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2020
Ban, T. Q., Nguyen, T. T. T., Long, V. T., Dung, P. D., Tung, B. T..  2020.  A Benchmarking of the Effectiveness of Modular Exponentiation Algorithms using the library GMP in C language. 2020 International Conference on Computational Intelligence (ICCI). :237–241.
This research aims to implement different modular exponentiation algorithms and evaluate the average complexity and compare it to the theoretical value. We use the library GMP to implement seven modular exponentiation algorithms. They are Left-to-right Square and Multiply, Right-to-left Square and Multiply, Left-to-right Signed Digit Square, and Multiply Left-to-right Square and Multiply Always Right-to-left Square and Multiply Always, Montgomery Ladder and Joye Ladder. For some exponent bit length, we choose 1024 bits and execute each algorithm on many exponent values and count the average numbers of squares and the average number of multiplications. Whenever relevant, our programs will check the consistency relations between the registers at the end of the exponentiation.
MATSUNAGA, Y., AOKI, N., DOBASHI, Y., KOJIMA, T..  2020.  A Black Box Modeling Technique for Distortion Stomp Boxes Using LSTM Neural Networks. 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC). :653–656.
This paper describes an experimental result of modeling stomp boxes of the distortion effect based on a machine learning approach. Our proposed technique models a distortion stomp box as a neural network consisting of LSTM layers. In this approach, the neural network is employed for learning the nonlinear behavior of the distortion stomp boxes. All the parameters for replicating the distortion sound are estimated through its training process using the input and output signals obtained from some commercial stomp boxes. The experimental result indicates that the proposed technique may have a certain appropriateness to replicate the distortion sound by using the well-trained neural networks.
Ferreira, B., Portela, B., Oliveira, T., Borges, G., Domingos, H. J., Leitao, J..  2020.  Boolean Searchable Symmetric Encryption with Filters on Trusted Hardware. IEEE Transactions on Dependable and Secure Computing. :1–1.
The prevalence and availability of cloud infrastructures has made them the de facto solution for storing and archiving data, both for organizations and individual users. Nonetheless, the cloud's wide spread adoption is still hindered by dependability and security concerns, particularly in applications with large data collections where efficient search and retrieval services are also major requirements. This leads to an increased tension between security, efficiency, and search expressiveness. In this paper we tackle this tension by proposing BISEN, a new provably-secure boolean searchable symmetric encryption scheme that improves these three complementary dimensions by exploring the design space of isolation guarantees offered by novel commodity hardware such as Intel SGX, abstracted as Isolated Execution Environments (IEEs). BISEN is the first scheme to support multiple users and enable highly expressive and arbitrarily complex boolean queries, with minimal information leakage regarding performed queries and accessed data, and verifiability regarding fully malicious adversaries. Furthermore, BISEN extends the traditional SSE model to support filter functions on search results based on generic metadata created by the users. Experimental validation and comparison with the state of art shows that BISEN provides better performance with enriched search semantics and security properties.
Kabin, I., Dyka, Z., Klann, D., Mentens, N., Batina, L., Langendoerfer, P..  2020.  Breaking a fully Balanced ASIC Coprocessor Implementing Complete Addition Formulas on Weierstrass Elliptic Curves. 2020 23rd Euromicro Conference on Digital System Design (DSD). :270–276.
In this paper we report on the results of selected horizontal SCA attacks against two open-source designs that implement hardware accelerators for elliptic curve cryptography. Both designs use the complete addition formula to make the point addition and point doubling operations indistinguishable. One of the designs uses in addition means to randomize the operation sequence as a countermeasure. We used the comparison to the mean and an automated SPA to attack both designs. Despite all these countermeasures, we were able to extract the keys processed with a correctness of 100%.
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.
Lehniger, Kai, Aftowicz, Marcin J., Langendorfer, Peter, Dyka, Zoya.  2020.  Challenges of Return-Oriented-Programming on the Xtensa Hardware Architecture. 2020 23rd Euromicro Conference on Digital System Design (DSD). :154–158.
This paper shows how the Xtensa architecture can be attacked with Return-Oriented-Programming (ROP). The presented techniques include possibilities for both supported Application Binary Interfaces (ABIs). Especially for the windowed ABI a powerful mechanism is presented that not only allows to jump to gadgets but also to manipulate registers without relying on specific gadgets. This paper purely focuses on how the properties of the architecture itself can be exploited to chain gadgets and not on specific attacks or a gadget catalog.
