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2021-08-11
Alsubaie, Fheed, Al-Akhras, Mousa, Alzahrani, Hamdan A..  2020.  Using Machine Learning for Intrusion Detection System in Wireless Body Area Network. 2020 First International Conference of Smart Systems and Emerging Technologies (SMARTTECH). :100–104.
This paper introduces a technique that enhances the capabilities of an intrusion detection system (IDS) in a wireless body area network (WBAN). This technique involves adopting two known machine-learning algorithms: artificial neural network (ANN) and the J48 form of decision trees. The enhanced technique reduces the security threats to a WBAN, such as denial-of-service (DoS) attacks. It is essential to manage noise, which might affect the data gathered by the sensors. In this paper, noise in data is measured because it can affect the accuracy of the machine learning algorithms and demonstrate the level of noise at which the machine-learning model can be trusted. The results show that J48 is the best model when there is no noise, with an accuracy reaching 99.66%, as compared to the ANN algorithm. However, with noisy datasets, ANN shows more tolerance to noise.
2021-07-27
Sengupta, Poushali, Paul, Sudipta, Mishra, Subhankar.  2020.  BUDS: Balancing Utility and Differential Privacy by Shuffling. 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT). :1–7.
Balancing utility and differential privacy by shuffling or BUDS is an approach towards crowd sourced, statistical databases, with strong privacy and utility balance using differential privacy theory. Here, a novel algorithm is proposed using one-hot encoding and iterative shuffling with the loss estimation and risk minimization techniques, to balance both the utility and privacy. In this work, after collecting one-hot encoded data from different sources and clients, a step of novel attribute shuffling technique using iterative shuffling (based on the query asked by the analyst) and loss estimation with an updation function and risk minimization produces a utility and privacy balanced differential private report. During empirical test of balanced utility and privacy, BUDS produces ε = 0.02 which is a very promising result. Our algorithm maintains a privacy bound of ε = ln[t/((n1-1)S)] and loss bound of c'\textbackslashtextbareln[t/((n1-1)S)]-1\textbackslashtextbar.
Kim, Hyeji, Jiang, Yihan, Kannan, Sreeram, Oh, Sewoong, Viswanath, Pramod.  2020.  Deepcode: Feedback Codes via Deep Learning. IEEE Journal on Selected Areas in Information Theory. 1:194—206.
The design of codes for communicating reliably over a statistically well defined channel is an important endeavor involving deep mathematical research and wide-ranging practical applications. In this work, we present the first family of codes obtained via deep learning, which significantly outperforms state-of-the-art codes designed over several decades of research. The communication channel under consideration is the Gaussian noise channel with feedback, whose study was initiated by Shannon; feedback is known theoretically to improve reliability of communication, but no practical codes that do so have ever been successfully constructed. We break this logjam by integrating information theoretic insights harmoniously with recurrent-neural-network based encoders and decoders to create novel codes that outperform known codes by 3 orders of magnitude in reliability and achieve a 3dB gain in terms of SNR. We also demonstrate several desirable properties of the codes: (a) generalization to larger block lengths, (b) composability with known codes, and (c) adaptation to practical constraints. This result also has broader ramifications for coding theory: even when the channel has a clear mathematical model, deep learning methodologies, when combined with channel-specific information-theoretic insights, can potentially beat state-of-the-art codes constructed over decades of mathematical research.
2021-06-30
Wang, Chenguang, Tindemans, Simon, Pan, Kaikai, Palensky, Peter.  2020.  Detection of False Data Injection Attacks Using the Autoencoder Approach. 2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS). :1—6.
State estimation is of considerable significance for the power system operation and control. However, well-designed false data injection attacks can utilize blind spots in conventional residual-based bad data detection methods to manipulate measurements in a coordinated manner and thus affect the secure operation and economic dispatch of grids. In this paper, we propose a detection approach based on an autoencoder neural network. By training the network on the dependencies intrinsic in `normal' operation data, it effectively overcomes the challenge of unbalanced training data that is inherent in power system attack detection. To evaluate the detection performance of the proposed mechanism, we conduct a series of experiments on the IEEE 118-bus power system. The experiments demonstrate that the proposed autoencoder detector displays robust detection performance under a variety of attack scenarios.
2021-05-13
Bansal, Naman, Agarwal, Chirag, Nguyen, Anh.  2020.  SAM: The Sensitivity of Attribution Methods to Hyperparameters. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). :11–21.
Attribution methods can provide powerful insights into the reasons for a classifier's decision. We argue that a key desideratum of an explanation method is its robustness to input hyperparameters which are often randomly set or empirically tuned. High sensitivity to arbitrary hyperparameter choices does not only impede reproducibility but also questions the correctness of an explanation and impairs the trust of end-users. In this paper, we provide a thorough empirical study on the sensitivity of existing attribution methods. We found an alarming trend that many methods are highly sensitive to changes in their common hyperparameters e.g. even changing a random seed can yield a different explanation! Interestingly, such sensitivity is not reflected in the average explanation accuracy scores over the dataset as commonly reported in the literature. In addition, explanations generated for robust classifiers (i.e. which are trained to be invariant to pixel-wise perturbations) are surprisingly more robust than those generated for regular classifiers.
2021-04-08
Tyagi, H., Vardy, A..  2015.  Universal Hashing for Information-Theoretic Security. Proceedings of the IEEE. 103:1781–1795.
The information-theoretic approach to security entails harnessing the correlated randomness available in nature to establish security. It uses tools from information theory and coding and yields provable security, even against an adversary with unbounded computational power. However, the feasibility of this approach in practice depends on the development of efficiently implementable schemes. In this paper, we review a special class of practical schemes for information-theoretic security that are based on 2-universal hash families. Specific cases of secret key agreement and wiretap coding are considered, and general themes are identified. The scheme presented for wiretap coding is modular and can be implemented easily by including an extra preprocessing layer over the existing transmission codes.
2021-03-15
Babu, S. A., Ameer, P. M..  2020.  Physical Adversarial Attacks Against Deep Learning Based Channel Decoding Systems. 2020 IEEE Region 10 Symposium (TENSYMP). :1511–1514.

