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Tsuyoshi Arai, Yasuo Okabe, Yoshinori Matsumoto, Koji Kawamura.  2020.  Detection of Bots in CAPTCHA as a Cloud Service Utilizing Machine Learning.

In recent years, the damage caused by unauthorized access using bots has increased. Compared with attacks on conventional login screens, the success rate is higher and detection of them is more difficult. CAPTCHA is commonly utilized as a technology for avoiding attacks by bots. But user's experience declines as the difficulty of CAPTCHA becomes higher corresponding to the advancement of the bot. As a solution, adaptive difficulty setting of CAPTCHA combining with bot detection technologies is considered. In this research, we focus on Capy puzzle CAPTCHA, which is widely used in commercial service. We use a supervised machine learning approach to detect bots. As a training data, we use access logs to several Web services, and add flags to attacks by bots detected in the past. We have extracted vectors fields like HTTP-User-Agent and some information from IP address (e.g. geographical information) from the access logs, and the dataset is investigated using supervised learning. By using XGBoost and LightGBM, we have achieved high ROC-AUC score more than 0.90, and further have detected suspicious accesses from some ISPs that has no bot discrimination flag.

Book Chapter
Li, Bo, Vorobeychik, Yevgeniy.  2014.  Feature Cross-Substitution in Adversarial Classification. Advances in Neural Information Processing Systems 27. :2087–2095.

The success of machine learning, particularly in supervised settings, has led to numerous attempts to apply it in adversarial settings such as spam and malware detection. The core challenge in this class of applications is that adversaries are not static data generators, but make a deliberate effort to evade the classifiers deployed to detect them. We investigate both the problem of modeling the objectives of such adversaries, as well as the algorithmic problem of accounting for rational, objective-driven adversaries. In particular, we demonstrate severe shortcomings of feature reduction in adversarial settings using several natural adversarial objective functions, an observation that is particularly pronounced when the adversary is able to substitute across similar features (for example, replace words with synonyms or replace letters in words). We offer a simple heuristic method for making learning more robust to feature cross-substitution attacks. We then present a more general approach based on mixed-integer linear programming with constraint generation, which implicitly trades off overfitting and feature selection in an adversarial setting using a sparse regularizer along with an evasion model. Our approach is the first method for combining an adversarial classification algorithm with a very general class of models of adversarial classifier evasion. We show that our algorithmic approach significantly outperforms state-of-the-art alternatives.

Waseem Abbas, Aron Laszka, Koutsoukos, Xenofon.  2015.  Resilient Wireless Sensor Networks for Cyber-Physical Systems. Cyber-Physical System Design with Sensor Networking Technologies.

Due to their low deployment costs, wireless sensor networks (WSN) may act as a key enabling technology for a variety of spatially-distributed cyber-physical system (CPS) applications, ranging from intelligent traffic control to smart grids. However, besides providing tremendous benefits in terms of deployment costs, they also open up new possibilities for malicious attackers, who aim to cause financial losses or physical damage. Since perfectly securing these spatially-distributed systems is either impossible or financially unattainable, we need to design them to be resilient to attacks: even if some parts of the system are compromised or unavailable due to the actions of an attacker, the system as a whole must continue to operate with minimal losses. In a CPS, control decisions affecting the physical process depend on the observed data from the sensor network. Any malicious activity in the sensor network can therefore severely impact the physical process, and consequently the overall CPS operations. These factors necessitate a deeper probe into the domain of resilient WSN for CPS. In this chapter, we provide an overview of various dimensions in this field, including objectives of WSN in CPS, attack scenarios and vulnerabilities, notion of attack-resilience in WSN for CPS, and solution approaches towards attaining resilience. We also highlight major challenges, recent developments, and future directions in this area.

Conference Paper
Khan, JavedAkhtar.  2019.  2019 3rd International Conference on Computing Methodologies and Communication (ICCMC). 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC). :619-623.

