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Journal Article
Ghazo, A. T. Al, Ibrahim, M., Ren, H., Kumar, R..  2020.  A2G2V: Automatic Attack Graph Generation and Visualization and Its Applications to Computer and SCADA Networks. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 50:3488–3498.
Securing cyber-physical systems (CPS) and Internet of Things (IoT) systems requires the identification of how interdependence among existing atomic vulnerabilities may be exploited by an adversary to stitch together an attack that can compromise the system. Therefore, accurate attack graphs play a significant role in systems security. A manual construction of the attack graphs is tedious and error-prone, this paper proposes a model-checking-based automated attack graph generator and visualizer (A2G2V). The proposed A2G2V algorithm uses existing model-checking tools, an architecture description tool, and our own code to generate an attack graph that enumerates the set of all possible sequences in which atomic-level vulnerabilities can be exploited to compromise system security. The architecture description tool captures a formal representation of the networked system, its atomic vulnerabilities, their pre-and post-conditions, and security property of interest. A model-checker is employed to automatically identify an attack sequence in the form of a counterexample. Our own code integrated with the model-checker parses the counterexamples, encodes those for specification relaxation, and iterates until all attack sequences are revealed. Finally, a visualization tool has also been incorporated with A2G2V to generate a graphical representation of the generated attack graph. The results are illustrated through application to computer as well as control (SCADA) networks.
Mitchell, R., Ing-Ray Chen.  2014.  Adaptive Intrusion Detection of Malicious Unmanned Air Vehicles Using Behavior Rule Specifications. Systems, Man, and Cybernetics: Systems, IEEE Transactions on. 44:593-604.


In this paper, we propose an adaptive specification-based intrusion detection system (IDS) for detecting malicious unmanned air vehicles (UAVs) in an airborne system in which continuity of operation is of the utmost importance. An IDS audits UAVs in a distributed system to determine if the UAVs are functioning normally or are operating under malicious attacks. We investigate the impact of reckless, random, and opportunistic attacker behaviors (modes which many historical cyber attacks have used) on the effectiveness of our behavior rule-based UAV IDS (BRUIDS) which bases its audit on behavior rules to quickly assess the survivability of the UAV facing malicious attacks. Through a comparative analysis with the multiagent system/ant-colony clustering model, we demonstrate a high detection accuracy of BRUIDS for compliant performance. By adjusting the detection strength, BRUIDS can effectively trade higher false positives for lower false negatives to cope with more sophisticated random and opportunistic attackers to support ultrasafe and secure UAV applications.
 

Ivanov, Radoslav, Pajic, Miroslav, Lee, Insup.  2016.  Attack-Resilient Sensor Fusion for Safety-Critical Cyber-Physical Systems. ACM Trans. Embed. Comput. Syst.. 15:21:1–21:24.

This article focuses on the design of safe and attack-resilient Cyber-Physical Systems (CPS) equipped with multiple sensors measuring the same physical variable. A malicious attacker may be able to disrupt system performance through compromising a subset of these sensors. Consequently, we develop a precise and resilient sensor fusion algorithm that combines the data received from all sensors by taking into account their specified precisions. In particular, we note that in the presence of a shared bus, in which messages are broadcast to all nodes in the network, the attacker’s impact depends on what sensors he has seen before sending the corrupted measurements. Therefore, we explore the effects of communication schedules on the performance of sensor fusion and provide theoretical and experimental results advocating for the use of the Ascending schedule, which orders sensor transmissions according to their precision starting from the most precise. In addition, to improve the accuracy of the sensor fusion algorithm, we consider the dynamics of the system in order to incorporate past measurements at the current time. Possible ways of mapping sensor measurement history are investigated in the article and are compared in terms of the confidence in the final output of the sensor fusion. We show that the precision of the algorithm using history is never worse than the no-history one, while the benefits may be significant. Furthermore, we utilize the complementary properties of the two methods and show that their combination results in a more precise and resilient algorithm. Finally, we validate our approach in simulation and experiments on a real unmanned ground robot.

