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HOU, RUI, Han, Min, Chen, Jing, Hu, Wenbin, Tan, Xiaobin, Luo, Jiangtao, Ma, Maode.  2019.  Theil-Based Countermeasure against Interest Flooding Attacks for Named Data Networks. IEEE Network. 33:116—121.
NDN has been widely regarded as a promising representation and implementation of information- centric networking (ICN) and serves as a potential candidate for the future Internet architecture. However, the security of NDN is threatened by a significant safety hazard known as an IFA, which is an evolution of DoS and distributed DoS attacks on IP-based networks. The IFA attackers can create numerous malicious interest packets into a named data network to quickly exhaust the bandwidth of communication channels and cache capacity of NDN routers, thereby seriously affecting the routers' ability to receive and forward packets for normal users. Accurate detection of the IFAs is the most critical issue in the design of a countermeasure. To the best of our knowledge, the existing IFA countermeasures still have limitations in terms of detection accuracy, especially for rapidly volatile attacks. This article proposes a TC to detect the distributions of normal and malicious interest packets in the NDN routers to further identify the IFA. The trace back method is used to prevent further attempts. The simulation results show the efficiency of the TC for mitigating the IFAs and its advantages over other typical IFA countermeasures.
Lim, Yeon-sup, Srivatsa, Mudhakar, Chakraborty, Supriyo, Taylor, Ian.  2018.  Learning Light-Weight Edge-Deployable Privacy Models. 2018 IEEE International Conference on Big Data (Big Data). :1290–1295.
Privacy becomes one of the important issues in data-driven applications. The advent of non-PC devices such as Internet-of-Things (IoT) devices for data-driven applications leads to needs for light-weight data anonymization. In this paper, we develop an anonymization framework that expedites model learning in parallel and generates deployable models for devices with low computing capability. We evaluate our framework with various settings such as different data schema and characteristics. Our results exhibit that our framework learns anonymization models up to 16 times faster than a sequential anonymization approach and that it preserves enough information in anonymized data for data-driven applications.
Lim, Yeon-sup, Srivatsa, Mudhakar, Chakraborty, Supriyo, Taylor, Ian.  2018.  Learning Light-Weight Edge-Deployable Privacy Models. 2018 IEEE International Conference on Big Data (Big Data). :1290–1295.
Privacy becomes one of the important issues in data-driven applications. The advent of non-PC devices such as Internet-of-Things (IoT) devices for data-driven applications leads to needs for light-weight data anonymization. In this paper, we develop an anonymization framework that expedites model learning in parallel and generates deployable models for devices with low computing capability. We evaluate our framework with various settings such as different data schema and characteristics. Our results exhibit that our framework learns anonymization models up to 16 times faster than a sequential anonymization approach and that it preserves enough information in anonymized data for data-driven applications.
Shahbaz, Ajmal, Hoang, Van-Thanh, Jo, Kang-Hyun.  2019.  Convolutional Neural Network based Foreground Segmentation for Video Surveillance Systems. IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society. 1:86–89.
Convolutional Neural Networks (CNN) have shown astonishing results in the field of computer vision. This paper proposes a foreground segmentation algorithm based on CNN to tackle the practical challenges in the video surveillance system such as illumination changes, dynamic backgrounds, camouflage, and static foreground object, etc. The network is trained using the input of image sequences with respective ground-truth. The algorithm employs a CNN called VGG-16 to extract features from the input. The extracted feature maps are upsampled using a bilinear interpolation. The upsampled feature mask is passed through a sigmoid function and threshold to get the foreground mask. Binary cross entropy is used as the error function to compare the constructed foreground mask with the ground truth. The proposed algorithm was tested on two standard datasets and showed superior performance as compared to the top-ranked foreground segmentation methods.
Scherzinger, Stefanie, Seifert, Christin, Wiese, Lena.  2019.  The Best of Both Worlds: Challenges in Linking Provenance and Explainability in Distributed Machine Learning. 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS). :1620–1629.
Machine learning experts prefer to think of their input as a single, homogeneous, and consistent data set. However, when analyzing large volumes of data, the entire data set may not be manageable on a single server, but must be stored on a distributed file system instead. Moreover, with the pressing demand to deliver explainable models, the experts may no longer focus on the machine learning algorithms in isolation, but must take into account the distributed nature of the data stored, as well as the impact of any data pre-processing steps upstream in their data analysis pipeline. In this paper, we make the point that even basic transformations during data preparation can impact the model learned, and that this is exacerbated in a distributed setting. We then sketch our vision of end-to-end explainability of the model learned, taking the pre-processing into account. In particular, we point out the potentials of linking the contributions of research on data provenance with the efforts on explainability in machine learning. In doing so, we highlight pitfalls we may experience in a distributed system on the way to generating more holistic explanations for our machine learning models.