Ivanov, P., Baklanov, V., Dymova, E..  2020.  Covert Channels of Data Communication. 2020 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT). :0557—0558.
The article is dedicated to covert channels of data communication in the protected operating system based on the Linux kernel with mandatory access control. The channel which is not intended by developers violates security policy and can lead to disclosure of confidential information. In this paper the covert storage channels are considered. Authors show opportunities to violate the secrecy policy in the protected operating system based on the Linux kernel experimentally. The first scenario uses time stamps of the last access to the files (“atime” stamp), the second scenario uses unreliable mechanism of the automatic login to the user session with another level of secrecy. Then, there are some recommendations to prevent these violations. The goal of this work is to analyze the methods of using covert channels, both previously known and new. The result of the article is recommendations allowing to eliminate security threats which can be embodied through covert channels.
Gomes, G., Dias, L., Correia, M..  2020.  CryingJackpot: Network Flows and Performance Counters against Cryptojacking. 2020 IEEE 19th International Symposium on Network Computing and Applications (NCA). :1—10.
Cryptojacking, the appropriation of users' computational resources without their knowledge or consent to obtain cryp-tocurrencies, is a widespread attack, relatively easy to implement and hard to detect. Either browser-based or binary, cryptojacking lacks robust and reliable detection solutions. This paper presents a hybrid approach to detect cryptojacking where no previous knowledge about the attacks or training data is needed. Our Cryp-tojacking Intrusion Detection Approach, Cryingjackpot, extracts and combines flow and performance counter-based features, aggregating hosts with similar behavior by using unsupervised machine learning algorithms. We evaluate Cryingjackpot experimentally with both an artificial and a hybrid dataset, achieving F1-scores up to 97%.
Dang, Tran Khanh, Truong, Phat T. Tran, Tran, Pi To.  2020.  Data Poisoning Attack on Deep Neural Network and Some Defense Methods. 2020 International Conference on Advanced Computing and Applications (ACOMP). :15–22.
In recent years, Artificial Intelligence has disruptively changed information technology and software engineering with a proliferation of technologies and applications based-on it. However, recent researches show that AI models in general and the most greatest invention since sliced bread - Deep Learning models in particular, are vulnerable to being hacked and can be misused for bad purposes. In this paper, we carry out a brief review of data poisoning attack - one of the two recently dangerous emerging attacks - and the state-of-the-art defense methods for this problem. Finally, we discuss current challenges and future developments.
Houzé, É, Diaconescu, A., Dessalles, J.-L., Mengay, D., Schumann, M..  2020.  A Decentralized Approach to Explanatory Artificial Intelligence for Autonomic Systems. 2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C). :115–120.
While Explanatory AI (XAI) is attracting increasing interest from academic research, most AI-based solutions still rely on black box methods. This is unsuitable for certain domains, such as smart homes, where transparency is key to gaining user trust and solution adoption. Moreover, smart homes are challenging environments for XAI, as they are decentralized systems that undergo runtime changes. We aim to develop an XAI solution for addressing problems that an autonomic management system either could not resolve or resolved in a surprising manner. This implies situations where the current state of affairs is not what the user expected, hence requiring an explanation. The objective is to solve the apparent conflict between expectation and observation through understandable logical steps, thus generating an argumentative dialogue. While focusing on the smart home domain, our approach is intended to be generic and transferable to other cyber-physical systems offering similar challenges. This position paper focuses on proposing a decentralized algorithm, called D-CAN, and its corresponding generic decentralized architecture. This approach is particularly suited for SISSY systems, as it enables XAI functions to be extended and updated when devices join and leave the managed system dynamically. We illustrate our proposal via several representative case studies from the smart home domain.
Zhang, J., Liao, Y., Zhu, X., Wang, H., Ding, J..  2020.  A Deep Learning Approach in the Discrete Cosine Transform Domain to Median Filtering Forensics. IEEE Signal Processing Letters. 27:276—280.
This letter presents a novel median filtering forensics approach, based on a convolutional neural network (CNN) with an adaptive filtering layer (AFL), which is built in the discrete cosine transform (DCT) domain. Using the proposed AFL, the CNN can determine the main frequency range closely related with the operational traces. Then, to automatically learn the multi-scale manipulation features, a multi-scale convolutional block is developed, exploring a new multi-scale feature fusion strategy based on the maxout function. The resultant features are further processed by a convolutional stream with pooling and batch normalization operations, and finally fed into the classification layer with the Softmax function. Experimental results show that our proposed approach is able to accurately detect the median filtering manipulation and outperforms the state-of-the-art schemes, especially in the scenarios of low image resolution and serious compression loss.
