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Journal Article
Leonardi, Christopher, Koziel, Brian, Kalach, Kassem, Jao, David, Azarderakhsh, Reza.  2016.  Key Compression for Isogeny-Based Cryptosystems.

We present a method for key compression in quantumresistant isogeny-based cryptosystems, which allows a reduction in and transmission costs of per-party public information by a factor of two, with no e ect on security. We achieve this reduction by associating a canonical choice of elliptic curve to each j-invariant, and representing elements on the curve as linear combinations with respect to a canonical choice of basis. This method of compressing public information can be applied to numerous isogeny-based protocols, such as key exchange, zero-knowledge identi cation, and public-key encryption. We performed personal computer and ARM implementations of the key exchange with compression and decompression in C and provided timing results, showing the computational cost of key compression and decompression at various security levels. Our results show that isogeny-based cryptosystems achieve by far the smallest possible key sizes among all existing families of post-quantum cryptosystems at practical security levels; e.g. 3073-bit public keys at the quantum 128-bit security level, comparable to (non-quantum) RSA key sizes.

Kumar, Suren, Dhiman, Vikas, Koch, Parker A, Corso, Jason J..  2018.  Learning Compositional Sparse Bimodal Models. IEEE Transactions on Pattern Analysis and Machine Intelligence. 40:1032—1044.

Various perceptual domains have underlying compositional semantics that are rarely captured in current models. We suspect this is because directly learning the compositional structure has evaded these models. Yet, the compositional structure of a given domain can be grounded in a separate domain thereby simplifying its learning. To that end, we propose a new approach to modeling bimodal perceptual domains that explicitly relates distinct projections across each modality and then jointly learns a bimodal sparse representation. The resulting model enables compositionality across these distinct projections and hence can generalize to unobserved percepts spanned by this compositional basis. For example, our model can be trained on red triangles and blue squares; yet, implicitly will also have learned red squares and blue triangles. The structure of the projections and hence the compositional basis is learned automatically; no assumption is made on the ordering of the compositional elements in either modality. Although our modeling paradigm is general, we explicitly focus on a tabletop building-blocks setting. To test our model, we have acquired a new bimodal dataset comprising images and spoken utterances of colored shapes (blocks) in the tabletop setting. Our experiments demonstrate the benefits of explicitly leveraging compositionality in both quantitative and human evaluation studies.