Park, Jungmin, Cho, Seongjoon, Lim, Taejin, Bhunia, Swarup, Tehranipoor, Mark.  2019.  SCR-QRNG: Side-Channel Resistant Design using Quantum Random Number Generator. 2019 IEEE/ACM International Conference on Computer-Aided Design (ICCAD). :1–8.
Random number generators play a pivotal role in generating security primitives, e.g., encryption keys, nonces, initial vectors, and random masking for side-channel countermeasures. A quantum entropy source based on radioactive isotope decay can be exploited to generate random numbers with sufficient entropy. If a deterministic random bit generator (DRBG) is combined for post-processing, throughput of the quantum random number generator (QRNG) can be improved. However, general DRBGs are susceptible to side-channel attacks. In this paper, we propose a framework called SCR-QRNG framework, which offers Side-Channel Resistant primitives using QRNG. The QRNG provides sources of randomness for modulating the clock frequency of a DRBG to obfuscate side-channel leakages, and to generate unbiased random numbers for security primitives. The QRNG has robustness against power side-channel attacks and is in compliance with NIST SP 800-22/90B and BSI AIS 31. We fabricate a quantum entropy chip, and implement a PCB module for a random frequency clock generator and a side-channel resistant QRNG on an FPGA.
Hayashi, Masahito.  2019.  Semi-Finite Length Analysis for Secure Random Number Generation. 2019 IEEE International Symposium on Information Theory (ISIT). :952–956.
To discuss secure key generation from imperfect random numbers, we address the secure key generation length. There are several studies for its asymptotic expansion up to the order √n or log n. However, these expansions have errors of the order o(√n) or o(log n), which does not go to zero asymptotically. To resolve this problem, we derive the asymptotic expansion up to the constant order for upper and lower bounds of these optimal values. While the expansions of upper and lower bonds do not match, they clarify the ranges of these optimal values, whose errors go to zero asymptotically.
Hyunki-Kim, Jinhyeok-Oh, Changuk-Jang, Okyeon-Yi, Juhong-Han, Hansaem-Wi, Chanil-Park.  2019.  Analysis of the Noise Source Entropy Used in OpenSSL’s Random Number Generation Mechanism. 2019 International Conference on Information and Communication Technology Convergence (ICTC). :59–62.
OpenSSL is an open source library that implements the Secure Socket Layer (SSL), a security protocol used by the TCP/IP layer. All cryptographic systems require random number generation for many reasons, such as cryptographic key generation and protocol challenge/response, OpenSSL is also the same. OpenSSL can be run on a variety of operating systems. especially when generating random numbers on Unix-like operating systems, it can use /dev /(u)random [6], as a seed to add randomness. In this paper, we analyze the process provided by OpenSSL when random number generation is required. We also provide considerations for application developers and OpenSSL users to use /dev/urandom and real-time clock (nanoseconds of timespec structure) as a seed to generate cryptographic random numbers in the Unix family.
Origines, Domingo V., Sison, Ariel M., Medina, Ruji P..  2019.  A Novel Pseudo-Random Number Generator Algorithm based on Entropy Source Epoch Timestamp. 2019 International Conference on Information and Communications Technology (ICOIACT). :50–55.
Random numbers are important tools for generating secret keys, encrypting messages, or masking the content of certain protocols with a random sequence that can be deterministically generated. The lack of assurance about the random numbers generated can cause serious damage to cryptographic protocols, prompting vulnerabilities to be exploited by the attackers. In this paper, a new pseudo - random number generator algorithm that uses dynamic system clock converted to Epoch Timestamp as PRNG seed was developed. The algorithm uses a Linear Congruential Generator (LCG) algorithm that produces a sequence of pseudo - randomized numbers that performs mathematical operations to transform numbers that appears to be unrelated to the Seed. Simulation result shows that the new PRNG algorithm does not generate repeated random numbers based on the frequency of iteration, a good indicator that the key for random numbers is secured. Numerical analysis using NIST Test Suite results concerning to random sequences generated random numbers has a total average of 0.342 P-value. For a p-value ≥ 0.001, a sequence would be considered to be random with a confidence of 99.9%. This shows that robustness and unpredictability were achieved. Hence, It is highly deterministic in nature and has a good quality of Pseudo-Random Numbers. It is therefore a good source of a session key generation for encryption, reciprocal in the authentication schemes and other cryptographic algorithm parameters that improve and secure data from any type of security attack.
Pouliot, David, Griffy, Scott, Wright, Charles V..  2019.  The Strength of Weak Randomization: Easily Deployable, Efficiently Searchable Encryption with Minimal Leakage. 2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). :517–529.

