work and daily life online in place of PC terminals. Mobile application system brings some security problems inevitably while it provides convenience for people, and becomes a main target of hackers. Therefore, it is imminent to strengthen the security detection of mobile applications. This paper divides mobile application security detection into client security detection and server security detection. We propose a combining static and dynamic security detection method to detect client-side. We provide a method to get network information of server by capturing and analyzing mobile application traffic, and propose a fuzzy testing method based on HTTP protocol to detect server-side security vulnerabilities. Finally, on the basis of this, an automated platform for security detection of mobile application system is developed. Experiments show that the platform can detect the vulnerabilities of mobile application client and server effectively, and realize the automation of mobile application security detection. It can also reduce the cost of mobile security detection and enhance the security of mobile applications.
2020-09-21
Zhang, Bing, Zhao, Yongli, Yan, Boyuan, Yan, Longchuan, WANG, YING, Zhang, Jie.  2019.  Failure Disposal by Interaction of the Cross-Layer Artificial Intelligence on ONOS-Based SDON Platform. 2019 Optical Fiber Communications Conference and Exhibition (OFC). :1–3.
We propose a new architecture introducing AI to span the control layer and the data layer in SDON. This demonstration shows the cooperation of the AI engines in two layers in dealing with failure disposal.
2020-09-08
Campioni, Lorenzo, Tortonesi, Mauro, Wissingh, Bastiaan, Suri, Niranjan, Hauge, Mariann, Landmark, Lars.  2019.  Experimental Evaluation of Named Data Networking (NDN) in Tactical Environments. MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM). :43–48.
Tactical edge networks represent a uniquely challenging environment from the communications perspective, due to their limited bandwidth and high node mobility. Several middleware communication solutions have been proposed to address those issues, adopting an evolutionary design approach that requires facing quite a few complications to provide applications with a suited network programming model while building on top of the TCP/IP stack. Information Centric Networking (ICN), instead, represents a revolutionary, clean slate approach that aims at replacing the entire TCP/IP stack with a new communication paradigm, better suited to cope with fluctuating channel conditions and network disruptions. This paper, stemmed from research conducted within NATO IST-161 RTG, investigates the effectiveness of Named Data Networking (NDN), the de facto standard implementation of ICN, in the context of tactical edge networks and its potential for adoption. We evaluated an NDN-based Blue Force Tracking (BFT) dissemination application within the Anglova scenario emulation environment, and found that NDN obtained better-than-expected results in terms of delivery ratio and latency, at the expense of a relatively high bandwidth consumption.
2020-09-04
Velan, Petr, Husák, Martin, Tovarňák, Daniel.  2018.  Rapid prototyping of flow-based detection methods using complex event processing. NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium. :1—3.
Detection of network attacks is the first step to network security. Many different methods for attack detection were proposed in the past. However, descriptions of these methods are often not complete and it is difficult to verify that the actual implementation matches the description. In this demo paper, we propose to use Complex Event Processing (CEP) for developing detection methods based on network flows. By writing the detection methods in an Event Processing Language (EPL), we can address the above-mentioned problems. The SQL-like syntax of most EPLs is easily readable so the detection method is self-documented. Moreover, it is directly executable in the CEP system, which eliminates inconsistencies between documentation and implementation. The demo will show a running example of a multi-stage HTTP brute force attack detection using Esper and its EPL.
Zhang, Xiao, Wang, Yanqiu, Wang, Qing, Zhao, Xiaonan.  2019.  A New Approach to Double I/O Performance for Ceph Distributed File System in Cloud Computing. 2019 2nd International Conference on Data Intelligence and Security (ICDIS). :68—75.