This paper proposes the implementation of progressive authentication service in smart android mobile phone. In this digital era, massive amount of work can be done in the digital form using the smart devices like smart phone , laptop, Tablets, etc. The number of smartphone users approx. reach to 299.24 million, as per the recent survey report [1] in 2019 this count will reach 2.7 billion and after 3 years, this count will increase up to 442.5 million. This article includes a cluster based progressive smart lock with a dependent combination that is short and more secure in nature. Android provides smart lock facilities with the combination of 9 dot, 6dot, 5dot, 4dot and 1-9 number. By using this mobile phone user will be able to generate pattern lock or number password for authentication. This is a single authentication system, this research paper includes a more secured multiple cluster based pattern match system.

Khakurel, U., Rawat, D., Njilla, L..  2019.  2019 IEEE International Conference on Industrial Internet (ICII). 2019 IEEE International Conference on Industrial Internet (ICII). :241—247.

FastChain is a simulator built in NS-3 which simulates the networked battlefield scenario with military applications, connecting tankers, soldiers and drones to form Internet-of-Battlefield-Things (IoBT). Computing, storage and communication resources in IoBT are limited during certain situations in IoBT. Under these circumstances, these resources should be carefully combined to handle the task to accomplish the mission. FastChain simulator uses Sharding approach to provide an efficient solution to combine resources of IoBT devices by identifying the correct and the best set of IoBT devices for a given scenario. Then, the set of IoBT devices for a given scenario collaborate together for sharding enabled Blockchain technology. Interested researchers, policy makers and developers can download and use the FastChain simulator to design, develop and evaluate blockchain enabled IoBT scenarios that helps make robust and trustworthy informed decisions in mission-critical IoBT environment.

Armin, J., Thompson, B., Ariu, D., Giacinto, G., Roli, F., Kijewski, P..  2015.  2020 Cybercrime Economic Costs: No Measure No Solution. 2015 10th International Conference on Availability, Reliability and Security. :701–710.

Governments needs reliable data on crime in order to both devise adequate policies, and allocate the correct revenues so that the measures are cost-effective, i.e., The money spent in prevention, detection, and handling of security incidents is balanced with a decrease in losses from offences. The analysis of the actual scenario of government actions in cyber security shows that the availability of multiple contrasting figures on the impact of cyber-attacks is holding back the adoption of policies for cyber space as their cost-effectiveness cannot be clearly assessed. The most relevant literature on the topic is reviewed to highlight the research gaps and to determine the related future research issues that need addressing to provide a solid ground for future legislative and regulatory actions at national and international levels.

Jun, Jaehoon, Rhee, Cyuyeol, Kim, Suhwan.  2016.  A 386-\$\textbackslashmu\$W, 15.2-bit Programmable-Gain Embedded Delta-Sigma ADC for Sensor Applications. Proceedings of the 2016 International Symposium on Low Power Electronics and Design. :278–283.

A power-efficient programmable-gain control function embedded Delta-Sigma (ΔΣ) analog-to-digital converter (ADC) for various smart sensor applications is presented. It consists of a programmable-gain switched-capacitor ΔΣ modulator followed by a digital decimation filter for down-sampling. The programmable function is realized with programmable coefficients of a loop filter using a capacitor array. The coefficient control is accomplished with keeping the location of poles of a noise transfer function, so the stability of a designed closed-loop transfer function can be assured. The proposed gain control method helps ADC to optimize its performance with varying input signal magnitude. The gain controllability requires negligible additional energy consuming or area occupying block. The power efficient programmable-gain ADC (PGADC) is well-suited for sensor devices. The gain amplification can be optimized from 0 to 18 dB with a 6 dB step. Measurements show that the PGADC achieves 15.2-bit resolution and 12.4-bit noise free resolution with 99.9 % reliability. The chip operates with a 3.3 V analog supply and a 1.8 V digital supply, while consuming only 97 μA analog current and 37 μA digital current. The analog core area is 0.064 mm2 in a standard 0.18-μm CMOS process.

Ly, Son Thai, Do, Nhu-Tai, Lee, Guee-Sang, Kim, Soo-Hyung, Yang, Hyung-Jeong.  2019.  A 3d Face Modeling Approach for in-The-Wild Facial Expression Recognition on Image Datasets. 2019 IEEE International Conference on Image Processing (ICIP). :3492—3496.