Al-Dhaqm, A., Razak, S. A., Dampier, D. A., Choo, K. R., Siddique, K., Ikuesan, R. A., Alqarni, A., Kebande, V. R..  2020.  Categorization and Organization of Database Forensic Investigation Processes. IEEE Access. 8:112846—112858.
Database forensic investigation (DBFI) is an important area of research within digital forensics. It's importance is growing as digital data becomes more extensive and commonplace. The challenges associated with DBFI are numerous, and one of the challenges is the lack of a harmonized DBFI process for investigators to follow. In this paper, therefore, we conduct a survey of existing literature with the hope of understanding the body of work already accomplished. Furthermore, we build on the existing literature to present a harmonized DBFI process using design science research methodology. This harmonized DBFI process has been developed based on three key categories (i.e. planning, preparation and pre-response, acquisition and preservation, and analysis and reconstruction). Furthermore, the DBFI has been designed to avoid confusion or ambiguity, as well as providing practitioners with a systematic method of performing DBFI with a higher degree of certainty.
Rommel García, Ignacio Algredo-Badillo, Miguel Morales-Sandoval, Claudia Feregrino-Uribe, René Cumplido.  2014.  A compact FPGA-based processor for the Secure Hash Algorithm SHA-256. Computers & Electrical Engineering. 40:194-202.

This work reports an efficient and compact FPGA processor for the SHA-256 algorithm. The novel processor architecture is based on a custom datapath that exploits the reusing of modules, having as main component a 4-input Arithmetic-Logic Unit not previously reported. This ALU is designed as a result of studying the type of operations in the SHA algorithm, their execution sequence and the associated dataflow. The processor hardware architecture was modeled in VHDL and implemented in FPGAs. The results obtained from the implementation in a Virtex5 device demonstrate that the proposed design uses fewer resources achieving higher performance and efficiency, outperforming previous approaches in the literature focused on compact designs, saving around 60% FPGA slices with an increased throughput (Mbps) and efficiency (Mbps/Slice). The proposed SHA processor is well suited for applications like Wi-Fi, TMP (Trusted Mobile Platform), and MTM (Mobile Trusted Module), where the data transfer speed is around 50 Mbps.

40th-year commemorative issue

Hong, Junho, Nuqui, Reynaldo F., Kondabathini, Anil, Ishchenko, Dmitry, Martin, Aaron.  2019.  Cyber Attack Resilient Distance Protection and Circuit Breaker Control for Digital Substations. IEEE Transactions on Industrial Informatics. 15:4332—4341.
This paper proposes new concepts for detecting and mitigating cyber attacks on substation automation systems by domain-based cyber-physical security solutions. The proposed methods form the basis of a distributed security domain layer that enables protection devices to collaboratively defend against cyber attacks at substations. The methods utilize protection coordination principles to cross check protection setting changes and can run real-time power system analysis to evaluate the impact of the control commands. The transient fault signature (TFS)-based cross-correlation coefficient algorithm has been proposed to detect the false sampled values data injection attack. The proposed functions were verified in a hardware-in-the-loop (HIL) simulation using commercial relays and a real-time digital simulator (RTDS). Various types of cyber intrusions are tested using this test bed to evaluate the consequences and impacts of cyber attacks to power grid as well as to validate the performance of the proposed research-grade cyber attack mitigation functions.
Iwendi, C., Uddin, M., Ansere, J. A., Nkurunziza, P., Anajemba, J. H., Bashir, A. K..  2018.  On Detection of Sybil Attack in Large-Scale VANETs Using Spider-Monkey Technique. IEEE Access. 6:47258–47267.
Sybil security threat in vehicular ad hoc networks (VANETs) has attracted much attention in recent times. The attacker introduces malicious nodes with multiple identities. As the roadside unit fails to synchronize its clock with legitimate vehicles, unintended vehicles are identified, and therefore erroneous messages will be sent to them. This paper proposes a novel biologically inspired spider-monkey time synchronization technique for large-scale VANETs to boost packet delivery time synchronization at minimized energy consumption. The proposed technique is based on the metaheuristic stimulated framework approach by the natural spider-monkey behavior. An artificial spider-monkey technique is used to examine the Sybil attacking strategies on VANETs to predict the number of vehicular collisions in a densely deployed challenge zone. Furthermore, this paper proposes the pseudocode algorithm randomly distributed for energy-efficient time synchronization in two-way packet delivery scenarios to evaluate the clock offset and the propagation delay in transmitting the packet beacon message to destination vehicles correctly. The performances of the proposed technique are compared with existing protocols. It performs better over long transmission distances for the detection of Sybil in dynamic VANETs' system in terms of measurement precision, intrusion detection rate, and energy efficiency.
Iltaf, Naima, Ghafoor, Abdul, Zia, Usman, Hussain, Mukhtar.  2014.  An Effective Model for Indirect Trust Computation in Pervasive Computing Environment. Wirel. Pers. Commun.. 75:1689–1713.