Deep Learning (DL), in spite of its huge success in many new fields, is extremely vulnerable to adversarial attacks. We demonstrate how an attacker applies physical white-box and black-box adversarial attacks to Channel decoding systems based on DL. We show that these attacks can affect the systems and decrease performance. We uncover that these attacks are more effective than conventional jamming attacks. Additionally, we show that classical decoding schemes are more robust than the deep learning channel decoding systems in the presence of both adversarial and jamming attacks.

2021-02-08
Xu, P., Miao, Q., Liu, T., Chen, X..  2015.  Multi-direction Edge Detection Operator. 2015 11th International Conference on Computational Intelligence and Security (CIS). :187—190.

Due to the noise in the images, the edges extracted from these noisy images are always discontinuous and inaccurate by traditional operators. In order to solve these problems, this paper proposes multi-direction edge detection operator to detect edges from noisy images. The new operator is designed by introducing the shear transformation into the traditional operator. On the one hand, the shear transformation can provide a more favorable treatment for directions, which can make the new operator detect edges in different directions and overcome the directional limitation in the traditional operator. On the other hand, all the single pixel edge images in different directions can be fused. In this case, the edge information can complement each other. The experimental results indicate that the new operator is superior to the traditional ones in terms of the effectiveness of edge detection and the ability of noise rejection.