Efficiently searchable and easily deployable encryption schemes enable an untrusted, legacy service such as a relational database engine to perform searches over encrypted data. The ease with which such schemes can be deployed on top of existing services makes them especially appealing in operational environments where encryption is needed but it is not feasible to replace large infrastructure components like databases or document management systems. Unfortunately all previously known approaches for efficiently searchable and easily deployable encryption are vulnerable to inference attacks where an adversary can use knowledge of the distribution of the data to recover the plaintext with high probability. We present a new efficiently searchable, easily deployable database encryption scheme that is provably secure against inference attacks even when used with real, low-entropy data. We implemented our constructions in Haskell and tested databases up to 10 million records showing our construction properly balances security, deployability and performance.

Voronych, Artur, Nyckolaychuk, Lyubov, Vozna, Nataliia, Pastukh, Taras.  2019.  Methods and Special Processors of Entropy Signal Processing. 2019 IEEE 15th International Conference on the Experience of Designing and Application of CAD Systems (CADSM). :1–4.

The analysis of applied tasks and methods of entropy signal processing are carried out in this article. The theoretical comments about the specific schemes of special processors for the determination of probability and correlation activity are given. The perspective of the influence of probabilistic entropy of C. Shannon as cipher signal receivers is reviewed. Examples of entropy-manipulated signals and system characteristics of the proposed special processors are given.

Puteaux, Pauline, Puech, William.  2019.  Image Analysis and Processing in the Encrypted Domain. 2019 IEEE International Conference on Image Processing (ICIP). :3020–3022.

In this research project, we are interested by finding solutions to the problem of image analysis and processing in the encrypted domain. For security reasons, more and more digital data are transferred or stored in the encrypted domain. However, during the transmission or the archiving of encrypted images, it is often necessary to analyze or process them, without knowing the original content or the secret key used during the encryption phase. We propose to work on this problem, by associating theoretical aspects with numerous applications. Our main contributions concern: data hiding in encrypted images, correction of noisy encrypted images, recompression of crypto-compressed images and secret image sharing.

Alioto, Massimo, Taneja, Sachin.  2019.  Enabling Ubiquitous Hardware Security via Energy-Efficient Primitives and Systems : (Invited Paper). 2019 IEEE Custom Integrated Circuits Conference (CICC). :1–8.
Security down to hardware (HW) has become a fundamental requirement in highly-connected and ubiquitously deployed systems, as a result of the recent discovery of a wide range of vulnerabilities in commercial devices, as well as the affordability of several attacks that were traditionally considered unlikely. HW security is now a fundamental requirement in view of the massive attack surface that they expose, and the substantial power penalty entailed by solutions at higher levels of abstraction.In large-scale networks of connected devices, attacks need to be counteracted at low cost down to individual nodes, which need to be identified or authenticated securely, and protect confidentiality and integrity of the data that is sensed, stored, processed and wirelessly exchanged. In many security-sensitive applications, physical attacks against individual chips need to be counteracted to truly enable an end-to-end chain of trust from nodes to cloud and actuation (i.e., always-on security). These requirements have motivated the on-going global research and development effort to assure hardware security at low cost and power penalty down to low-end devices (i.e., ubiquitous security).This paper provides a fresh overview of the fundamentals, the design requirements and the state of the art in primitives for HW security. Challenges and future directions are discussed using recent silicon demonstrations as case studies.
Liu, Zhenpeng, He, Yupeng, Wang, Wensheng, Wang, Shuo, Li, Xiaofei, Zhang, Bin.  2019.  AEH-MTD: Adaptive Moving Target Defense Scheme for SDN. 2019 IEEE International Conference on Smart Internet of Things (SmartIoT). :142–147.