The performance of indirect trust computation models (based on recommendations) can be easily compromised due to the subjective and social-based prejudice of the provided recommendations. Eradicating the influence of such recommendation remains an important and challenging issue in indirect trust computation models. An effective model for indirect trust computation is proposed which is capable of identifying dishonest recommendations. Dishonest recommendations are identified by using deviation based detecting technique. The concept of measuring the credibility of recommendation (rather than credibility of recommender) using fuzzy inference engine is also proposed to determine the influence of each honest recommendation. The proposed model has been compared with other existing evolutionary recommendation models in this field, and it is shown that the model is more accurate in measuring the trustworthiness of unknown entity.

Stan, O., Bitton, R., Ezrets, M., Dadon, M., Inokuchi, M., Yoshinobu, O., Tomohiko, Y., Elovici, Y., Shabtai, A..  2020.  Extending Attack Graphs to Represent Cyber-Attacks in Communication Protocols and Modern IT Networks. IEEE Transactions on Dependable and Secure Computing. :1–1.
An attack graph is a method used to enumerate the possible paths that an attacker can take in the organizational network. MulVAL is a known open-source framework used to automatically generate attack graphs. MulVAL's default modeling has two main shortcomings. First, it lacks the ability to represent network protocol vulnerabilities, and thus it cannot be used to model common network attacks, such as ARP poisoning. Second, it does not support advanced types of communication, such as wireless and bus communication, and thus it cannot be used to model cyber-attacks on networks that include IoT devices or industrial components. In this paper, we present an extended network security model for MulVAL that: (1) considers the physical network topology, (2) supports short-range communication protocols, (3) models vulnerabilities in the design of network protocols, and (4) models specific industrial communication architectures. Using the proposed extensions, we were able to model multiple attack techniques including: spoofing, man-in-the-middle, and denial of service attacks, as well as attacks on advanced types of communication. We demonstrate the proposed model in a testbed which implements a simplified network architecture comprised of both IT and industrial components
Ignacio X. Dominguez, Prairie Rose Goodwin, David L. Roberts, Robert St. Amant.  2016.  Human Subtlety Proofs: Using Computer Games to Model Cognitive Processes for Cybersecurity. International Journal of Human–Computer Interaction. :null.

AbstractThis article describes an emerging direction in the intersection between human-computer interaction and cognitive science: the use of cognitive models to give insight into the challenges of cybersecurity. The article gives a brief overview of work in different areas of cybersecurity where cognitive modeling research plays a role, with regard to direct interaction between end users and computer systems and with regard to the needs of security analysts working behind the scenes. The problem of distinguishing between human users and automated agents (bots) interacting with computer systems is introduced, as well as ongoing efforts toward building Human Subtlety Proofs, persistent and unobtrusive windows into human cognition with direct application to cybersecurity. Two computer games are described, proxies to illustrate different ways in which cognitive modeling can potentially contribute to the development of HSPs and similar cybersecurity applications.

Ignacio X. Dominguez, Prairie Rose Goodwin,, David L. Roberts,, Robert St. Amant.  2016.  Human Subtlety Proofs: Using Computer Games to Model Cognitive Processes for Cybersecurity. International Journal of Human-Computer Interaction, special issue on Cognitive Foundations for Human-Computer Interaction.

This article describes an emerging direction in the intersection between human–computer interaction and cognitive science: the use of cognitive models to give insight into the challenges of cybersecurity (cyber-SA). The article gives a brief overview of work in different areas of cyber-SA where cognitive modeling research plays a role, with regard to direct interaction between end users and computer systems and with regard to the needs of security analysts working behind the scenes. The problem of distinguishing between human users and automated agents (bots) interacting with computer systems is introduced, as well as ongoing efforts toward building Human Subtlety Proofs (HSPs), persistent and unobtrusive windows into human cognition with direct application to cyber-SA. Two computer games are described, proxies to illustrate different ways in which cognitive modeling can potentially contribute to the development of HSPs and similar cyber-SA applications.

