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Suriano, Antonio, Striccoli, Domenico, Piro, Giuseppe, Bolla, Raffele, Boggia, Gennaro.  2020.  Attestation of Trusted and Reliable Service Function Chains in the ETSI-NFV Framework. 2020 6th IEEE Conference on Network Softwarization (NetSoft). :479—486.

The new generation of digital services are natively conceived as an ordered set of Virtual Network Functions, deployed across boundaries and organizations. In this context, security threats, variable network conditions, computational and memory capabilities and software vulnerabilities may significantly weaken the whole service chain, thus making very difficult to combat the newest kinds of attacks. It is thus extremely important to conceive a flexible (and standard-compliant) framework able to attest the trustworthiness and the reliability of each single function of a Service Function Chain. At the time of this writing, and to the best of authors knowledge, the scientific literature addressed all of these problems almost separately. To bridge this gap, this paper proposes a novel methodology, properly tailored within the ETSI-NFV framework. From one side, Software-Defined Controllers continuously monitor the properties and the performance indicators taken from networking domains of each single Virtual Network Function available in the architecture. From another side, a high-level orchestrator combines, on demand, the suitable Virtual Network Functions into a Service Function Chain, based on the user requests, targeted security requirements, and measured reliability levels. The paper concludes by further explaining the functionalities of the proposed architecture through a use case.

Suriarachchi, I., Withana, S., Plale, B..  2018.  Big Provenance Stream Processing for Data Intensive Computations. 2018 IEEE 14th International Conference on e-Science (e-Science). :245–255.
In the business and research landscape of today, data analysis consumes public and proprietary data from numerous sources, and utilizes any one or more of popular data-parallel frameworks such as Hadoop, Spark and Flink. In the Data Lake setting these frameworks co-exist. Our earlier work has shown that data provenance in Data Lakes can aid with both traceability and management. The sheer volume of fine-grained provenance generated in a multi-framework application motivates the need for on-the-fly provenance processing. We introduce a new parallel stream processing algorithm that reduces fine-grained provenance while preserving backward and forward provenance. The algorithm is resilient to provenance events arriving out-of-order. It is evaluated using several strategies for partitioning a provenance stream. The evaluation shows that the parallel algorithm performs well in processing out-of-order provenance streams, with good scalability and accuracy.
Surnin, Oleg, Hussain, Fatima, Hussain, Rasheed, Ostrovskaya, Svetlana, Polovinkin, Andrey, Lee, JooYoung, Fernando, Xavier.  2019.  Probabilistic Estimation of Honeypot Detection in Internet of Things Environment. 2019 International Conference on Computing, Networking and Communications (ICNC). :191–196.
With the emergence of the Internet of Things (IoT) and the increasing number of resource-constrained interconnected smart devices, there is a noticeable increase in the number of cyber security crimes. In the face of the possible attacks on IoT networks such as network intrusion, denial of service, spoofing and so on, there is a need to develop efficient methods to locate vulnerabilities and mitigate attacks in IoT networks. Without loss of generality, we consider only intrusion-related threats to IoT. A honeypot is a system used to understand the potential dynamic threats and act as a proactive measure to detect any intrusion into the network. It is used as a trap for intruders to control unauthorized access to the network by analyzing malicious traffic. However, a sophisticated attacker can detect the presence of a honeypot and abort the intrusion mission. Therefore it is essential for honeypots to be undetectable. In this paper, we study and analyze possible techniques for SSH and telnet honeypot detection. Moreover, we propose a new methodology for probabilistic estimation of honeypot detection and an automated software implemented this methodology.
Suryawanshi, Shubhangi, Goswami, Anurag, Patil, Pramod.  2019.  Email Spam Detection : An Empirical Comparative Study of Different ML and Ensemble Classifiers. 2019 IEEE 9th International Conference on Advanced Computing (IACC). :69–74.