Sekar, K., Devi, K. Suganya, Srinivasan, P., SenthilKumar, V. M..  2020.  Deep Wavelet Architecture for Compressive sensing Recovery. 2020 Seventh International Conference on Information Technology Trends (ITT). :185–189.
The deep learning-based compressive Sensing (CS) has shown substantial improved performance and in run-time reduction with signal sampling and reconstruction. In most cases, moreover, these techniques suffer from disrupting artefacts or high-frequency contents at low sampling ratios. Similarly, this occurs in the multi-resolution sampling method, which further collects more components with lower frequencies. A promising innovation combining CS with convolutionary neural network has eliminated the sparsity constraint yet recovery persists slow. We propose a Deep wavelet based compressive sensing with multi-resolution framework provides better improvement in reconstruction as well as run time. The proposed model demonstrates outstanding quality on test functions over previous approaches.
Van, L. X., Dung, L. H., Hoa, D. V..  2020.  Developing Root Problem Aims to Create a Secure Digital Signature Scheme in Data Transfer. 2020 International Conference on Green and Human Information Technology (ICGHIT). :25–30.
This paper presents the proposed method of building a digital signature algorithm which is based on the difficulty of solving root problem and some expanded root problems on Zp. The expanded root problem is a new form of difficult problem without the solution, also originally proposed and applied to build digital signature algorithms. This proposed method enable to build a high-security digital signature platform for practical applications.
Almogbil, Atheer, Alghofaili, Abdullah, Deane, Chelsea, Leschke, Timothy, Almogbil, Atheer, Alghofaili, Abdullah.  2020.  Digital Forensic Analysis of Fitbit Wearable Technology: An Investigator’s Guide. 2020 7th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/2020 6th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom). :44—49.
Wearable technology, such as Fitbit devices, log a user's daily activities, heart rate, calories burned, step count, and sleep activity. This information is valuable to digital forensic investigators as it may serve as evidence to a crime, to either support a suspect's innocence or guilt. It is important for an investigator to find and analyze every piece of data for accuracy and integrity; however, there is no standard for conducting a forensic investigation for wearable technology. In this paper, we conduct a forensic analysis of two different Fitbit devices using open-source tools. It is the responsibility of the investigator to show how the data was obtained and to ensure that the data was not modified during the analysis. This paper will guide investigators in understanding what data is collected by a Fitbit device (specifically the Ionic smartwatch and Alta tracker), how to handle Fitbit devices, and how to extract and forensically analyze said devices using open-source tools, Autopsy Sleuth Kit and Bulk Extractor Viewer.
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
Nguyen, H. M., Derakhshani, R..  2020.  Eyebrow Recognition for Identifying Deepfake Videos. 2020 International Conference of the Biometrics Special Interest Group (BIOSIG). :1—5.
Deepfake imagery that contains altered faces has become a threat to online content. Current anti-deepfake approaches usually do so by detecting image anomalies, such as visible artifacts or inconsistencies. However, with deepfake advances, these visual artifacts are becoming harder to detect. In this paper, we show that one can use biometric eyebrow matching as a tool to detect manipulated faces. Our method could provide an 0.88 AUC and 20.7% EER for deepfake detection when applied to the highest quality deepfake dataset, Celeb-DF.
Song, X., Dong, C., Yuan, D., Xu, Q., Zhao, M..  2020.  Forward Private Searchable Symmetric Encryption with Optimized I/O Efficiency. IEEE Transactions on Dependable and Secure Computing. 17:912–927.
Recently, several practical attacks raised serious concerns over the security of searchable encryption. The attacks have brought emphasis on forward privacy, which is the key concept behind solutions to the adaptive leakage-exploiting attacks, and will very likely to become a must-have property of all new searchable encryption schemes. For a long time, forward privacy implies inefficiency and thus most existing searchable encryption schemes do not support it. Very recently, Bost (CCS 2016) showed that forward privacy can be obtained without inducing a large communication overhead. However, Bost's scheme is constructed with a relatively inefficient public key cryptographic primitive, and has poor I/O performance. Both of the deficiencies significantly hinder the practical efficiency of the scheme, and prevent it from scaling to large data settings. To address the problems, we first present FAST, which achieves forward privacy and the same communication efficiency as Bost's scheme, but uses only symmetric cryptographic primitives. We then present FASTIO, which retains all good properties of FAST, and further improves I/O efficiency. We implemented the two schemes and compared their performance with Bost's scheme. The experiment results show that both our schemes are highly efficient.