Block storage resources are essential in an Infrastructure-as-a-Service(IaaS) cloud computing system. It is used for storing virtual machines' images. It offers persistent storage service even the virtual machine is off. Distribute storage systems are used to provide block storage services in IaaS, such as Amazon EBS, Cinder, Ceph, Sheepdog. Ceph is widely used as the backend block storage service of OpenStack platform. It converts block devices into objects with the same size and saves them on the local file system. The performance of block devices provided by Ceph is only 30% of hard disks in many cases. One of the key issues that affect the performance of Ceph is the three replicas for fault tolerance. But our research finds that replicas are not the real reason slow down the performance. In this paper, we present a new approach to accelerate the IO operations. The experiment results show that by using our storage engine, Ceph can offer faster IO performance than the hard disk in most cases. Our new storage engine provides more than three times up than the original one.
2020-08-28
Dauenhauer, Ralf, Müller, Tobias.  2016.  An Evaluation of Information Connection in Augmented Reality for 3D Scenes with Occlusion. 2016 IEEE International Symposium on Mixed and Augmented Reality (ISMAR-Adjunct). :235—237.
Most augmented reality applications connect virtual information to anchors, i.e. physical places or objects, by using spatial overlays or proximity. However, for industrial use cases this is not always feasible because specific parts must remain fully visible in order to meet work or security requirements. In these situations virtual information must be displayed at alternative positions while connections to anchors must still be clearly recognizable. In our previous research we were the first to show that for simple scenes connection lines are most suitable for this. To extend these results to more complex environments, we conducted an experiment on the effects of visual interruptions in connection lines and incorrect occlusion. Completion time and subjective mental effort for search tasks were used as measures. Our findings confirm that also in 3D scenes with partial occlusion connection lines are preferable to connect virtual information with anchors if an assignment via overlay or close proximity is not feasible. The results further imply that neither incorrectly used depth cues nor missing parts of connection lines make a significant difference concerning completion time or subjective mental effort. For designers of industrial augmented reality applications this means that they can choose either visualization based on their needs.
Brewer, John N., Dimitoglou, George.  2019.  Evaluation of Attack Vectors and Risks in Automobiles and Road Infrastructure. 2019 International Conference on Computational Science and Computational Intelligence (CSCI). :84—89.

The evolution of smart automobiles and vehicles within the Internet of Things (IoT) - particularly as that evolution leads toward a proliferation of completely autonomous vehicles - has sparked considerable interest in the subject of vehicle/automotive security. While the attack surface is wide, there are patterns of exploitable vulnerabilities. In this study we reviewed, classified according to their attack surface and evaluated some of the common vehicle and infrastructure attack vectors identified in the literature. To remediate these attack vectors, specific technical recommendations have been provided as a way towards secure deployments of smart automobiles and transportation infrastructures.

2020-08-14
Gu, Zuxing, Zhou, Min, Wu, Jiecheng, Jiang, Yu, Liu, Jiaxiang, Gu, Ming.  2019.  IMSpec: An Extensible Approach to Exploring the Incorrect Usage of APIs. 2019 International Symposium on Theoretical Aspects of Software Engineering (TASE). :216—223.
Application Programming Interfaces (APIs) usually have usage constraints, such as call conditions or call orders. Incorrect usage of these constraints, called API misuse, will result in system crashes, bugs, and even security problems. It is crucial to detect such misuses early in the development process. Though many approaches have been proposed over the last years, recent studies show that API misuses are still prevalent, especially the ones specific to individual projects. In this paper, we strive to improve current API-misuse detection capability for large-scale C programs. First, We propose IMSpec, a lightweight domain-specific language enabling developers to specify API usage constraints in three different aspects (i.e., parameter validation, error handling, and causal calling), which are the majority of API-misuse bugs. Then, we have tailored a constraint guided static analysis engine to automatically parse IMSpec rules and detect API-misuse bugs with rich semantics. We evaluate our approach on widely used benchmarks and real-world projects. The results show that our easily extensible approach performs better than state-of-the-art tools. We also discover 19 previously unknown bugs in real-world open-source projects, all of which have been confirmed by the corresponding developers.
Walla, Sebastian, Rossow, Christian.  2019.  MALPITY: Automatic Identification and Exploitation of Tarpit Vulnerabilities in Malware. 2019 IEEE European Symposium on Security and Privacy (EuroS P). :590—605.