 

Online access

Ignacio X. Dominguez, Prairie Rose Goodwin, David L. Roberts, Robert St. Amant.  2016.  Human Subtlety Proofs: Using Computer Games to Model Cognitive Processes for Cybersecurity. International Journal of Human–Computer Interaction.

This article describes an emerging direction in the intersection between human-computer interaction and cognitive science: the use of cognitive models to give insight into the challenges of cybersecurity. The article gives a brief overview of work in different areas of cybersecurity where cognitive modeling research plays a role, with regard to direct interaction between end users and computer systems and with regard to the needs of security analysts working behind the scenes. The problem of distinguishing between human users and automated agents (bots) interacting with computer systems is introduced, as well as ongoing efforts toward building Human Subtlety Proofs, persistent and unobtrusive windows into human cognition with direct application to cybersecurity. Two computer games are described, proxies to illustrate different ways in which cognitive modeling can potentially contribute to the development of HSPs and similar cybersecurity applications.

Yang, Y., McLaughlin, K., Sezer, S., Littler, T., Im, E.G., Pranggono, B., Wang, H.F..  2014.  Multiattribute SCADA-Specific Intrusion Detection System for Power Networks. Power Delivery, IEEE Transactions on. 29:1092-1102.

The increased interconnectivity and complexity of supervisory control and data acquisition (SCADA) systems in power system networks has exposed the systems to a multitude of potential vulnerabilities. In this paper, we present a novel approach for a next-generation SCADA-specific intrusion detection system (IDS). The proposed system analyzes multiple attributes in order to provide a comprehensive solution that is able to mitigate varied cyber-attack threats. The multiattribute IDS comprises a heterogeneous white list and behavior-based concept in order to make SCADA cybersystems more secure. This paper also proposes a multilayer cyber-security framework based on IDS for protecting SCADA cybersecurity in smart grids without compromising the availability of normal data. In addition, this paper presents a SCADA-specific cybersecurity testbed to investigate simulated attacks, which has been used in this paper to validate the proposed approach.

Insinga, A. R., Bjørk, R., Smith, A., Bahl, C. R. H..  2016.  Optimally Segmented Permanent Magnet Structures. IEEE Transactions on Magnetics. 52:1–6.

We present an optimization approach that can be employed to calculate the globally optimal segmentation of a 2-D magnetic system into uniformly magnetized pieces. For each segment, the algorithm calculates the optimal shape and the optimal direction of the remanent flux density vector, with respect to a linear objective functional. We illustrate the approach with results for magnet design problems from different areas, such as a permanent magnet electric motor, a beam-focusing quadrupole magnet for particle accelerators, and a rotary device for magnetic refrigeration.

Carter, K.M., Idika, N., Streilein, W.W..  2014.  Probabilistic Threat Propagation for Network Security. Information Forensics and Security, IEEE Transactions on. 9:1394-1405.

Techniques for network security analysis have historically focused on the actions of the network hosts. Outside of forensic analysis, little has been done to detect or predict malicious or infected nodes strictly based on their association with other known malicious nodes. This methodology is highly prevalent in the graph analytics world, however, and is referred to as community detection. In this paper, we present a method for detecting malicious and infected nodes on both monitored networks and the external Internet. We leverage prior community detection and graphical modeling work by propagating threat probabilities across network nodes, given an initial set of known malicious nodes. We enhance prior work by employing constraints that remove the adverse effect of cyclic propagation that is a byproduct of current methods. We demonstrate the effectiveness of probabilistic threat propagation on the tasks of detecting botnets and malicious web destinations.

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.
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.