Dai, Q., Shi, L..  2020.  A Game-Theoretic Analysis of Cyber Attack-Mitigation in Centralized Feeder Automation System. 2020 IEEE Power Energy Society General Meeting (PESGM). :1–5.
The intelligent electronic devices widely deployed across the distribution network are inevitably making the feeder automation (FA) system more vulnerable to cyber-attacks, which would lead to disastrous socio-economic impacts. This paper proposes a three-stage game-theoretic framework that the defender allocates limited security resources to minimize the economic impacts on FA system while the attacker deploys limited attack resources to maximize the corresponding impacts. Meanwhile, the probability of successful attack is calculated based on the Bayesian attack graph, and a fault-tolerant location technique for centralized FA system is elaborately considered during analysis. The proposed game-theoretic framework is converted into a two-level zero-sum game model and solved by the particle swarm optimization (PSO) combined with a generalized reduced gradient algorithm. Finally, the proposed model is validated on distribution network for RBTS bus 2.
Long, Vu Duc, Duong, Ta Nguyen Binh.  2020.  Group Instance: Flexible Co-Location Resistant Virtual Machine Placement in IaaS Clouds. 2020 IEEE 29th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE). :64—69.
This paper proposes and analyzes a new virtual machine (VM) placement technique called Group Instance to deal with co-location attacks in public Infrastructure-as-a-Service (IaaS) clouds. Specifically, Group Instance organizes cloud users into groups with pre-determined sizes set by the cloud provider. Our empirical results obtained via experiments with real-world data sets containing million of VM requests have demonstrated the effectiveness of the new technique. In particular, the advantages of Group Instance are three-fold: 1) it is simple and highly configurable to suit the financial and security needs of cloud providers, 2) it produces better or at least similar performance compared to more complicated, state-of-the-art algorithms in terms of resource utilization and co-location security, and 3) it does not require any modifications to the underlying infrastructures of existing public cloud services.
Shukla, M. K., Dubey, A. K., Upadhyay, D., Novikov, B..  2020.  Group Key Management in Cloud for Shared Media Sanitization. 2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC). :117—120.
Cloud provides a low maintenance and affordable storage to various applications and users. The data owner allows the cloud users to access the documents placed in the cloud service provider based on the user's access control vector provided to the cloud users by the data owners. In such type of scenarios, the confidentiality of the documents exchanged between the cloud service provider and the users should be maintained. The existing approaches used to provide this facility are not computation and communication efficient for performing key updating in the data owner side and the key recovery in the user side. This paper discusses the key management services provided to the cloud users. Remote key management and client-side key management are two approaches used by cloud servers. This paper also aims to discuss the method for destroying the encryption/decryption group keys for shared data to securing the data after deletion. Crypto Shredding or Crypto Throw technique is deployed for the same.
Tian, X., Ding, R., Wu, X., Bai, G..  2020.  Hardware Implementation of a Cryptographically Secure Pseudo-Random Number Generators Based on Koblitz Elliptic Curves. 2020 IEEE 3rd International Conference on Electronics Technology (ICET). :91–94.
In this brief, a cryptographically secure pseudo-random number generator based on the NIST Koblitz elliptic curve K-163 is implemented. A 3-stage pipelined multiplier is adopted to speed up point additions. In addition, Frobenius map and point additions are performed in parallel to reduce the clock cycles required for scalar multiplication. By expanding the multiplier with a multiplexer, exponentiation and multiplication can be executed simultaneously, thus greatly reducing the clock cycles needed for inversion. Implementation results on Xilinx Virtex-4 show that the frequency of the multiplier is up to 248 MHz, therefore it takes only 2.21 us for scalar multiplication over K-163. The cryptographically secure pseudo-random number generator can produce 452 Kbit random number every second.
Papadogiannaki, E., Deyannis, D., Ioannidis, S..  2020.  Head(er)Hunter: Fast Intrusion Detection using Packet Metadata Signatures. 2020 IEEE 25th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD). :1–6.