Distributed Denial of Service attack is very harmful to software-defined networking. Effective defense measures are the key to ensure SDN security. An adaptive moving target defense scheme based on end information hopping for SDN is proposed. The source address entropy value and the flow rate method are used to detect the network condition. According to the detection result, the end information is adjusted by time adaptive or space adaptive. A model of active network defense is constructed. The experimental results show that the proposed scheme enhances the anti-attack capability and serviceability compared with other methods, and has greater dynamics and flexibility.

Nataliani, Yessica, Yang, Miin-Shen.  2019.  Feature-Weighted Fuzzy K-Modes Clustering. Proceedings of the 2019 3rd International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence. :63–68.
Fuzzy k-modes (FKM) are variants of fuzzy c-means used for categorical data. The FKM algorithms generally treat feature components with equal importance. However, in clustering process, different feature weights need to be assigned for feature components because some irrelevant features may degrade the performance of the FKM algorithms. In this paper, we propose a novel algorithm, called feature-weighted fuzzy k-modes (FW-FKM), to improve FKM with a feature-weight entropy term such that it can automatically compute different feature weights for categorical data. Some numerical and real data sets are used to compare FW-FKM with some existing methods in the literature. Experimental results and comparisons actually demonstrate these good aspects of the proposed FW-FKM with its effectiveness and usefulness in practice.
Zhang, Naiji, Jaafar, Fehmi, Malik, Yasir.  2019.  Low-Rate DoS Attack Detection Using PSD Based Entropy and Machine Learning. 2019 6th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/ 2019 5th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom). :59–62.
The Distributed Denial of Service attack is one of the most common attacks and it is hard to mitigate, however, it has become more difficult while dealing with the Low-rate DoS (LDoS) attacks. The LDoS exploits the vulnerability of TCP congestion-control mechanism by sending malicious traffic at the low constant rate and influence the victim machine. Recently, machine learning approaches are applied to detect the complex DDoS attacks and improve the efficiency and robustness of the intrusion detection system. In this research, the algorithm is designed to balance the detection rate and its efficiency. The detection algorithm combines the Power Spectral Density (PSD) entropy function and Support Vector Machine to detect LDoS traffic from normal traffic. In our solution, the detection rate and efficiency are adjustable based on the parameter in the decision algorithm. To have high efficiency, the detection method will always detect the attacks by calculating PSD-entropy first and compare it with the two adaptive thresholds. The thresholds can efficiently filter nearly 19% of the samples with a high detection rate. To minimize the computational cost and look only for the patterns that are most relevant for detection, Support Vector Machine based machine learning model is applied to learn the traffic pattern and select appropriate features for detection algorithm. The experimental results show that the proposed approach can detect 99.19% of the LDoS attacks and has an O (n log n) time complexity in the best case.
Hussain, Syed Saiq, Sohail Ibrahim, Muhammad, Mir, Syed Zain, Yasin, Sajid, Majeed, Muhammad Kashif, Ghani, Azfar.  2018.  Efficient Video Encryption Using Lightweight Cryptography Algorithm. 2018 3rd International Conference on Emerging Trends in Engineering, Sciences and Technology (ICEEST). :1-6.