Iwamoto, M., Ohta, K., Shikata, J..  2018.  Security Formalizations and Their Relationships for Encryption and Key Agreement in Information-Theoretic Cryptography. IEEE Transactions on Information Theory. 64:654–685.
This paper analyzes the formalizations of information-theoretic security for the fundamental primitives in cryptography: symmetric-key encryption and key agreement. Revisiting the previous results, we can formalize information-theoretic security using different methods, by extending Shannon's perfect secrecy, by information-theoretic analogues of indistinguishability and semantic security, and by the frameworks for composability of protocols. We show the relationships among the security formalizations and obtain the following results. First, in the case of encryption, there are significant gaps among the formalizations, and a certain type of relaxed perfect secrecy or a variant of information-theoretic indistinguishability is the strongest notion. Second, in the case of key agreement, there are significant gaps among the formalizations, and a certain type of relaxed perfect secrecy is the strongest notion. In particular, in both encryption and key agreement, the formalization of composable security is not stronger than any other formalizations. Furthermore, as an application of the relationships in encryption and key agreement, we simultaneously derive a family of lower bounds on the size of secret keys and security quantities required under the above formalizations, which also implies the importance and usefulness of the relationships.
Park, Junkil, Ivanov, Radoslav, Weimer, James, Pajic, Miroslav, Son, Sang Hyuk, Lee, Insup.  2017.  Security of Cyber-Physical Systems in the Presence of Transient Sensor Faults. ACM Trans. Cyber-Phys. Syst.. 1:15:1–15:23.
This article is concerned with the security of modern Cyber-Physical Systems in the presence of transient sensor faults. We consider a system with multiple sensors measuring the same physical variable, where each sensor provides an interval with all possible values of the true state. We note that some sensors might output faulty readings and others may be controlled by a malicious attacker. Differing from previous works, in this article, we aim to distinguish between faults and attacks and develop an attack detection algorithm for the latter only. To do this, we note that there are two kinds of faults—transient and permanent; the former are benign and short-lived, whereas the latter may have dangerous consequences on system performance. We argue that sensors have an underlying transient fault model that quantifies the amount of time in which transient faults can occur. In addition, we provide a framework for developing such a model if it is not provided by manufacturers. Attacks can manifest as either transient or permanent faults depending on the attacker’s goal. We provide different techniques for handling each kind. For the former, we analyze the worst-case performance of sensor fusion over time given each sensor’s transient fault model and develop a filtered fusion interval that is guaranteed to contain the true value and is bounded in size. To deal with attacks that do not comply with sensors’ transient fault models, we propose a sound attack detection algorithm based on pairwise inconsistencies between sensor measurements. Finally, we provide a real-data case study on an unmanned ground vehicle to evaluate the various aspects of this article.
Conference Paper
Karras, Panagiotis, Nikitin, Artyom, Saad, Muhammad, Bhatt, Rudrika, Antyukhov, Denis, Idreos, Stratos.  2016.  Adaptive Indexing over Encrypted Numeric Data. Proceedings of the 2016 International Conference on Management of Data. :171–183.

Today, outsourcing query processing tasks to remote cloud servers becomes a viable option; such outsourcing calls for encrypting data stored at the server so as to render it secure against eavesdropping adversaries and/or an honest-but-curious server itself. At the same time, to be efficiently managed, outsourced data should be indexed, and even adaptively so, as a side-effect of query processing. Computationally heavy encryption schemes render such outsourcing unattractive; an alternative, Order-Preserving Encryption Scheme (OPES), intentionally preserves and reveals the order in the data, hence is unattractive from the security viewpoint. In this paper, we propose and analyze a scheme for lightweight and indexable encryption, based on linear-algebra operations. Our scheme provides higher security than OPES and allows for range and point queries to be efficiently evaluated over encrypted numeric data, with decryption performed at the client side. We implement a prototype that performs incremental, query-triggered adaptive indexing over encrypted numeric data based on this scheme, without leaking order information in advance, and without prohibitive overhead, as our extensive experimental study demonstrates.