This paper explores the benefits of 3D face modeling for in-the-wild facial expression recognition (FER). Since there is limited in-the-wild 3D FER dataset, we first construct 3D facial data from available 2D dataset using recent advances in 3D face reconstruction. The 3D facial geometry representation is then extracted by deep learning technique. In addition, we also take advantage of manipulating the 3D face, such as using 2D projected images of 3D face as additional input for FER. These features are then fused with that of 2D FER typical network. By doing so, despite using common approaches, we achieve a competent recognition accuracy on Real-World Affective Faces (RAF) database and Static Facial Expressions in the Wild (SFEW 2.0) compared with the state-of-the-art reports. To the best of our knowledge, this is the first time such a deep learning combination of 3D and 2D facial modalities is presented in the context of in-the-wild FER.

Abi-Antoun, Marwan, Khalaj, Ebrahim, Vanciu, Radu, Moghimi, Ahmad.  2016.  Abstract Runtime Structure for Reasoning About Security: Poster. Proceedings of the Symposium and Bootcamp on the Science of Security. :1–3.

We propose an interactive approach where analysts reason about the security of a system using an abstraction of its runtime structure, as opposed to looking at the code. They interactively refine a hierarchical object graph, set security properties on abstract objects or edges, query the graph, and investigate the results by studying highlighted objects or edges or tracing to the code. Behind the scenes, an inference analysis and an extraction analysis maintain the soundness of the graph with respect to the code.

Kuka, Mário, Vojanec, Kamil, Kučera, Jan, Benáček, Pavel.  2019.  Accelerated DDoS Attacks Mitigation using Programmable Data Plane. 2019 ACM/IEEE Symposium on Architectures for Networking and Communications Systems (ANCS). :1–3.

DDoS attacks are a significant threat to internet service or infrastructure providers. This poster presents an FPGA-accelerated device and DDoS mitigation technique to overcome such attacks. Our work addresses amplification attacks whose goal is to generate enough traffic to saturate the victims links. The main idea of the device is to efficiently filter malicious traffic at high-speeds directly in the backbone infrastructure before it even reaches the victim's network. We implemented our solution for two FPGA platforms using the high-level description in P4, and we report on its performance in terms of throughput and hardware resources.

Duraisamy, Karthi, Lu, Hao, Pande, Partha Pratim, Kalyanaraman, Ananth.  2017.  Accelerating Graph Community Detection with Approximate Updates via an Energy-Efficient NoC. Proceedings of the 54th Annual Design Automation Conference 2017. :89:1–89:6.

Community detection is an advanced graph operation that is used to reveal tightly-knit groups of vertices (aka. communities) in real-world networks. Given the intractability of the problem, efficient heuristics are used in practice. Yet, even the best of these state-of-the-art heuristics can become computationally demanding over large inputs and can generate workloads that exhibit inherent irregularity in data movement on manycore platforms. In this paper, we posit that effective acceleration of the graph community detection operation can be achieved by reducing the cost of data movement through a combined innovation at both software and hardware levels. More specifically, we first propose an efficient software-level parallelization of community detection that uses approximate updates to cleverly exploit a diminishing returns property of the algorithm. Secondly, as a way to augment this innovation at the software layer, we design an efficient Wireless Network on Chip (WiNoC) architecture that is suited to handle the irregular on-chip data movements exhibited by the community detection algorithm under both unicast- and broadcast-heavy cache coherence protocols. Experimental results show that our resulting WiNoC-enabled manycore platform achieves on average 52% savings in execution time, without compromising on the quality of the outputs, when compared to a traditional manycore platform designed with a wireline mesh NoC and running community detection without employing approximate updates.

Datta, A., Kar, S., Sinopoli, B., Weerakkody, S..  2016.  Accountability in cyber-physical systems. 2016 Science of Security for Cyber-Physical Systems Workshop (SOSCYPS). :1–3.