Recent Development in Hardware and Software Technology for the communication email is preferred. But due to the unbidden emails, it affects communication. There is a need for detection and classification of spam email. In this present research email spam detection and classification, models are built. We have used different Machine learning classifiers like Naive Bayes, SVM, KNN, Bagging and Boosting (Adaboost), and Ensemble Classifiers with a voting mechanism. Evaluation and testing of classifiers is performed on email spam dataset from UCI Machine learning repository and Kaggle website. Different accuracy measures like Accuracy Score, F measure, Recall, Precision, Support and ROC are used. The preliminary result shows that Ensemble Classifier with a voting mechanism is the best to be used. It gives the minimum false positive rate and high accuracy.

Susanto, Stiawan, D., Arifin, M. A. S., Idris, M. Y., Budiarto, R..  2020.  IoT Botnet Malware Classification Using Weka Tool and Scikit-learn Machine Learning. 2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI). :15—20.

Botnet is one of the threats to internet network security-Botmaster in carrying out attacks on the network by relying on communication on network traffic. Internet of Things (IoT) network infrastructure consists of devices that are inexpensive, low-power, always-on, always connected to the network, and are inconspicuous and have ubiquity and inconspicuousness characteristics so that these characteristics make IoT devices an attractive target for botnet malware attacks. In identifying whether packet traffic is a malware attack or not, one can use machine learning classification methods. By using Weka and Scikit-learn analysis tools machine learning, this paper implements four machine learning algorithms, i.e.: AdaBoost, Decision Tree, Random Forest, and Naïve Bayes. Then experiments are conducted to measure the performance of the four algorithms in terms of accuracy, execution time, and false positive rate (FPR). Experiment results show that the Weka tool provides more accurate and efficient classification methods. However, in false positive rate, the use of Scikit-learn provides better results.

Susilo, Willy, Duong, Dung Hoang, Le, Huy Quoc.  2020.  Efficient Post-quantum Identity-based Encryption with Equality Test. 2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS). :633—640.
Public key encryption with equality test (PKEET) enables the testing whether two ciphertexts encrypt the same message. Identity-based encryption with equality test (IBEET) simplify the certificate management of PKEET, which leads to many potential applications such as in smart city applications or Wireless Body Area Networks. Lee et al. (ePrint 2016) proposed a generic construction of IBEET scheme in the standard model utilising a 3-level hierachy IBE together with a one-time signature scheme, which can be instantiated in lattice setting. Duong et al. (ProvSec 2019) proposed the first direct construction of IBEET in standard model from lattices. However, their scheme achieve CPA security only. In this paper, we improve the Duong et al.'s construction by proposing an IBEET in standard model which achieves CCA2 security and with smaller ciphertext and public key size.
Susilo, Willy, Chen, Rongmao, Guo, Fuchun, Yang, Guomin, Mu, Yi, Chow, Yang-Wai.  2016.  Recipient Revocable Identity-Based Broadcast Encryption: How to Revoke Some Recipients in IBBE Without Knowledge of the Plaintext. Proceedings of the 11th ACM on Asia Conference on Computer and Communications Security. :201–210.
In this paper, we present the notion of recipient-revocable identity-based broadcast encryption scheme. In this notion, a content provider will produce encrypted content and send them to a third party (which is a broadcaster). This third party will be able to revoke some identities from the ciphertext. We present a security model to capture these requirements, as well as a concrete construction. The ciphertext consists of k+3 group elements, assuming that the maximum number of revocation identities is k. That is, the ciphertext size is linear in the maximal size of R, where R is the revocation identity set. However, we say that the additional elements compared to that from an IBBE scheme are only for the revocation but not for decryption. Therefore, the ciphertext sent to the users for decryption will be of constant size (i.e.,3 group elements). Finally, we present the proof of security of our construction.
Susukailo, Vitalii, Opirskyy, Ivan, Vasylyshyn, Sviatoslav.  2020.  Analysis of the attack vectors used by threat actors during the pandemic. 2020 IEEE 15th International Conference on Computer Sciences and Information Technologies (CSIT). 2:261—264.

This article describes attacks methods, vectors and technics used by threat actors during pandemic situations in the world. Identifies common targets of threat actors and cyber-attack tactics. The article analyzes cybersecurity challenges and specifies possible solutions and improvements in cybersecurity. Defines cybersecurity controls, which should be taken against analyzed attack vectors.