Zolanvari, Maede, Teixeira, Marcio A., Gupta, Lav, Khan, Khaled M., Jain, Raj.  2019.  Machine Learning-Based Network Vulnerability Analysis of Industrial Internet of Things. IEEE Internet of Things Journal. 6:6822—6834.
It is critical to secure the Industrial Internet of Things (IIoT) devices because of potentially devastating consequences in case of an attack. Machine learning (ML) and big data analytics are the two powerful leverages for analyzing and securing the Internet of Things (IoT) technology. By extension, these techniques can help improve the security of the IIoT systems as well. In this paper, we first present common IIoT protocols and their associated vulnerabilities. Then, we run a cyber-vulnerability assessment and discuss the utilization of ML in countering these susceptibilities. Following that, a literature review of the available intrusion detection solutions using ML models is presented. Finally, we discuss our case study, which includes details of a real-world testbed that we have built to conduct cyber-attacks and to design an intrusion detection system (IDS). We deploy backdoor, command injection, and Structured Query Language (SQL) injection attacks against the system and demonstrate how a ML-based anomaly detection system can perform well in detecting these attacks. We have evaluated the performance through representative metrics to have a fair point of view on the effectiveness of the methods.
Machida, H., Fujiwara, T., Fujimoto, C., Kanamori, Y., Tanaka, J., Takezawa, M..  2019.  Magnetic Domain Structures and Magnetic Properties of Lightly Nd-Doped Sm–Co Magnets With High Squareness and High Heat Resistance. IEEE Transactions on Magnetics. 55:1–4.
The relationship between magnetic domain structures and magnetic properties of Nd-doped Sm(Fe, Cu, Zr, Co)7.5 was investigated. In the preparation process, slow cooling between sintering and solution treatment was employed to promote homogenization of microstructures. The developed magnet achieved a maximum energy product, [BH]m, of 33.8 MGOe and coercivity, Hcb, of 11.2 kOe at 25 °C, respectively. Moreover, B-H line at 150 °C was linear, which means that irreversible demagnetization does not occur even at 150 °C. Temperature coefficients of remanent magnetic flux density, Br, and intrinsic coercivity, Hcj, were 0.035%/K and 0.24%/K, respectively, as usual the conventional Sm-Co magnet. Magnetic domain structures were observed with a Kerr effect microscope with a magnetic field applied from 0 to -20 kOe, and then reverse magnetic domains were generated evenly from grain boundaries. Microstructures referred to as “cell structures” were observed with a scanning transmission electron microscope. Fe and Cu were separated to 2-17 and 1-5 phases, respectively. Moreover, without producing impurity phases, Nd showed the same composition behavior with Sm in a cell structure.
Kim, Seung-Wook, Park, Sun Young, Han, Junghyun.  2018.  Magnetization Dynamics for Magnetic Object Interactions. ACM Trans. Graph.. 37:121:1–121:13.
The goal of this paper is to simulate the interactions between magnetic objects in a physically correct way. The simulation scheme is based on magnetization dynamics, which describes the temporal change of magnetic moments. For magnetization dynamics, the Landau-Lifshitz-Gilbert equation is adopted, which is widely used in micromagnetics. Through effectively-designed novel models of magnets, it is extended into the macro scale so as to be combined with real-time rigid-body dynamics. The overall simulation is stable and enables us to implement mutual induction and remanence that have not been tackled by the state-of-the-art technique in magnet simulation. The proposed method can be applied to various fields including magnet experiments in the virtual world.
Han, Lansheng, Qian, Mengxiao, Xu, Xingbo, Fu, Cai, Kwisaba, Hamza.  2014.  Malicious code detection model based on behavior association. Tsinghua Science and Technology. 19:508-515.

Malicious applications can be introduced to attack users and services so as to gain financial rewards, individuals' sensitive information, company and government intellectual property, and to gain remote control of systems. However, traditional methods of malicious code detection, such as signature detection, behavior detection, virtual machine detection, and heuristic detection, have various weaknesses which make them unreliable. This paper presents the existing technologies of malicious code detection and a malicious code detection model is proposed based on behavior association. The behavior points of malicious code are first extracted through API monitoring technology and integrated into the behavior; then a relation between behaviors is established according to data dependence. Next, a behavior association model is built up and a discrimination method is put forth using pushdown automation. Finally, the exact malicious code is taken as a sample to carry out an experiment on the behavior's capture, association, and discrimination, thus proving that the theoretical model is viable.

Han, Lansheng, Qian, Mengxiao, Xu, Xingbo, Fu, Cai, Kwisaba, Hamza.  2014.  Malicious code detection model based on behavior association. Tsinghua Science and Technology. 19:508-515.

Malicious applications can be introduced to attack users and services so as to gain financial rewards, individuals' sensitive information, company and government intellectual property, and to gain remote control of systems. However, traditional methods of malicious code detection, such as signature detection, behavior detection, virtual machine detection, and heuristic detection, have various weaknesses which make them unreliable. This paper presents the existing technologies of malicious code detection and a malicious code detection model is proposed based on behavior association. The behavior points of malicious code are first extracted through API monitoring technology and integrated into the behavior; then a relation between behaviors is established according to data dependence. Next, a behavior association model is built up and a discrimination method is put forth using pushdown automation. Finally, the exact malicious code is taken as a sample to carry out an experiment on the behavior's capture, association, and discrimination, thus proving that the theoretical model is viable.
 