Our position is that a key component of securing cyber-physical systems (CPS) is to develop a theory of accountability that encompasses both control and computing systems. We envision that a unified theory of accountability in CPS can be built on a foundation of causal information flow analysis. This theory will support design and analysis of mechanisms at various stages of the accountability regime: attack detection, responsibility-assignment (e.g., attack identification or localization), and corrective measures (e.g., via resilient control) As an initial step in this direction, we summarize our results on attack detection in control systems. We use the Kullback-Liebler (KL) divergence as a causal information flow measure. We then recover, using information flow analyses, a set of existing results in the literature that were previously proved using different techniques. These results cover passive detection, stealthy attack characterization, and active detection. This research direction is related to recent work on accountability in computational systems [1], [2], [3], [4]. We envision that by casting accountability theories in computing and control systems in terms of causal information flow, we can provide a common foundation to develop a theory for CPS that compose elements from both domains.

Noureddine, M. A., Marturano, A., Keefe, K., Bashir, M., Sanders, W. H..  2017.  Accounting for the Human User in Predictive Security Models. 2017 IEEE 22nd Pacific Rim International Symposium on Dependable Computing (PRDC). :329–338.

Given the growing sophistication of cyber attacks, designing a perfectly secure system is not generally possible. Quantitative security metrics are thus needed to measure and compare the relative security of proposed security designs and policies. Since the investigation of security breaches has shown a strong impact of human errors, ignoring the human user in computing these metrics can lead to misleading results. Despite this, and although security researchers have long observed the impact of human behavior on system security, few improvements have been made in designing systems that are resilient to the uncertainties in how humans interact with a cyber system. In this work, we develop an approach for including models of user behavior, emanating from the fields of social sciences and psychology, in the modeling of systems intended to be secure. We then illustrate how one of these models, namely general deterrence theory, can be used to study the effectiveness of the password security requirements policy and the frequency of security audits in a typical organization. Finally, we discuss the many challenges that arise when adopting such a modeling approach, and then present our recommendations for future work.

Kyoungwoo Heo.  2014.  An Accumulated Loss Recovery Algorithm on Overlay Multicast System Using Fountain Codes. Information Science and Applications (ICISA), 2014 International Conference on. :1-3.

In this paper, we propose an accumulated loss recovery algorithm on overlay multicast system using Fountain codes. Fountain code successfully decodes the packet loss, but it is weak in accumulated losses on multicast tree. The proposed algorithm overcomes an accumulated loss and significantly reduces delay on overlay multicast tree.

Uemura, Toshiaki, Kashiwabara, Yuta, Kawanuma, Daiki, Tomii, Takashi.  2016.  Accuracy Evaluation by GPS Data Correction for the EV Energy Consumption Database. Adjunct Proceedings of the 13th International Conference on Mobile and Ubiquitous Systems: Computing Networking and Services. :213–218.
Electric vehicles (EVs) are expected to be applicable to smart grids because they have large-capacity batteries. It is important that smart grid users be able to estimate surplus battery energy and/or surplus capacity in advance of deploying EVs. We constructed a database, the Energy COnsumption LOG (ECOLOG) Database System, to store vehicle daily logs acquired by smartphones placed in vehicles. The electrical energy consumption is estimated from GPS coordinate data using an EV energy-consumption model. This research specifically examines commuting with a vehicle used for same route every day. We corrected GPS coordinate data by map matching, and input the data to the EV energy consumption model. We regard the remaining battery capacity data acquired by the EV CAN as correct data. Then we evaluate the accuracy of driving energy consumption logs as estimated using the corrected GPS coordinate data.
Khadka, A., Argyriou, V., Remagnino, P..  2020.  Accurate Deep Net Crowd Counting for Smart IoT Video acquisition devices. 2020 16th International Conference on Distributed Computing in Sensor Systems (DCOSS). :260—264.

A novel deep neural network is proposed, for accurate and robust crowd counting. Crowd counting is a complex task, as it strongly depends on the deployed camera characteristics and, above all, the scene perspective. Crowd counting is essential in security applications where Internet of Things (IoT) cameras are deployed to help with crowd management tasks. The complexity of a scene varies greatly, and a medium to large scale security system based on IoT cameras must cater for changes in perspective and how people appear from different vantage points. To address this, our deep architecture extracts multi-scale features with a pyramid contextual module to provide long-range contextual information and enlarge the receptive field. Experiments were run on three major crowd counting datasets, to test our proposed method. Results demonstrate our method supersedes the performance of state-of-the-art methods.