Sutcliffe, Richard J., Kowarsch, Benjamin.  2016.  Closing the Barn Door: Re-Prioritizing Safety, Security, and Reliability. Proceedings of the 21st Western Canadian Conference on Computing Education. :1:1–1:15.

Past generations of software developers were well on the way to building a software engineering mindset/gestalt, preferring tools and techniques that concentrated on safety, security, reliability, and code re-usability. Computing education reflected these priorities and was, to a great extent organized around these themes, providing beginning software developers a basis for professional practice. In more recent times, economic and deadline pressures and the de-professionalism of practitioners have combined to drive a development agenda that retains little respect for quality considerations. As a result, we are now deep into a new and severe software crisis. Scarcely a day passes without news of either a debilitating data or website hack, or the failure of a mega-software project. Vendors, individual developers, and possibly educators can anticipate an equally destructive flood of malpractice litigation, for the argument that they systematically and recklessly ignored known best development practice of long standing is irrefutable. Yet we continue to instruct using methods and to employ development tools we know, or ought to know, are inherently insecure, unreliable, and unsafe, and that produce software of like ilk. The authors call for a renewed professional and educational focus on software quality, focusing on redesigned tools that enable and encourage known best practice, combined with reformed educational practices that emphasize writing human readable, safe, secure, and reliable software. Practitioners can only deploy sound management techniques, appropriate tool choice, and best practice development methodologies such as thorough planning and specification, scope management, factorization, modularity, safety, appropriate team and testing strategies, if those ideas and techniques are embedded in the curriculum from the beginning. The authors have instantiated their ideas in the form of their highly disciplined new version of Niklaus Wirth's 1980s Modula-2 programming notation under the working moniker Modula-2 R10. They are now working on an implementation that will be released under a liberal open source license in the hope that it will assist in reforming the CS curriculum around a best practices core so as to empower would-be professionals with the intellectual and practical mindset to begin resolving the software crisis. They acknowledge there is no single software engineering silver bullet, but assert that professional techniques can be inculcated throughout a student's four-year university tenure, and if implemented in the workplace, these can greatly reduce the likelihood of multiplied IT failures at the hands of our graduates. The authors maintain that professional excellence is a necessary mindset, a habit of self-discipline that must be intentionally embedded in all aspects of one's education, and subsequently drive all aspects of one's practice, including, but by no means limited to, the choice and use of programming tools.

Sutradhar, M. R., Sultana, N., Dey, H., Arif, H..  2018.  A New Version of Kerberos Authentication Protocol Using ECC and Threshold Cryptography for Cloud Security. 2018 Joint 7th International Conference on Informatics, Electronics Vision (ICIEV) and 2018 2nd International Conference on Imaging, Vision Pattern Recognition (icIVPR). :239–244.

Dependency on cloud computing are increasing day by day due to its beneficial aspects. As day by day we are relying on cloud computing, the securities issues are coming up. There are lots of security protocols but now-a-days those protocol are not secured enough to provide a high security. One of those protocols which were once highly secured, is Kerberos authentication protocol. With the advancement of technology, Kerberos authentication protocol is no longer as secured as it was before. Many authors have thought about the improvement of Kerberos authentication protocol and consequently they have proposed different types of protocol models by using a renowned public key cryptography named RSA cryptography. Though RSA cryptography is good to some extent but this cryptography has some flaws that make this cryptography less secured as well as less efficient. In this paper, we are combining Elliptic Curve Cryptography (ECC) as well as Threshold Cryptography to create a new version of Kerberos authentication protocol. Our proposed model will provide secure transaction of data which will not only be hard to break but also increase memory efficiency, cost efficiency, and reduce the burden of computation.