Lee, Seungkwang, Kim, Taesung, Kang, Yousung.  2018.  A Masked White-Box Cryptographic Implementation for Protecting Against Differential Computation Analysis. IEEE Transactions on Information Forensics and Security. 13:2602–2615.
Recently, gray-box attacks on white-box cryptographic implementations have succeeded. These attacks are more efficient than white-box attacks because they can be performed without detailed knowledge of the target implementation. The success of the gray-box attack is reportedly due to the unbalanced encodings used to generate the white-box lookup table. In this paper, we propose a method to protect the gray-box attack against white-box implementations. The basic idea is to apply the masking technique before encoding intermediate values during the white-box lookup table generation. Because we do not require any random source in runtime, it is possible to perform efficient encryption and decryption using our method. The security and performance analysis shows that the proposed method can be a reliable and efficient countermeasure.
Jingtang Luo, Xiaolong Yang, Jin Wang, Jie Xu, Jian Sun, Keping Long.  2014.  On a Mathematical Model for Low-Rate Shrew DDoS. Information Forensics and Security, IEEE Transactions on. 9:1069-1083.

The shrew distributed denial of service (DDoS) attack is very detrimental for many applications, since it can throttle TCP flows to a small fraction of their ideal rate at very low attack cost. Earlier works mainly focused on empirical studies of defending against the shrew DDoS, and very few of them provided analytic results about the attack itself. In this paper, we propose a mathematical model for estimating attack effect of this stealthy type of DDoS. By originally capturing the adjustment behaviors of victim TCPs congestion window, our model can comprehensively evaluate the combined impact of attack pattern (i.e., how the attack is configured) and network environment on attack effect (the existing models failed to consider the impact of network environment). Henceforth, our model has higher accuracy over a wider range of network environments. The relative error of our model remains around 10% for most attack patterns and network environments, whereas the relative error of the benchmark model in previous works has a mean value of 69.57%, and it could be more than 180% in some cases. More importantly, our model reveals some novel properties of the shrew attack from the interaction between attack pattern and network environment, such as the minimum cost formula to launch a successful attack, and the maximum effect formula of a shrew attack. With them, we are able to find out how to adaptively tune the attack parameters (e.g., the DoS burst length) to improve its attack effect in a given network environment, and how to reconfigure the network resource (e.g., the bottleneck buffer size) to mitigate the shrew DDoS with a given attack pattern. Finally, based on our theoretical results, we put forward a simple strategy to defend the shrew attack. The simulation results indicate that this strategy can remarkably increase TCP throughput by nearly half of the bottleneck bandwidth (and can be higher) for general attack patterns.

Kanizo, Y., Hay, D., Keslassy, I..  2015.  Maximizing the Throughput of Hash Tables in Network Devices with Combined SRAM/DRAM Memory. Parallel and Distributed Systems, IEEE Transactions on. 26:796-809.

Hash tables form a core component of many algorithms as well as network devices. Because of their large size, they often require a combined memory model, in which some of the elements are stored in a fast memory (for example, cache or on-chip SRAM) while others are stored in much slower memory (namely, the main memory or off-chip DRAM). This makes the implementation of real-life hash tables particularly delicate, as a suboptimal choice of the hashing scheme parameters may result in a higher average query time, and therefore in a lower throughput. In this paper, we focus on multiple-choice hash tables. Given the number of choices, we study the tradeoff between the load of a hash table and its average lookup time. The problem is solved by analyzing an equivalent problem: the expected maximum matching size of a random bipartite graph with a fixed left-side vertex degree. Given two choices, we provide exact results for any finite system, and also deduce asymptotic results as the fast memory size increases. In addition, we further consider other variants of this problem and model the impact of several parameters. Finally, we evaluate the performance of our models on Internet backbone traces, and illustrate the impact of the memories speed difference on the choice of parameters. In particular, we show that the common intuition of entirely avoiding slow memory accesses by using highly efficient schemes (namely, with many fast-memory choices) is not always optimal.
 