The natural redundancy in video data due to its spatio-temporal correlation of neighbouring pixels require highly complex encryption process to successfully cipher the data. Conventional encryption methods are based on lengthy keys and higher number of rounds which are inefficient for low powered, small battery operated devices. Motivated by the success of lightweight encryption methods specially designed for IoT environment, herein an efficient method for video encryption is proposed. The proposed technique is based on a recently proposed encryption algorithm named Secure IoT (SIT), which utilizes P and Q functions of the KHAZAD cipher to achieve high encryption at low computation cost. Extensive simulations are performed to evaluate the efficacy of the proposed method and results are compared with Secure Force (SF-64) cipher. Under all conditions the proposed method achieved significantly improved results.

Aparna, H., Bhoomija, Faustina, Devi, R. Santhiya, Thenmozhi, K., Amirtharajan, Rengarajan, Praveenkumar, Padmapriya.  2019.  Image Encryption Based on Quantum-Assisted DNA Coded System. 2019 International Conference on Computer Communication and Informatics (ICCCI). :1-4.
Information security is winding up noticeably more vital in information stockpiling and transmission. Images are generally utilised for various purposes. As a result, the protection of image from the unauthorised client is critical. Established encryption techniques are not ready to give a secure framework. To defeat this, image encryption is finished through DNA encoding which is additionally included with confused 1D and 2D logistic maps. The key communication is done through the quantum channel using the BB84 protocol. To recover the encrypted image DNA decoding is performed. Since DNA encryption is invertible, decoding can be effectively done through DNA subtraction. It decreases the complexity and furthermore gives more strength when contrasted with traditional encryption plans. The enhanced strength of the framework is measured utilising measurements like NPCR, UACI, Correlation and Entropy.
Li, Xianxian, Luo, Chunfeng, Liu, Peng, Wang, Li-e.  2019.  Information Entropy Differential Privacy: A Differential Privacy Protection Data Method Based on Rough Set Theory. 2019 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech). :918–923.
Data have become an important asset for analysis and behavioral prediction, especially correlations between data. Privacy protection has aroused academic and social concern given the amount of personal sensitive information involved in data. However, existing works assume that the records are independent of each other, which is unsuitable for associated data. Many studies either fail to achieve privacy protection or lead to excessive loss of information while applying data correlations. Differential privacy, which achieves privacy protection by injecting random noise into the statistical results given the correlation, will improve the background knowledge of adversaries. Therefore, this paper proposes an information entropy differential privacy solution for correlation data privacy issues based on rough set theory. Under the solution, we use rough set theory to measure the degree of association between attributes and use information entropy to quantify the sensitivity of the attribute. The information entropy difference privacy is achieved by clustering based on the correlation and adding personalized noise to each cluster while preserving the correlations between data. Experiments show that our algorithm can effectively preserve the correlation between the attributes while protecting privacy.
Haghighat, Mohammad Hashem, Li, Jun.  2018.  Edmund: Entropy Based Attack Detection and Mitigation Engine Using Netflow Data. Proceedings of the 8th International Conference on Communication and Network Security. :1–6.
Dozens of signature and anomaly based solutions have been proposed to detect malicious activities in computer networks. However, the number of successful attacks are increasing every day. In this paper, we developed a novel entropy based technique, called Edmund, to detect and mitigate Network attacks. While analyzing full payload network traffic was not recommended due to users' privacy, Edmund used netflow data to detect abnormal behavior. The experimental results showed that Edmund was able to highly accurate detect (around 95%) different application, transport, and network layers attacks. It could identify more than 100K malicious flows raised by 1168 different attackers in our campus. Identifying the attackers, is a great feature, which enables the network administrators to mitigate DDoS effects during the attack time.
Yuan, Jie, Li, Xiaoyong.  2018.  A Reliable and Lightweight Trust Computing Mechanism for IoT Edge Devices Based on Multi-Source Feedback Information Fusion. IEEE Access. 6:23626–23638.
The integration of Internet of Things (IoT) and edge computing is currently a new research hotspot. However, the lack of trust between IoT edge devices has hindered the universal acceptance of IoT edge computing as outsourced computing services. In order to increase the adoption of IoT edge computing applications, first, IoT edge computing architecture should establish efficient trust calculation mechanism to alleviate the concerns of numerous users. In this paper, a reliable and lightweight trust mechanism is originally proposed for IoT edge devices based on multi-source feedback information fusion. First, due to the multi-source feedback mechanism is used for global trust calculation, our trust calculation mechanism is more reliable against bad-mouthing attacks caused by malicious feedback providers. Then, we adopt lightweight trust evaluating mechanism for cooperations of IoT edge devices, which is suitable for largescale IoT edge computing because it facilitates low-overhead trust computing algorithms. At the same time, we adopt a feedback information fusion algorithm based on objective information entropy theory, which can overcome the limitations of traditional trust schemes, whereby the trust factors are weighted manually or subjectively. And the experimental results show that the proposed trust calculation scheme significantly outperforms existing approaches in both computational efficiency and reliability.
Wang, Bo, Wang, Xunting.  2018.  Vulnerability Assessment Method for Cyber Physical Power System Considering Node Heterogeneity. 2018 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia). :1109-1113.
In order to make up for the shortcomings of traditional evaluation methods neglecting node difference, a vulnerability assessment method considering node heterogeneity for cyber physical power system (CPPS) is proposed. Based on the entropy of the power flow and complex network theory, we establish heterogeneity evaluation index system for CPPS, which considers the survivability of island survivability and short-term operation of the communication network. For mustration, hierarchical CPPS model and distributed CPPS model are established respectively based on partitioning characteristic and different relationships of power grid and communication network. Simulation results show that distributed system is more robust than hierarchical system of different weighting factor whether under random attack or deliberate attack and a hierarchical system is more sensitive to the weighting factor. The proposed method has a better recognition effect on the equilibrium of the network structure and can assess the vulnerability of CPPS more accurately.
Katsini, Christina, Raptis, George E., Fidas, Christos, Avouris, Nikolaos.  2018.  Towards Gaze-Based Quantification of the Security of Graphical Authentication Schemes. Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications. :17:1-17:5.