Sutton, A., Samavi, R., Doyle, T. E., Koff, D..  2018.  Digitized Trust in Human-in-the-Loop Health Research. 2018 16th Annual Conference on Privacy, Security and Trust (PST). :1–10.
In this paper, we propose an architecture that utilizes blockchain technology for enabling verifiable trust in collaborative health research environments. The architecture supports the human-in-the-loop paradigm for health research by establishing trust between participants, including human researchers and AI systems, by making all data transformations transparent and verifiable by all participants. We define the trustworthiness of the system and provide an analysis of the architecture in terms of trust requirements. We then evaluate our architecture by analyzing its resiliency to common security threats and through an experimental realization.
Sutton, Sara, Bond, Benjamin, Tahiri, Sementa, Rrushi, Julian.  2019.  Countering Malware Via Decoy Processes with Improved Resource Utilization Consistency. 2019 First IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA). :110—119.
The concept of a decoy process is a new development of defensive deception beyond traditional honeypots. Decoy processes can be exceptionally effective in detecting malware, directly upon contact or by redirecting malware to decoy I/O. A key requirement is that they resemble their real counterparts very closely to withstand adversarial probes by threat actors. To be usable, decoy processes need to consume only a small fraction of the resources consumed by their real counterparts. Our contribution in this paper is twofold. We attack the resource utilization consistency of decoy processes provided by a neural network with a heatmap training mechanism, which we find to be insufficiently trained. We then devise machine learning over control flow graphs that improves the heatmap training mechanism. A neural network retrained by our work shows higher accuracy and defeats our attacks without a significant increase in its own resource utilization.
Suwannasa, A., Broadbent, M., Mauthe, A..  2020.  Vicinity-based Replica Finding in Named Data Networking. 2020 International Conference on Information Networking (ICOIN). :146–151.
In Named Data Networking (NDN) architectures, a content object is located according to the content's identifier and can be retrieved from all nodes that hold a replica of the content. The default forwarding strategy of NDN is to forward an Interest packet along the default path from the requester to the server to find a content object according to its name prefix. However, the best path may not be the default path, since content might also be located nearby. Hence, the default strategy could result in a sub-optimal delivery efficiency. To address this issue we introduce a vicinity-based replica finding scheme. This is based on the observation that content objects might be requested several times. Therefore, replicas can be often cached within a particular neighbourhood and thus it might be efficient to specifically look for them in order to improve the content delivery performance. Within this paper, we evaluate the optimal size of the vicinity within which content should be located (i.e. the distance between the requester and its neighbours that are considered within the content search). We also compare the proposed scheme with the default NDN forwarding strategy with respect to replica finding efficiency and network overhead. Using the proposed scheme, we demonstrate that the replica finding mechanism reduces the delivery time effectively with acceptable overhead costs.
Suwansrikham, P., She, K..  2018.  Asymmetric Secure Storage Scheme for Big Data on Multiple Cloud Providers. 2018 IEEE 4th International Conference on Big Data Security on Cloud (BigDataSecurity), IEEE International Conference on High Performance and Smart Computing, (HPSC) and IEEE International Conference on Intelligent Data and Security (IDS). :121-125.

Recently, cloud computing is an emerging technology along with big data. Both technologies come together. Due to the enormous size of data in big data, it is impossible to store them in local storage. Alternatively, even we want to store them locally, we have to spend much money to create bit data center. One way to save money is store big data in cloud storage service. Cloud storage service provides users space and security to store the file. However, relying on single cloud storage may cause trouble for the customer. CSP may stop its service anytime. It is too risky if data owner hosts his file only single CSP. Also, the CSP is the third party that user have to trust without verification. After deploying his file to CSP, the user does not know who access his file. Even CSP provides a security mechanism to prevent outsider attack. However, how user ensure that there is no insider attack to steal or corrupt the file. This research proposes the way to minimize the risk, ensure data privacy, also accessing control. The big data file is split into chunks and distributed to multiple cloud storage provider. Even there is insider attack; the attacker gets only part of the file. He cannot reconstruct the whole file. After splitting the file, metadata is generated. Metadata is a place to keep chunk information, includes, chunk locations, access path, username and password of data owner to connect each CSP. Asymmetric security concept is applied to this research. The metadata will be encrypted and transfer to the user who requests to access the file. The file accessing, monitoring, metadata transferring is functions of dew computing which is an intermediate server between the users and cloud service.

Suzanna, Sia Xin Yun, Anthony, Li Lianjie.  2017.  Hierarchical Module Classification in Mixed-Initiative Conversational Agent System. Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. :2535–2538.