Beran, P., Klöhn, M., Hohe, H..  2019.  Measurement Characteristics of Different Integrated Three-Dimensional Magnetic Field Sensors. IEEE Magnetics Letters. 10:1–5.
Datasheets of different commercially available integrated sensors for vector measurements of magnetic fields provide typical specifications, such as measurement range, sampling rate, resolution, and noise. Other characteristics of interest, such as linearity, cross-sensitivity, remanent magnetization, and drifts over temperature, are mostly missing. This letter presents testing results of those characteristics of integrated three-dimensional (3-D) sensors working with different sensor principles and technologies in a reproducible measuring process. The sensors are exposed to temperatures from -20 °C to 80 °C and are cycled in hysteresis loops in fields up to 2.5 mT. For applying high-accuracy magnetic fields, a calibrated 3-D Helmholtz coil setup is used. Commercially available integrated 3-D magnetic field sensors are put in operation on a printed circuit board using nonmagnetic passive components. All sensors are configured for best measurement accuracy according to their data-sheets. The results show that sensors based on anisotropic magnetoresistance have high accuracy and low offsets yet also a high degree of nonlinearity. Hall-based sensors show good linearity but also high cross-sensitivity. A magnetic remanence appears for Hall-based sensors with integrated magnetic concentrators as well as for sensors using anisotropic magnetoresistance. Nearly all sensors show remaining drifts over temperature regarding offset and sensitivity up to several percentages.
Zhang, Fan, Kodituwakku, Hansaka Angel Dias Edirisinghe, Hines, J. Wesley, Coble, Jamie.  2019.  Multilayer Data-Driven Cyber-Attack Detection System for Industrial Control Systems Based on Network, System, and Process Data. IEEE Transactions on Industrial Informatics. 15:4362—4369.
The growing number of attacks against cyber-physical systems in recent years elevates the concern for cybersecurity of industrial control systems (ICSs). The current efforts of ICS cybersecurity are mainly based on firewalls, data diodes, and other methods of intrusion prevention, which may not be sufficient for growing cyber threats from motivated attackers. To enhance the cybersecurity of ICS, a cyber-attack detection system built on the concept of defense-in-depth is developed utilizing network traffic data, host system data, and measured process parameters. This attack detection system provides multiple-layer defense in order to gain the defenders precious time before unrecoverable consequences occur in the physical system. The data used for demonstrating the proposed detection system are from a real-time ICS testbed. Five attacks, including man in the middle (MITM), denial of service (DoS), data exfiltration, data tampering, and false data injection, are carried out to simulate the consequences of cyber attack and generate data for building data-driven detection models. Four classical classification models based on network data and host system data are studied, including k-nearest neighbor (KNN), decision tree, bootstrap aggregating (bagging), and random forest (RF), to provide a secondary line of defense of cyber-attack detection in the event that the intrusion prevention layer fails. Intrusion detection results suggest that KNN, bagging, and RF have low missed alarm and false alarm rates for MITM and DoS attacks, providing accurate and reliable detection of these cyber attacks. Cyber attacks that may not be detectable by monitoring network and host system data, such as command tampering and false data injection attacks by an insider, are monitored for by traditional process monitoring protocols. In the proposed detection system, an auto-associative kernel regression model is studied to strengthen early attack detection. The result shows that this approach detects physically impactful cyber attacks before significant consequences occur. The proposed multiple-layer data-driven cyber-attack detection system utilizing network, system, and process data is a promising solution for safeguarding an ICS.
White, E. M. H., Kassen, A. G., Simsek, E., Tang, W., Ott, R. T., Anderson, I. E..  2017.  Net Shape Processing of Alnico Magnets by Additive Manufacturing. IEEE Transactions on Magnetics. 53:1–6.