In this paper, we introduce a two-step method for estimating the strength of user-created graphical passwords based on the eye-gaze behaviour during password composition. First, the individuals' gaze patterns, represented by the unique fixations on each area of interest (AOI) and the total fixation duration per AOI, are calculated. Second, the gaze-based entropy of the individual is calculated. To investigate whether the proposed metric is a credible predictor of the password strength, we conducted two feasibility studies. Results revealed a strong positive correlation between the strength of the created passwords and the gaze-based entropy. Hence, we argue that the proposed gaze-based metric allows for unobtrusive prediction of the strength of the password a user is going to create and enables intervention to the password composition for helping users create stronger passwords.

Zhang, Xian, Ben, Kerong, Zeng, Jie.  2018.  Cross-Entropy: A New Metric for Software Defect Prediction. 2018 IEEE International Conference on Software Quality, Reliability and Security (QRS). :111-122.

Defect prediction is an active topic in software quality assurance, which can help developers find potential bugs and make better use of resources. To improve prediction performance, this paper introduces cross-entropy, one common measure for natural language, as a new code metric into defect prediction tasks and proposes a framework called DefectLearner for this process. We first build a recurrent neural network language model to learn regularities in source code from software repository. Based on the trained model, the cross-entropy of each component can be calculated. To evaluate the discrimination for defect-proneness, cross-entropy is compared with 20 widely used metrics on 12 open-source projects. The experimental results show that cross-entropy metric is more discriminative than 50% of the traditional metrics. Besides, we combine cross-entropy with traditional metric suites together for accurate defect prediction. With cross-entropy added, the performance of prediction models is improved by an average of 2.8% in F1-score.

Liu, Y., Yuan, X., Li, M., Zhang, W., Zhao, Q., Zhong, J., Cao, Y., Li, Y., Chen, L., Li, H. et al..  2018.  High Speed Device-Independent Quantum Random Number Generation without Detection Loophole. 2018 Conference on Lasers and Electro-Optics (CLEO). :1–2.

We report a an experimental study of device-independent quantum random number generation based on an detection-loophole free Bell test with entangled photons. After considering statistical fluctuations and applying an 80 Gb × 45.6 Mb Toeplitz matrix hashing, we achieve a final random bit rate of 114 bits/s, with a failure probability less than 10-5.