Law enforcement agencies regularly take down botnets as the ultimate defense against global malware operations. By arresting malware authors, and simultaneously infiltrating or shutting down a botnet's network infrastructures (such as C2 servers), defenders stop global threats and mitigate pending infections. In this paper, we propose malware tarpits, an orthogonal defense that does not require seizing botnet infrastructures, and at the same time can also be used to slow down malware spreading and infiltrate its monetization techniques. A tarpit is a network service that causes a client to stay busy with a network operation. Our work aims to automatically identify network operations used by malware that will block the malware either forever or for a significant amount of time. We describe how to non-intrusively exploit such tarpit vulnerabilities in malware to slow down or, ideally, even stop malware. Using dynamic malware analysis, we monitor how malware interacts with the POSIX and Winsock socket APIs. From this, we infer network operations that would have blocked when provided certain network inputs. We augment this vulnerability search with an automated generation of tarpits that exploit the identified vulnerabilities. We apply our prototype MALPITY on six popular malware families and discover 12 previously-unknown tarpit vulnerabilities, revealing that all families are susceptible to our defense. We demonstrate how to, e.g., halt Pushdo's DGA-based C2 communication, hinder SalityP2P peers from receiving commands or updates, and stop Bashlite's spreading engine.
2020-07-27
Dangiwa, Bello Ahmed, Kumar, Smitha S.  2018.  A Business Card Reader Application for iOS devices based on Tesseract. 2018 International Conference on Signal Processing and Information Security (ICSPIS). :1–4.
As the accessibility of high-resolution smartphone camera has increased and an improved computational speed, it is now convenient to build Business Card Readers on mobile phones. The project aims to design and develop a Business Card Reader (BCR) Application for iOS devices, using an open-source OCR Engine - Tesseract. The system accuracy was tested and evaluated using a dataset of 55 digital business cards obtained from an online repository. The accuracy result of the system was up to 74% in terms of both text recognition and data detection. A comparative analysis was carried out against a commercial business card reader application and our application performed vastly reasonable.
Liu, Xianyu, Zheng, Min, Pan, Aimin, Lu, Quan.  2018.  Hardening the Core: Understanding and Detection of XNU Kernel Vulnerabilities. 2018 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W). :10–13.
The occurrence of security vulnerabilities in kernel, especially for macOS/iOS kernel XNU, has increased rapidly in recent years. Naturally, concerns were raised due to the high risks they would lead to, which in general are much more serious than common application vulnerabilities. However, discovering XNU kernel vulnerabilities is always very challenging, and the main approach in practice is still manual analysis, which obviously is not a scalable method. In this paper, we perform an in-depth empirical study on the 406 published XNU kernel vulnerabilities to identify distinguishing characteristics of them and then leverage the features to guide our vulnerability detection, i.e., locating suspicious functions. To further improve the efficiency of vulnerability detection, we present KInspector, a new and lightweight framework to detect XNU kernel vulnerabilities by leveraging feedback-based fuzzing techniques. We thoroughly evaluate our approach on XNU with various versions, and the results turn out to be quite promising: 21 N/0-day vulnerabilities have been discovered in our experiments.
2020-07-20
Urien, Pascal.  2019.  Designing Attacks Against Automotive Control Area Network Bus and Electronic Control Units. 2019 16th IEEE Annual Consumer Communications Networking Conference (CCNC). :1–4.
Security is a critical issue for new car generation targeting intelligent transportation systems (ITS), involving autonomous and connected vehicles. In this work we designed a low cost CAN probe and defined analysis tools in order to build attack scenarios. We reuse some threats identified by a previous work. Future researches will address new security protocols.
2020-05-18
Gou, Linfeng, Zhou, Zihan, Liang, Aixia, Wang, Lulu, Liu, Zhidan.  2018.  Dynamic Threshold Design Based on Kalman Filter in Multiple Fault Diagnosis. 2018 37th Chinese Control Conference (CCC). :6105–6109.