Alternatives to rare earth permanent magnets, such as alnico, will reduce supply instability, increase sustainability, and could decrease the cost of permanent magnets, especially for high-temperature applications, such as traction drive motors. Alnico magnets with moderate coercivity, high remanence, and relatively high-energy product are conventionally processed by directional solidification and (significant) final machining, contributing to increased costs and additional material waste. Additive manufacturing (AM) is developing as a cost effective method to build net-shape 3-D parts with minimal final machining and properties comparable to wrought parts. This paper describes initial studies of net-shape fabrication of alnico magnets by AM using a laser engineered net shaping (LENS) system. High-pressure gas atomized pre-alloyed powders of two different modified alnico “8” compositions, with high purity and sphericity, were built into cylinders using the LENS process, and followed by heat treatment. The magnetic properties showed improvement over their cast and sintered counterparts. The resulting alnico permanent magnets were characterized using scanning electron microscopy, energy dispersive spectroscopy, electron backscatter diffraction, and hysteresisgraph measurements. These results display the potential for net-shape processing of alnico permanent magnets for use in next generation traction-drive motors and other applications requiring high temperatures and/or complex engineered part geometries.

Kaczmarek, J., Wrobel, M.R..  2014.  Operating system security by integrity checking and recovery using write-protected storage. Information Security, IET. 8:122-131.

An integrity checking and recovery (ICAR) system is presented here, which protects file system integrity and automatically restores modified files. The system enables files cryptographic hashes generation and verification, as well as configuration of security constraints. All of the crucial data, including ICAR system binaries, file backups and hashes database are stored in a physically write-protected storage to eliminate the threat of unauthorised modification. A buffering mechanism was designed and implemented in the system to increase operation performance. Additionally, the system supplies user tools for cryptographic hash generation and security database management. The system is implemented as a kernel extension, compliant with the Linux security model. Experimental evaluation of the system was performed and showed an approximate 10% performance degradation in secured file access compared to regular access.
 

Widergren, Steve, Melton, Ron, Khandekar, Aditya, Nordman, Bruce, Knight, Mark.  2019.  The Plug-and-Play Electricity Era: Interoperability to Integrate Anything, Anywhere, Anytime. IEEE Power and Energy Magazine. 17:47–58.
The inforrmation age continues to transform the mechanics of integrating electric power devices and systems, from coordinated operations based purely on the physics of electric power engineering to an increasing blend of power with information and communication technology. Integrating electric system components is not just about attaching wires. It requires the connection of computer-based automation systems to associated sensing and communication equipment. The architectural impacts are significant. Well-considered and commonly held concepts, principles, and organizational structures continue to emerge to address the complexity of the integrated operational challenges that drive our society to expect more flexibility in configuring the electric power system, while simultaneously achieving greater efficiency, reliability, and resilience. Architectural concepts, such as modularity and composability, contribute to the creation of structures that enable the connection of power system equipment characterized by clearly defined interfaces consisting of physical and cyberlinks. The result of successful electric power system component connection is interoperation: the discipline that drives integration to be simple and reliable.
Zhenfeng Zhang, Kang Yang, Xuexian Hu, Yuchen Wang.  2016.  Practical Anonymous Password Authentication and TLS with Anonymous Client Authentication.

Anonymous authentication allows one to authenticate herself without revealing her identity, and becomes an important technique for constructing privacy-preserving Internet connections. Anonymous password authentication is highly desirable as it enables a client to authenticate herself by a human-memorable password while preserving her privacy. In this paper, we introduce a novel approach for designing anonymous password-authenticated key exchange (APAKE) protocols using algebraic message authentication codes (MACs), where an algebraic MAC wrapped by a password is used by a client for anonymous authentication, and a server issues algebraic MACs to clients and acts as the verifier of login protocols. Our APAKE construction is secure provided that the algebraic MAC is strongly existentially unforgeable under random message and chosen verification queries attack (suf-rmva), weak pseudorandom and tag-randomization simulatable, and has simulation-sound extractable non-interactive zero-knowledge proofs (SE-NIZKs). To design practical APAKE protocols, we instantiate an algebraic MAC based on the q-SDH assumption which satisfies all the required properties, and construct credential presentation algorithms for the MAC which have optimal efficiency for a randomize-then-prove paradigm. Based on the algebraic MAC, we instantiate a highly practical APAKE protocol and denote it by APAKE, which is much more efficient than the mechanisms specified by ISO/IEC 20009-4. An efficient revocation mechanism for APAKE is also proposed.