Our operational context is a task-oriented dialog system where no single module satisfactorily addresses the range of conversational queries from humans. Such systems must be equipped with a range of technologies to address semantic, factual, task-oriented, open domain conversations using rule-based, semantic-web, traditional machine learning and deep learning. This raises two key challenges. First, the modules need to be managed and selected appropriately. Second, the complexity of troubleshooting on such systems is high. We address these challenges with a mixed-initiative model that controls conversational logic through hierarchical classification. We also developed an interface to increase interpretability for operators and to aggregate module performance.

Suzic, B., Latinovic, M..  2020.  Rethinking Authorization Management of Web-APIs. 2020 IEEE International Conference on Pervasive Computing and Communications (PerCom). :1—10.

Service providers typically utilize Web APIs to enable the sharing of tenant data and resources with numerous third party web, cloud, and mobile applications. Security mechanisms such as OAuth 2.0 and API keys are commonly applied to manage authorization aspects of such integrations. However, these mechanisms impose functional and security drawbacks both for service providers and their users due to their static design, coarse and context insensitive capabilities, and weak interoperability. Implementing secure, feature-rich, and flexible data sharing services still poses a challenge that many providers face in the process of opening their interfaces to the public.To address these issues, we design the framework that allows pluggable and transparent externalization of authorization functionality for service providers and flexibility in defining and managing security aspects of resource sharing with third parties for their users. Our solution applies a holistic perspective that considers service descriptions, data fragments, security policies, as well as system interactions and states as an integrated space dynamically exposed and collaboratively accessed by agents residing across organizational boundaries.In this work we present design aspects of our contribution and illustrate its practical implementation by analyzing case scenario involving resource sharing of a popular service.

Suzuki, Ippei, Ochiai, Yoichi.  2017.  Unphotogenic Light: High-Speed Projection Method to Prevent Secret Photography by Small Cameras. ACM SIGGRAPH 2017 Posters. :65:1–65:2.
We present a new method to protect projected content from secret photography using high-speed projection. Protection techniques for digital copies have been discussed over many years from the viewpoint of data protection. However, content displayed by general display techniques is not only visible to the human eye but also can be captured by cameras. Therefore, projected content is, at times, secretly taken by malicious small cameras even when protection techniques for digital copies are adopted. In this study, we aim to realize a protectable projection method that allows people to observe content with their eyes but not record content with camera devices.
Suzuki, Kenichi, Kiselyov, Oleg, Kameyama, Yukiyoshi.  2016.  Finally, Safely-extensible and Efficient Language-integrated Query. Proceedings of the 2016 ACM SIGPLAN Workshop on Partial Evaluation and Program Manipulation. :37–48.

Language-integrated query is an embedding of database queries into a host language to code queries at a higher level than the all-to-common concatenation of strings of SQL fragments. The eventually produced SQL is ensured to be well-formed and well-typed, and hence free from the embarrassing (security) problems. Language-integrated query takes advantage of the host language's functional and modular abstractions to compose and reuse queries and build query libraries. Furthermore, language-integrated query systems like T-LINQ generate efficient SQL, by applying a number of program transformations to the embedded query. Alas, the set of transformation rules is not designed to be extensible. We demonstrate a new technique of integrating database queries into a typed functional programming language, so to write well-typed, composable queries and execute them efficiently on any SQL back-end as well as on an in-memory noSQL store. A distinct feature of our framework is that both the query language as well as the transformation rules needed to generate efficient SQL are safely user-extensible, to account for many variations in the SQL back-ends, as well for domain-specific knowledge. The transformation rules are guaranteed to be type-preserving and hygienic by their very construction. They can be built from separately developed and reusable parts and arbitrarily composed into optimization pipelines. With this technique we have embedded into OCaml a relational query language that supports a very large subset of SQL including grouping and aggregation. Its types cover the complete set of intricate SQL behaviors.