Dong, Qiuxiang, Huang, Dijiang, Luo, Jim, Kang, Myong.  2018.  Achieving Fine-Grained Access Control with Discretionary User Revocation over Cloud Data. 2018 IEEE Conference on Communications and Network Security (CNS). :1—9.
Cloud storage solutions have gained momentum in recent years. However, cloud servers can not be fully trusted. Data access control have becomes one of the main impediments for further adoption. One appealing approach is to incorporate the access control into encrypted data, thus removing the need to trust the cloud servers. Among existing cryptographic solutions, Ciphertext Policy Attribute-Based Encryption (CP-ABE) is well suited for fine-grained data access control in cloud storage. As promising as it is, user revocation is a cumbersome problem that impedes its wide application. To address this issue, we design an access control system called DUR-CP-ABE, which implements identity-based User Revocation in a data owner Discretionary way. In short, the proposed solution provides the following salient features. First, user revocation enforcement is based on the discretion of the data owner, thus providing more flexibility. Second, no private key updates are needed when user revocation occurs. Third, the proposed scheme allows for group revocation of affiliated users in a batch operation. To the best of our knowledge, DUR-CP-ABE is the first CP-ABE solution to provide affiliation- based batch revocation functionality, which fits naturally into organizations' Identity and Access Management (IAM) structure. The analysis shows that the proposed access control system is provably secure and efficient in terms of computation, communi- cation and storage.
Green, Benjamin, Krotofil, Marina, Hutchison, David.  2016.  Achieving ICS Resilience and Security Through Granular Data Flow Management. Proceedings of the 2Nd ACM Workshop on Cyber-Physical Systems Security and Privacy. :93–101.

Modern Industrial Control Systems (ICS) rely on enterprise to plant floor connectivity. Where the size, diversity, and therefore complexity of ICS increase, operational requirements, goals, and challenges defined by users across various sub-systems follow. Recent trends in Information Technology (IT) and Operational Technology (OT) convergence may cause operators to lose a comprehensive understanding of end-to-end data flow requirements. This presents a risk to system security and resilience. Sensors were once solely applied for operational process use, but now act as inputs supporting a diverse set of organisational requirements. If these are not fully understood, incomplete risk assessment, and inappropriate implementation of security controls could occur. In search of a solution, operators may turn to standards and guidelines. This paper reviews popular standards and guidelines, prior to the presentation of a case study and conceptual tool, highlighting the importance of data flows, critical data processing points, and system-to-user relationships. The proposed approach forms a basis for risk assessment and security control implementation, aiding the evolution of ICS security and resilience.

Burley, Diana, Bishop, Matt, Kaza, Siddharth, Gibson, David S., Hawthorne, Elizabeth, Buck, Scott.  2017.  ACM Joint Task Force on Cybersecurity Education. Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education. :683–684.
In this special session, members of the ACM Joint Task Force (JTF) on Cybersecurity Education will provide an overview of the task force mission, objectives, and release a draft of the curricular guidelines. After the overview, task force members will engage session participants in the curricular development process and solicit feedback on the draft guidelines.
Kimmich, J. M., Schlesinger, A., Tschaikner, M., Ochmann, M., Frank, S..  2018.  Acoustical Analysis of Coupled Rooms Applied to the Deutsche Oper Berlin. 2018 Joint Conference - Acoustics. :1–9.
The aim of the project SIMOPERA is to simulate and optimize the acoustics in large and complex rooms, with special focus on the Deutsche Oper Berlin as an example of application. Firstly, characteristic subspaces of the opera are considered such as the orchestra pit, the stage and the auditorium. Special attention is paid to the orchestra pit, where high sound pressure levels can occur, leading to noise related risks for the musicians. However, lowering the sound pressure level in the orchestra pit should not violate other objectives as the propagation of sound into the auditorium, the balance between the stage performers and the orchestra across the hall, and the mutual audibility between performers and orchestra members. For that reason, a hybrid simulation method consisting of the wave-based Finite Element Method (FEM) and the Boundary Element Method (BEM) for low frequencies and geometrical methods like the mirror source method and ray tracing for higher frequencies is developed in order to determine the relevant room acoustic quantities such as impulse response functions, reverberation time, clarity, center time etc. Measurements in the opera will continuously accompany the numerical calculations. Finally, selected constructive means for reducing the sound level in the orchestra pit will be analyzed.
Schulz, A., Kotson, M., Meiners, C., Meunier, T., O’Gwynn, D., Trepagnier, P., Weller-Fahy, D..  2017.  Active Dependency Mapping: A Data-Driven Approach to Mapping Dependencies in Distributed Systems. 2017 IEEE International Conference on Information Reuse and Integration (IRI). :84–91.