Lee, Won-Jong, Hwang, Seok Joong, Shin, Youngsam, Ryu, Soojung, Ihm, Insung.  2016.  Adaptive Multi-rate Ray Sampling on Mobile Ray Tracing GPU. SIGGRAPH ASIA 2016 Mobile Graphics and Interactive Applications. :3:1–3:6.
We present an adaptive multi-rate ray sampling algorithm targeting mobile ray-tracing GPUs. We efficiently combine two existing algorithms, adaptive supersampling and undersampling, into a single framework targeting ray-tracing GPUs and extend it to a new multi-rate sampling scheme by utilizing tile-based rendering and frame-to-frame coherency. The experimental results show that our implementation is a versatile solution for future ray-tracing GPUs as it provides up to 2.98 times better efficiency in terms of performance per Watt by reducing the number of rays to be fed into the dedicated hardware and minimizing the memory operations.
Usama, Muhammad, Asim, Muhammad, Qadir, Junaid, Al-Fuqaha, Ala, Imran, Muhammad Ali.  2019.  Adversarial Machine Learning Attack on Modulation Classification. 2019 UK/ China Emerging Technologies (UCET). :1—4.

Modulation classification is an important component of cognitive self-driving networks. Recently many ML-based modulation classification methods have been proposed. We have evaluated the robustness of 9 ML-based modulation classifiers against the powerful Carlini & Wagner (C-W) attack and showed that the current ML-based modulation classifiers do not provide any deterrence against adversarial ML examples. To the best of our knowledge, we are the first to report the results of the application of the C-W attack for creating adversarial examples against various ML models for modulation classification.

Deb, Supratim, Ge, Zihui, Isukapalli, Sastry, Puthenpura, Sarat, Venkataraman, Shobha, Yan, He, Yates, Jennifer.  2017.  AESOP: Automatic Policy Learning for Predicting and Mitigating Network Service Impairments. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. :1783–1792.

Efficient management and control of modern and next-gen networks is of paramount importance as networks have to maintain highly reliable service quality whilst supporting rapid growth in traffic demand and new application services. Rapid mitigation of network service degradations is a key factor in delivering high service quality. Automation is vital to achieving rapid mitigation of issues, particularly at the network edge where the scale and diversity is the greatest. This automation involves the rapid detection, localization and (where possible) repair of service-impacting faults and performance impairments. However, the most significant challenge here is knowing what events to detect, how to correlate events to localize an issue and what mitigation actions should be performed in response to the identified issues. These are defined as policies to systems such as ECOMP. In this paper, we present AESOP, a data-driven intelligent system to facilitate automatic learning of policies and rules for triggering remedial actions in networks. AESOP combines best operational practices (domain knowledge) with a variety of measurement data to learn and validate operational policies to mitigate service issues in networks. AESOP's design addresses the following key challenges: (i) learning from high-dimensional noisy data, (ii) capturing multiple fault models, (iii) modeling the high service-cost of false positives, and (iv) accounting for the evolving network infrastructure. We present the design of our system and show results from our ongoing experiments to show the effectiveness of our policy leaning framework.

Larsson, A., Ibrahim, O., Olsson, L., Laere, J. van.  2017.  Agent based simulation of a payment system for resilience assessments. 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). :314–318.

We provide an agent based simulation model of the Swedish payment system. The simulation model is to be used to analyze the consequences of loss of functionality, or disruptions of the payment system for the food and fuel supply chains as well as the bank sector. We propose a gaming simulation approach, using a computer based role playing game, to explore the collaborative responses from the key actors, in order to evoke and facilitate collective resilience.

Bakhtiyor, Abdurakhimov, Zarif, Khudoykulov, Orif, Allanov, Ilkhom, Boykuziev.  2020.  Algebraic Cryptanalysis of O'zDSt 1105:2009 Encryption Algorithm. 2020 International Conference on Information Science and Communications Technologies (ICISCT). :1—7.
In this paper, we examine algebraic attacks on the O'zDSt 1105:2009. We begin with a brief review of the meaning of algebraic cryptanalysis, followed by an algebraic cryptanalysis of O'zDSt 1105:2009. Primarily O'zDSt 1105:2009 encryption algorithm is decomposed and each transformation in it is algebraic described separately. Then input and output of each transformation are expressed with other transformation, encryption key, plaintext and cipher text. Created equations, unknowns on it and degree of unknowns are analyzed, and then overall result is given. Based on experimental results, it is impossible to save all system of equations that describes all transformations in O'zDSt 1105:2009 standard. Because, this task requires 273 bytes for the second round. For this reason, it is advisable to evaluate the parameters of the system of algebraic equations, representing the O'zDSt 1105:2009 standard, theoretically.