Suzuki, Yuhei, Ichikawa, Yuichi, Yamada, Hisato, Ikushima, Kenji.  2019.  Nondestructive evaluation of residual stress through acoustically stimulated electromagnetic response in welded steel. 2019 IEEE International Ultrasonics Symposium (IUS). :1564–1566.
Tensile residual stresses combined with an applied tensile stress can reduce the reliability of steel components. Nondestructive evaluation of residual stress is thus important to avoid unintended fatigue or cracking. Because magnetic hysteresis properties of ferromagnetic materials are sensitive to stress, nondestructive evaluation of residual stress through magnetic properties can be expected. The spatial mapping of local magnetic hysteresis properties becomes possible by using the acoustically stimulated electromagnetic (ASEM) method and the tensile stress dependence of the hysteresis properties has been investigated in steel. It is found that the coercivity Hc and the remanent magnetization signal Vr monotonically decrease with increasing the tensile stress. In this work, we verified the detection of residual stresses through the ASEM response in a welded steel plate. Tensile stresses are intentionally introduced on the opposite side of the partially welded face by controlling welding temperatures. We found that Hc and Vr clearly decrease in the welded region, suggesting that the presence of tensile residual stresses is well detected by the hysteresis parameters.
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Švábenský, V., Vykopal, J..  2018.  Gathering Insights from Teenagers’ Hacking Experience with Authentic Cybersecurity Tools. 2018 IEEE Frontiers in Education Conference (FIE). :1—4.

This Work-In-Progress Paper for the Innovative Practice Category presents a novel experiment in active learning of cybersecurity. We introduced a new workshop on hacking for an existing science-popularizing program at our university. The workshop participants, 28 teenagers, played a cybersecurity game designed for training undergraduates and professionals in penetration testing. Unlike in learning environments that are simplified for young learners, the game features a realistic virtual network infrastructure. This allows exploring security tools in an authentic scenario, which is complemented by a background story. Our research aim is to examine how young players approach using cybersecurity tools by interacting with the professional game. A preliminary analysis of the game session showed several challenges that the workshop participants faced. Nevertheless, they reported learning about security tools and exploits, and 61% of them reported wanting to learn more about cybersecurity after the workshop. Our results support the notion that young learners should be allowed more hands-on experience with security topics, both in formal education and informal extracurricular events.

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Svetinovic, Davor.  2017.  Blockchain Engineering for the Internet of Things: Systems Security Perspective. Proceedings of the 3rd ACM International Workshop on IoT Privacy, Trust, and Security. :1–1.
The Internet of Things (IoT) technology has a potential to bring the benefits of intelligently interconnecting not just computers and humans, but most of everyday things. IoT has a promise of opening significant business process improvement opportunities leading to economic growth and cost reductions. However, there are many challenges facing IoT, including significant scalability and security challenges due to the integration of potentially huge number of things into the network. Many of scalability and security issues stem from a centralized, primarily client/server, architecture of IoT systems and frameworks. Blockchain technology, as a relativelly new approach to decentralized computation and assets management and transfer, has a potential to help solve a number of scalability and security issues that IoT is facing, primarilly through the removal of centralized points of failure for such systems. As such, blockchain technology and IoT integration provides a promising direction and it has recently generated significant research interest, e.g., [4]. In this talk, we present our experiences based on our recent project in enhancing security and privacy in decentralized energy trading in smart grids using blockchain, multi-signatures and anonymous messaging streams [1], that has built upon our previous work on Bitcoin-based decentralized carbon emissions trading infrastructure model [2]. In particular, we present the blockchain systems security issues within the context of IoT security and privacy requirements [3]. This is done with the intention of producing an early integrated security model for blockchain-powered IoT systems [5]. The presentation is constrained to the discussion of the architecture-level requirements [6]. Finally, we will present the main opportunity loss if the integration ignores the full realization of the real-world asset transaction paradigm.
Swain, P., Kamalia, U., Bhandarkar, R., Modi, T..  2019.  CoDRL: Intelligent Packet Routing in SDN Using Convolutional Deep Reinforcement Learning. 2019 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS). :1—6.

Software Defined Networking (SDN) provides opportunities for flexible and dynamic traffic engineering. However, in current SDN systems, routing strategies are based on traditional mechanisms which lack in real-time modification and less efficient resource utilization. To overcome these limitations, deep learning is used in this paper to improve the routing computation in SDN. This paper proposes Convolutional Deep Reinforcement Learning (CoDRL) model which is based on deep reinforcement learning agent for routing optimization in SDN to minimize the mean network delay and packet loss rate. The CoDRL model consists of Deep Deterministic Policy Gradients (DDPG) deep agent coupled with Convolution layer. The proposed model tends to automatically adapts the dynamic packet routing using network data obtained through the SDN controller, and provides the routing configuration that attempts to reduce network congestion and minimize the mean network delay. Hence, the proposed deep agent exhibits good convergence towards providing routing configurations that improves the network performance.