We integrate APAKE into TLS to present an anonymous client authentication mode where clients holding passwords can authenticate themselves to a server anonymously. Our implementation with 128-bit security shows that the average connection time of APAKE-based ciphersuite is 2.8 ms. With APAKE integrated into the OpenSSL library and using an Apache web server on a 2-core desktop computer, we could serve 953 ECDHE-ECDSA-AES128-GCM-SHA256 HTTPS connections per second for a 10 KB payload. Compared to ECDSA-signed elliptic curve Diffie-Hellman ciphersuite with mutual authentication, this means a 0.27 KB increased handshake size and a 13% reduction in throughput.

Fersch, Manuel, Kiltz, Eike, Poettering, Bertram.  2016.  On the Provable Security of (EC)DSA Signatures.

Among the signature schemes most widely deployed in practice are the DSA (Digital Signature Algorithm) and its elliptic curves variant ECDSA. They are represented in many international standards, including IEEE P1363, ANSI X9.62, and FIPS 186-4. Their popularity stands in stark contrast to the absence of rigorous security analyses: Previous works either study modified versions of (EC)DSA or provide a security analysis of unmodified ECDSA in the generic group model. Unfortunately, works following the latter approach assume abstractions of non-algebraic functions over generic groups for which it remains unclear how they translate to the security of ECDSA in practice. For instance, it has been pointed out that prior results in the generic group model actually establish strong unforgeability of ECDSA, a property that the scheme de facto does not possess. As, further, no formal results are known for DSA, understanding the security of both schemes remains an open problem. In this work we propose GenericDSA, a signature framework that subsumes both DSA and ECDSA in unmodified form. It carefully models the "modulo q" conversion function of (EC)DSA as a composition of three independent functions. The two outer functions mimic algebraic properties in the function's domain and range, the inner one is modeled as a bijective random oracle. We rigorously prove results on the security of GenericDSA that indicate that forging signatures in (EC)DSA is as hard as solving discrete logarithms. Importantly, our proofs do not assume generic group behavior.

Jun, Jaeyung, Choi, Kyu Hyun, Kim, Hokwon, Yu, Sang Ho, Kim, Seon Wook, Han, Youngsun.  2017.  Recovering from Biased Distribution of Faulty Cells in Memory by Reorganizing Replacement Regions Through Universal Hashing. ACM Trans. Des. Autom. Electron. Syst.. 23:16:1–16:21.

Recently, scaling down dynamic random access memory (DRAM) has become more of a challenge, with more faults than before and a significant degradation in yield. To improve the yield in DRAM, a redundancy repair technique with intra-subarray replacement has been extensively employed to replace faulty elements (i.e., rows or columns with defective cells) with spare elements in each subarray. Unfortunately, such technique cannot efficiently handle a biased distribution of faulty cells because each subarray has a fixed number of spare elements. In this article, we propose a novel redundancy repair technique that uses a hashing method to solve this problem. Our hashing technique reorganizes replacement regions by changing the way in which their replacement information is referred, thus making faulty cells become evenly distributed to the regions. We also propose a fast repair algorithm to find the best hash function among all possible candidates. Even if our approach requires little hardware overhead, it significantly improves the yield when compared with conventional redundancy techniques. In particular, the results of our experiment show that our technique saves spare elements by about 57% and 55% for a yield of 99% at BER 1e-6 and 5e-7, respectively.

Kim, M., Park, H., Kim, C., Park, S. K., Ri, H. C..  2017.  The Relation Between Local Hysteresis Losses and Remanent Magnetic Fields in HTSC Films. IEEE Transactions on Applied Superconductivity. 27:1–4.

Various critical state models have been developed to understand the hysteresis loss mechanism of high-temperature superconducting (HTSC) films. The analytic relation between the hysteresis loss and the remanent field was obtained based on Bean's critical state model for thin films in the full-penetration case. Furthermore, numerical calculation of local hysteresis loops was carried out by Kim's critical state model. In this paper, we investigated local hysteresis losses for a GdBCO coated conductor by using low-temperature scanning Hall probe microscopy and reproduced the experimental results by applying the critical state model. Because of the demagnetizing effect in thin films, analysis of local hysteresis losses can be useful approach to understand of total hysteresis losses.