More than 75% of the Internet traffic is now encrypted, while this percentage is constantly increasing. The majority of communications are secured using common encryption protocols such as SSL/TLS and IPsec to ensure security and protect the privacy of Internet users. Yet, encryption can be exploited to hide malicious activities. Traditionally, network traffic inspection is based on techniques like deep packet inspection (DPI). Common applications for DPI include but are not limited to firewalls, intrusion detection and prevention systems, L7 filtering and packet forwarding. The core functionality of such DPI implementations is based on pattern matching that enables searching for specific strings or regular expressions inside the packet contents. With the widespread adoption of network encryption though, DPI tools that rely on packet payload content are becoming less effective, demanding the development of more sophisticated techniques in order to adapt to current network encryption trends. In this work, we present HeaderHunter, a fast signature-based intrusion detection system even in encrypted network traffic. We generate signatures using only network packet metadata extracted from packet headers. Also, to cope with the ever increasing network speeds, we accelerate the inner computations of our proposed system using off-the-shelf GPUs.
Deng, L., Luo, J., Zhou, J., Wang, J..  2020.  Identity-based Secret Sharing Access Control Framework for Information-Centric Networking. 2020 IEEE/CIC International Conference on Communications in China (ICCC). :507–511.
Information-centric networking (ICN) has played an increasingly important role in the next generation network design. However, to make better use of request-response communication mode in the ICN network, revoke user privileges more efficiently and protect user privacy more safely, an effective access control mechanism is needed. In this paper, we propose IBSS (identity-based secret sharing), which achieves efficient content distribution by using improved Shamir's secret sharing method. At the same time, collusion attacks are avoided by associating polynomials' degree with the number of users. When authenticating user identity and transmitting content, IBE and IBS are introduced to achieve more efficient and secure identity encryption. From the experimental results, the scheme only introduces an acceptable delay in file retrieval, and it can request follow-up content very efficiently.
Seneviratne, Piyumi, Perera, Dilanka, Samarasekara, Harinda, Keppitiyagama, Chamath, Thilakarathna, Kenneth, De Soyza, Kasun, Wijesekara, Primal.  2020.  Impact of Video Surveillance Systems on ATM PIN Security. 2020 20th International Conference on Advances in ICT for Emerging Regions (ICTer). :59–64.
ATM transactions are verified using two-factor authentication. The PIN is one of the factors (something you know) and the ATM Card is the other factor (something you have). Therefore, banks make significant investments on PIN Mailers and HSMs to preserve the security and confidentiality in the generation, validation, management and the delivery of the PIN to their customers. Moreover, banks install surveillance cameras inside ATM cubicles as a physical security measure to prevent fraud and theft. However, in some cases, ATM PIN-Pad and the PIN entering process get revealed through the surveillance camera footage itself. We demonstrate that visibility of forearm movements is sufficient to infer PINs with a significant level of accuracy. Video footage of the PIN entry process simulated in an experimental setup was analyzed using two approaches. The human observer-based approach shows that a PIN can be guessed with a 30% of accuracy within 3 attempts whilst the computer-assisted analysis of footage gave an accuracy of 50%. The results confirm that ad-hoc installation of surveillance cameras can weaken ATM PIN security significantly by potentially exposing one factor of a two-factor authentication system. Our investigation also revealed that there are no guidelines, standards or regulations governing the placement of surveillance cameras inside ATM cubicles in Sri Lanka.
Wang, H., Zeng, X., Lei, Y., Ren, S., Hou, F., Dong, N..  2020.  Indoor Object Identification based on Spectral Subtraction of Acoustic Room Impulse Response. 2020 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC). :1–4.
Object identification in the room environment is a key technique in many advanced engineering applications such as the unidentified object recognition in security surveillance, human identification and barrier recognition for AI robots. The identification technique based on the sound field perturbation analysis is capable of giving immersive identification which avoids the occlusion problem in the traditional vision-based method. In this paper, a new insight into the relation between the object and the variation of the sound field is presented. The sound field difference before and after the object locates in the environment is analyzed using the spectral subtraction based on the room impulse response. The spectral subtraction shows that the energy loss caused by the sound absorption is the essential factor which perturbs the sound field. By using the energy loss with high uniqueness as the extracted feature, an object identification technique is constructed under the classical supervised pattern recognition framework. The experiment in a real room validates that the system has high identification accuracy. In addition, based on the feature property of position insensitivity, this technique can achieve high identifying accuracy with a quite small training data set, which demonstrates that the technique has potential to be used in real engineering applications.