We introduce Active Dependency Mapping (ADM), a method for establishing dependency relations among a set of interdependent services. The approach is to artificially degrade network performance to infer which assets on the network support a particular process. Artificial degradation of the network environment could be transparent to users; run continuously it could identify dependencies that are rare or occur only at certain timescales. A useful byproduct of this dependency analysis is a quantitative assessment of the resilience and robustness of the system. This technique is intriguing for hardening both enterprise networks and cyber physical systems. We present a proof-of-concept experiment executed on a real-world set of interrelated software services. We assess the efficacy of the approach, discuss current limitations, and suggest options for future development of ADM.

Krishnan, Sanjay, Franklin, Michael J., Goldberg, Ken, Wang, Jiannan, Wu, Eugene.  2016.  ActiveClean: An Interactive Data Cleaning Framework For Modern Machine Learning. Proceedings of the 2016 International Conference on Management of Data. :2117–2120.

Databases can be corrupted with various errors such as missing, incorrect, or inconsistent values. Increasingly, modern data analysis pipelines involve Machine Learning, and the effects of dirty data can be difficult to debug.Dirty data is often sparse, and naive sampling solutions are not suited for high-dimensional models. We propose ActiveClean, a progressive framework for training Machine Learning models with data cleaning. Our framework updates a model iteratively as the analyst cleans small batches of data, and includes numerous optimizations such as importance weighting and dirty data detection. We designed a visual interface to wrap around this framework and demonstrate ActiveClean for a video classification problem and a topic modeling problem.

Livshitz, Ilva I., Lontsikh, Pawel A., Lontsiklr, Natalia P., Karascv, Sergey, Golovina, Elena.  2019.  The Actual Problems of IT-Security Process Assurance. 2019 International Conference "Quality Management, Transport and Information Security, Information Technologies" (IT QM IS). :140–144.

The article deals with the aspects of IT-security of business processes, using a variety of methodological tools, including Integrated Management Systems. Currently, all IMS consist of at least 2 management systems, including the IT-Security Management System. Typically, these IMS cover biggest part of the company business processes, but in practice, there are examples of different scales, even within a single facility. However, it should be recognized that the total number of such projects both in the Russian Federation and in the World is small. The security of business processes will be considered on the example of the incident of Norsk Hydro. In the article the main conclusions are given to confirm the possibility of security, continuity and recovery of critical business processes on the example of this incident.

C. H. Hsieh, C. M. Lai, C. H. Mao, T. C. Kao, K. C. Lee.  2015.  "AD2: Anomaly detection on active directory log data for insider threat monitoring". 2015 International Carnahan Conference on Security Technology (ICCST). :287-292.

What you see is not definitely believable is not a rare case in the cyber security monitoring. However, due to various tricks of camouflages, such as packing or virutal private network (VPN), detecting "advanced persistent threat"(APT) by only signature based malware detection system becomes more and more intractable. On the other hand, by carefully modeling users' subsequent behaviors of daily routines, probability for one account to generate certain operations can be estimated and used in anomaly detection. To the best of our knowledge so far, a novel behavioral analytic framework, which is dedicated to analyze Active Directory domain service logs and to monitor potential inside threat, is now first proposed in this project. Experiments on real dataset not only show that the proposed idea indeed explores a new feasible direction for cyber security monitoring, but also gives a guideline on how to deploy this framework to various environments.