The choice of threshold is an important part of fault diagnosis. Most of the current methods use a constant threshold for detection and it is difficult to meet the robustness and sensitivity requirements of the diagnosis system. This article develops a dynamic threshold algorithm for aircraft engine fault detection and isolation systems. The algorithm firstly analyzes the bounded norm uncertainty that may appear in the process of model based on the state space equation, and gives the time domain response range calculation formula under the influence of uncertain parameters; then the Kalman filter is combined to calculate the threshold with the real-time change of state; the simulation is performed at the end. The simulation results show that dynamic threshold range changes with status in real time.
2020-04-20
Huang, Zhen, Lie, David, Tan, Gang, Jaeger, Trent.  2019.  Using Safety Properties to Generate Vulnerability Patches. 2019 IEEE Symposium on Security and Privacy (SP). :539–554.
Security vulnerabilities are among the most critical software defects in existence. When identified, programmers aim to produce patches that prevent the vulnerability as quickly as possible, motivating the need for automatic program repair (APR) methods to generate patches automatically. Unfortunately, most current APR methods fall short because they approximate the properties necessary to prevent the vulnerability using examples. Approximations result in patches that either do not fix the vulnerability comprehensively, or may even introduce new bugs. Instead, we propose property-based APR, which uses human-specified, program-independent and vulnerability-specific safety properties to derive source code patches for security vulnerabilities. Unlike properties that are approximated by observing the execution of test cases, such safety properties are precise and complete. The primary challenge lies in mapping such safety properties into source code patches that can be instantiated into an existing program. To address these challenges, we propose Senx, which, given a set of safety properties and a single input that triggers the vulnerability, detects the safety property violated by the vulnerability input and generates a corresponding patch that enforces the safety property and thus, removes the vulnerability. Senx solves several challenges with property-based APR: it identifies the program expressions and variables that must be evaluated to check safety properties and identifies the program scopes where they can be evaluated, it generates new code to selectively compute the values it needs if calling existing program code would cause unwanted side effects, and it uses a novel access range analysis technique to avoid placing patches inside loops where it could incur performance overhead. Our evaluation shows that the patches generated by Senx successfully fix 32 of 42 real-world vulnerabilities from 11 applications including various tools or libraries for manipulating graphics/media files, a programming language interpreter, a relational database engine, a collection of programming tools for creating and managing binary programs, and a collection of basic file, shell, and text manipulation tools.
2020-03-23
Pewny, Jannik, Koppe, Philipp, Holz, Thorsten.  2019.  STEROIDS for DOPed Applications: A Compiler for Automated Data-Oriented Programming. 2019 IEEE European Symposium on Security and Privacy (EuroS P). :111–126.
The wide-spread adoption of system defenses such as the randomization of code, stack, and heap raises the bar for code-reuse attacks. Thus, attackers utilize a scripting engine in target programs like a web browser to prepare the code-reuse chain, e.g., relocate gadget addresses or perform a just-in-time gadget search. However, many types of programs do not provide such an execution context that an attacker can use. Recent advances in data-oriented programming (DOP) explored an orthogonal way to abuse memory corruption vulnerabilities and demonstrated that an attacker can achieve Turing-complete computations without modifying code pointers in applications. As of now, constructing DOP exploits requires a lot of manual work-for every combination of application and payload anew. In this paper, we present novel techniques to automate the process of generating DOP exploits. We implemented a compiler called STEROIDS that leverages these techniques and compiles our high-level language SLANG into low-level DOP data structures driving malicious computations at run time. This enables an attacker to specify her intent in an application-and vulnerability-independent manner to maximize reusability. We demonstrate the effectiveness of our techniques and prototype implementation by specifying four programs of varying complexity in SLANG that calculate the Levenshtein distance, traverse a pointer chain to steal a private key, relocate a ROP chain, and perform a JIT-ROP attack. STEROIDS compiles each of those programs to low-level DOP data structures targeted at five different applications including GStreamer, Wireshark and ProFTPd, which have vastly different vulnerabilities and DOP instances. Ultimately, this shows that our compiler is versatile, can be used for both 32-bit and 64-bit applications, works across bug classes, and enables highly expressive attacks without conventional code-injection or code-reuse techniques in applications lacking a scripting engine.