Al-Dhaqm, A., Razak, S. A., Ikuesan, R. A., Kebande, V. R., Siddique, K..  2020.  A Review of Mobile Forensic Investigation Process Models. IEEE Access. 8:173359—173375.
Mobile Forensics (MF) field uses prescribed scientific approaches with a focus on recovering Potential Digital Evidence (PDE) from mobile devices leveraging forensic techniques. Consequently, increased proliferation, mobile-based services, and the need for new requirements have led to the development of the MF field, which has in the recent past become an area of importance. In this article, the authors take a step to conduct a review on Mobile Forensics Investigation Process Models (MFIPMs) as a step towards uncovering the MF transitions as well as identifying open and future challenges. Based on the study conducted in this article, a review of the literature revealed that there are a few MFIPMs that are designed for solving certain mobile scenarios, with a variety of concepts, investigation processes, activities, and tasks. A total of 100 MFIPMs were reviewed, to present an inclusive and up-to-date background of MFIPMs. Also, this study proposes a Harmonized Mobile Forensic Investigation Process Model (HMFIPM) for the MF field to unify and structure whole redundant investigation processes of the MF field. The paper also goes the extra mile to discuss the state of the art of mobile forensic tools, open and future challenges from a generic standpoint. The results of this study find direct relevance to forensic practitioners and researchers who could leverage the comprehensiveness of the developed processes for investigation.
Zonouz, S.A., Khurana, H., Sanders, W.H., Yardley, T.M..  2014.  RRE: A Game-Theoretic Intrusion Response and Recovery Engine. Parallel and Distributed Systems, IEEE Transactions on. 25:395-406.

Preserving the availability and integrity of networked computing systems in the face of fast-spreading intrusions requires advances not only in detection algorithms, but also in automated response techniques. In this paper, we propose a new approach to automated response called the response and recovery engine (RRE). Our engine employs a game-theoretic response strategy against adversaries modeled as opponents in a two-player Stackelberg stochastic game. The RRE applies attack-response trees (ART) to analyze undesired system-level security events within host computers and their countermeasures using Boolean logic to combine lower level attack consequences. In addition, the RRE accounts for uncertainties in intrusion detection alert notifications. The RRE then chooses optimal response actions by solving a partially observable competitive Markov decision process that is automatically derived from attack-response trees. To support network-level multiobjective response selection and consider possibly conflicting network security properties, we employ fuzzy logic theory to calculate the network-level security metric values, i.e., security levels of the system's current and potentially future states in each stage of the game. In particular, inputs to the network-level game-theoretic response selection engine, are first fed into the fuzzy system that is in charge of a nonlinear inference and quantitative ranking of the possible actions using its previously defined fuzzy rule set. Consequently, the optimal network-level response actions are chosen through a game-theoretic optimization process. Experimental results show that the RRE, using Snort's alerts, can protect large networks for which attack-response trees have more than 500 nodes.

Cao, L., Kang, Y., Wu, Q., Wu, R., Guo, X., Feng, T..  2020.  Searchable encryption cloud storage with dynamic data update to support efficient policy hiding. China Communications. 17:153–163.
Ciphertext policy attribute based encryption (CP-ABE) can provide high finegrained access control for cloud storage. However, it needs to solve problems such as property privacy protection, ciphertext search and data update in the application process. Therefore, based on CP-ABE scheme, this paper proposes a dynamically updatable searchable encryption cloud storage (DUSECS) scheme. Using the characteristics of homomorphic encryption, the encrypted data is compared to achieve efficient hiding policy. Meanwhile, adopting linked list structure, the DUSECS scheme realizes the dynamic data update and integrity detection, and the search encryption against keyword guessing attacks is achieved by combining homomorphic encryption with aggregation algorithm. The analysis of security and performance shows that the scheme is secure and efficient.
Kloft, Marius, Laskov, Pavel.  2012.  Security Analysis of Online Centroid Anomaly Detection. J. Mach. Learn. Res.. 13:3681–3724.