Swami, Shivam, Rakshit, Joydeep, Mohanram, Kartik.  2016.  SECRET: Smartly EnCRypted Energy Efficient Non-volatile Memories. Proceedings of the 53rd Annual Design Automation Conference. :166:1–166:6.

Data persistence in emerging non-volatile memories (NVMs) poses a multitude of security vulnerabilities, motivating main memory encryption for data security. However, practical encryption algorithms demonstrate strong diffusion characteristics that increase cell flips, resulting in increased write energy/latency and reduced lifetime of NVMs. State-of-the-art security solutions have focused on reducing the encryption penalty (increased write energy/latency and reduced memory lifetime) in single-level cell (SLC) NVMs; however, the realization of low encryption penalty solutions for multi-/triple-level cell (MLC/TLC) secure NVMs remains an open area of research. This work synergistically integrates zero-based partial writes with XOR-based energy masking to realize Smartly EnCRypted Energy efficienT, i.e., SECRET MLC/TLC NVMs, without compromising the security of the underlying encryption technique. Our simulations on an MLC (TLC) resistive RAM (RRAM) architecture across SPEC CPU2006 workloads demonstrate that for 6.25% (7.84%) memory overhead, SECRET reduces write energy by 80% (63%), latency by 37% (49%), and improves memory lifetime by 63% (56%) over conventional advanced encryption standard-based (AES-based) counter mode encryption.

Swami, Shivam, Rakshit, Joydeep, Mohanram, Kartik.  2016.  SECRET: Smartly EnCRypted Energy Efficient Non-volatile Memories. Proceedings of the 53rd Annual Design Automation Conference. :166:1–166:6.

Data persistence in emerging non-volatile memories (NVMs) poses a multitude of security vulnerabilities, motivating main memory encryption for data security. However, practical encryption algorithms demonstrate strong diffusion characteristics that increase cell flips, resulting in increased write energy/latency and reduced lifetime of NVMs. State-of-the-art security solutions have focused on reducing the encryption penalty (increased write energy/latency and reduced memory lifetime) in single-level cell (SLC) NVMs; however, the realization of low encryption penalty solutions for multi-/triple-level cell (MLC/TLC) secure NVMs remains an open area of research. This work synergistically integrates zero-based partial writes with XOR-based energy masking to realize Smartly EnCRypted Energy efficienT, i.e., SECRET MLC/TLC NVMs, without compromising the security of the underlying encryption technique. Our simulations on an MLC (TLC) resistive RAM (RRAM) architecture across SPEC CPU2006 workloads demonstrate that for 6.25% (7.84%) memory overhead, SECRET reduces write energy by 80% (63%), latency by 37% (49%), and improves memory lifetime by 63% (56%) over conventional advanced encryption standard-based (AES-based) counter mode encryption.

Swathi, P, Modi, Chirag, Patel, Dhiren.  2019.  Preventing Sybil Attack in Blockchain Using Distributed Behavior Monitoring of Miners. 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT). :1–6.

Blockchain technology is useful with the record keeping of digital transactions, IoT, supply chain management etc. However, we have observed that the traditional attacks are possible on blockchain due to lack of robust identity management. We found that Sybil attack can cause severe impact in public/permissionless blockchain, in which an attacker can subvert the blockchain by creating a large number of pseudonymous identities (i.e. Fake user accounts) and push legitimate entities in the minority. Such virtual nodes can act like genuine nodes to create disproportionately large influence on the network. This may lead to several other attacks like DoS, DDoS etc. In this paper, a Sybil attack is demonstrated on a blockchain test bed with its impact on the throughput of the system. We propose a solution directive, in which each node monitors the behavior of other nodes and checks for the nodes which are forwarding the blocks of only particular user. Such nodes are quickly identified, blacklisted and notified to other nodes, and thus the Sybil attack can be restricted. We analyze experimental results of the proposed solution.