Naik, Nitin, Jenkins, Paul, Gillett, Jonathan, Mouratidis, Haralambos, Naik, Kshirasagar, Song, Jingping.  2019.  Lockout-Tagout Ransomware: A Detection Method for Ransomware using Fuzzy Hashing and Clustering. 2019 IEEE Symposium Series on Computational Intelligence (SSCI). :641–648.

Ransomware attacks are a prevalent cybersecurity threat to every user and enterprise today. This is attributed to their polymorphic behaviour and dispersion of inexhaustible versions due to the same ransomware family or threat actor. A certain ransomware family or threat actor repeatedly utilises nearly the same style or codebase to create a vast number of ransomware versions. Therefore, it is essential for users and enterprises to keep well-informed about this threat landscape and adopt proactive prevention strategies to minimise its spread and affects. This requires a technique to detect ransomware samples to determine the similarity and link with the known ransomware family or threat actor. Therefore, this paper presents a detection method for ransomware by employing a combination of a similarity preserving hashing method called fuzzy hashing and a clustering method. This detection method is applied on the collected WannaCry/WannaCryptor ransomware samples utilising a range of fuzzy hashing and clustering methods. The clustering results of various clustering methods are evaluated through the use of the internal evaluation indexes to determine the accuracy and consistency of their clustering results, thus the effective combination of fuzzy hashing and clustering method as applied to the particular ransomware corpus. The proposed detection method is a static analysis method, which requires fewer computational overheads and performs rapid comparative analysis with respect to other static analysis methods.

Naik, Nitin, Jenkins, Paul, Savage, Nick, Yang, Longzhi.  2019.  Cyberthreat Hunting - Part 1: Triaging Ransomware using Fuzzy Hashing, Import Hashing and YARA Rules. 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). :1–6.

Ransomware is currently one of the most significant cyberthreats to both national infrastructure and the individual, often requiring severe treatment as an antidote. Triaging ran-somware based on its similarity with well-known ransomware samples is an imperative preliminary step in preventing a ransomware pandemic. Selecting the most appropriate triaging method can improve the precision of further static and dynamic analysis in addition to saving significant t ime a nd e ffort. Currently, the most popular and proven triaging methods are fuzzy hashing, import hashing and YARA rules, which can ascertain whether, or to what degree, two ransomware samples are similar to each other. However, the mechanisms of these three methods are quite different and their comparative assessment is difficult. Therefore, this paper presents an evaluation of these three methods for triaging the four most pertinent ransomware categories WannaCry, Locky, Cerber and CryptoWall. It evaluates their triaging performance and run-time system performance, highlighting the limitations of each method.

2020-03-16
Singh, Rina, Graves, Jeffrey A., Anantharaj, Valentine, Sukumar, Sreenivas R..  2019.  Evaluating Scientific Workflow Engines for Data and Compute Intensive Discoveries. 2019 IEEE International Conference on Big Data (Big Data). :4553–4560.
Workflow engines used to script scientific experiments involving numerical simulation, data analysis, instruments, edge sensors, and artificial intelligence have to deal with the complexities of hardware, software, resource availability, and the collaborative nature of science. In this paper, we survey workflow engines used in data-intensive and compute-intensive discovery pipelines from scientific disciplines such as astronomy, high energy physics, earth system science, bio-medicine, and material science and present a qualitative analysis of their respective capabilities. We compare 5 popular workflow engines and their differentiated approach to job orchestration, job launching, data management and provenance, security authentication, ease-ofuse, workflow description, and scripting semantics. The comparisons presented in this paper allow practitioners to choose the appropriate engine for their scientific experiment and lead to recommendations for future work.