Security issues are crucial in a number of machine learning applications, especially in scenarios dealing with human activity rather than natural phenomena (e.g., information ranking, spam detection, malware detection, etc.). In such cases, learning algorithms may have to cope with manipulated data aimed at hampering decision making. Although some previous work addressed the issue of handling malicious data in the context of supervised learning, very little is known about the behavior of anomaly detection methods in such scenarios. In this contribution, we analyze the performance of a particular method–online centroid anomaly detection–in the presence of adversarial noise. Our analysis addresses the following security-related issues: formalization of learning and attack processes, derivation of an optimal attack, and analysis of attack efficiency and limitations. We derive bounds on the effectiveness of a poisoning attack against centroid anomaly detection under different conditions: attacker's full or limited control over the traffic and bounded false positive rate. Our bounds show that whereas a poisoning attack can be effectively staged in the unconstrained case, it can be made arbitrarily difficult (a strict upper bound on the attacker's gain) if external constraints are properly used. Our experimental evaluation, carried out on real traces of HTTP and exploit traffic, confirms the tightness of our theoretical bounds and the practicality of our protection mechanisms.

Ali, Sk Subidh, Ibrahim, Mohamed, Sinanoglu, Ozgur, Chakrabarty, Krishnendu, Karri, Ramesh.  2016.  Security Assessment of Cyberphysical Digital Microfluidic Biochips. IEEE/ACM Trans. Comput. Biol. Bioinformatics. 13:445–458.

A digital microfluidic biochip (DMFB) is an emerging technology that enables miniaturized analysis systems for point-of-care clinical diagnostics, DNA sequencing, and environmental monitoring. A DMFB reduces the rate of sample and reagent consumption, and automates the analysis of assays. In this paper, we provide the first assessment of the security vulnerabilities of DMFBs. We identify result-manipulation attacks on a DMFB that maliciously alter the assay outcomes. Two practical result-manipulation attacks are shown on a DMFB platform performing enzymatic glucose assay on serum. In the first attack, the attacker adjusts the concentration of the glucose sample and thereby modifies the final result. In the second attack, the attacker tampers with the calibration curve of the assay operation. We then identify denial-of-service attacks, where the attacker can disrupt the assay operation by tampering either with the droplet-routing algorithm or with the actuation sequence. We demonstrate these attacks using a digital microfluidic synthesis simulator. The results show that the attacks are easy to implement and hard to detect. Therefore, this work highlights the need for effective protections against malicious modifications in DMFBs.

Kebin Liu, Qiang Ma, Wei Gong, Xin Miao, Yunhao Liu.  2014.  Self-Diagnosis for Detecting System Failures in Large-Scale Wireless Sensor Networks. Wireless Communications, IEEE Transactions on. 13:5535-5545.

Existing approaches to diagnosing sensor networks are generally sink based, which rely on actively pulling state information from sensor nodes so as to conduct centralized analysis. First, sink-based tools incur huge communication overhead to the traffic-sensitive sensor networks. Second, due to the unreliable wireless communications, sink often obtains incomplete and suspicious information, leading to inaccurate judgments. Even worse, it is always more difficult to obtain state information from problematic or critical regions. To address the given issues, we present a novel self-diagnosis approach, which encourages each single sensor to join the fault decision process. We design a series of fault detectors through which multiple nodes can cooperate with each other in a diagnosis task. Fault detectors encode the diagnosis process to state transitions. Each sensor can participate in the diagnosis by transiting the detector's current state to a new state based on local evidences and then passing the detector to other nodes. Having sufficient evidences, the fault detector achieves the Accept state and outputs a final diagnosis report. We examine the performance of our self-diagnosis tool called TinyD2 on a 100-node indoor testbed and conduct field studies in the GreenOrbs system, which is an operational sensor network with 330 nodes outdoor.