2020-09-21
Akbay, Abdullah Basar, Wang, Weina, Zhang, Junshan.  2019.  Data Collection from Privacy-Aware Users in the Presence of Social Learning. 2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton). :679–686.
We study a model where a data collector obtains data from users through a payment mechanism to learn the underlying state from the elicited data. The private signal of each user represents her individual knowledge about the state. Through social interactions, each user can also learn noisy versions of her friends' signals, which is called group signals. Based on both her private signal and group signals, each user makes strategic decisions to report a privacy-preserved version of her data to the data collector. We develop a Bayesian game theoretic framework to study the impact of social learning on users' data reporting strategies and devise the payment mechanism for the data collector accordingly. Our findings reveal that, the Bayesian-Nash equilibrium can be in the form of either a symmetric randomized response (SR) strategy or an informative non-disclosive (ND) strategy. A generalized majority voting rule is applied by each user to her noisy group signals to determine which strategy to follow. When a user plays the ND strategy, she reports privacy-preserving data completely based on her group signals, independent of her private signal, which indicates that her privacy cost is zero. Both the data collector and the users can benefit from social learning which drives down the privacy costs and helps to improve the state estimation at a given payment budget. We derive bounds on the minimum total payment required to achieve a given level of state estimation accuracy.
2020-09-11
Shekhar, Heemany, Moh, Melody, Moh, Teng-Sheng.  2019.  Exploring Adversaries to Defend Audio CAPTCHA. 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA). :1155—1161.
CAPTCHA is a web-based authentication method used by websites to distinguish between humans (valid users) and bots (attackers). Audio captcha is an accessible captcha meant for the visually disabled section of users such as color-blind, blind, near-sighted users. Firstly, this paper analyzes how secure current audio captchas are from attacks using machine learning (ML) and deep learning (DL) models. Each audio captcha is made up of five, seven or ten random digits[0-9] spoken one after the other along with varying background noise throughout the length of the audio. If the ML or DL model is able to correctly identify all spoken digits and in the correct order of occurance in a single audio captcha, we consider that captcha to be broken and the attack to be successful. Throughout the paper, accuracy refers to the attack model's success at breaking audio captchas. The higher the attack accuracy, the more unsecure the audio captchas are. In our baseline experiments, we found that attack models could break audio captchas that had no background noise or medium background noise with any number of spoken digits with nearly 99% to 100% accuracy. Whereas, audio captchas with high background noise were relatively more secure with attack accuracy of 85%. Secondly, we propose that the concepts of adversarial examples algorithms can be used to create a new kind of audio captcha that is more resilient towards attacks. We found that even after retraining the models on the new adversarial audio data, the attack accuracy remained as low as 25% to 36% only. Lastly, we explore the benefits of creating adversarial audio captcha through different algorithms such as Basic Iterative Method (BIM) and deepFool. We found that as long as the attacker has less than 45% sample from each kinds of adversarial audio datasets, the defense will be successful at preventing attacks.
2020-09-08
Mufassa, Fauzil Halim, Anwar, Khoirul.  2019.  Extrinsic Information Transfer (EXIT) Analysis for Short Polar Codes. 2019 Symposium on Future Telecommunication Technologies (SOFTT). 1:1–6.

Ze the quality of channels into either completely noisy or noieseless channels. This paper presents extrinsic information transfer (EXIT) analysis for iterative decoding of Polar codes to reveal the mechanism of channel transformation. The purpose of understanding the transformation process are to comprehend the placement process of information bit and frozen bit and to comprehend the security standard of Polar codes. Mutual information derived based on the concept of EXIT chart for check nodes and variable nodes of low density parity check (LDPC) codes and applied to Polar codes. This paper explores the quality of the polarized channels in finite blocklength. The finite block-length is of our interest since in the fifth telecommunications generation (5G) the block length is limited. This paper reveals the EXIT curve changes of Polar codes and explores the polarization characteristics, thus, high value of mutual informations for frozen bit are needed to be detectable. If it is the other way, the error correction capability of Polar codes would be drastically decreases. These results are expected to be a reference for developments of Polar codes for 5G technologies and beyond.

2020-08-03
Al-Emadi, Sara, Al-Ali, Abdulla, Mohammad, Amr, Al-Ali, Abdulaziz.  2019.  Audio Based Drone Detection and Identification using Deep Learning. 2019 15th International Wireless Communications Mobile Computing Conference (IWCMC). :459–464.
In recent years, unmanned aerial vehicles (UAVs) have become increasingly accessible to the public due to their high availability with affordable prices while being equipped with better technology. However, this raises a great concern from both the cyber and physical security perspectives since UAVs can be utilized for malicious activities in order to exploit vulnerabilities by spying on private properties, critical areas or to carry dangerous objects such as explosives which makes them a great threat to the society. Drone identification is considered the first step in a multi-procedural process in securing physical infrastructure against this threat. In this paper, we present drone detection and identification methods using deep learning techniques such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Convolutional Recurrent Neural Network (CRNN). These algorithms will be utilized to exploit the unique acoustic fingerprints of the flying drones in order to detect and identify them. We propose a comparison between the performance of different neural networks based on our dataset which features audio recorded samples of drone activities. The major contribution of our work is to validate the usage of these methodologies of drone detection and identification in real life scenarios and to provide a robust comparison of the performance between different deep neural network algorithms for this application. In addition, we are releasing the dataset of drone audio clips for the research community for further analysis.
2020-06-15
Puteaux, Pauline, Puech, William.  2018.  Noisy Encrypted Image Correction based on Shannon Entropy Measurement in Pixel Blocks of Very Small Size. 2018 26th European Signal Processing Conference (EUSIPCO). :161–165.
Many techniques have been presented to protect image content confidentiality. The owner of an image encrypts it using a key and transmits the encrypted image across a network. If the recipient is authorized to access the original content of the image, he can reconstruct it losslessly. However, if during the transmission the encrypted image is noised, some parts of the image can not be deciphered. In order to localize and correct these errors, we propose an approach based on the local Shannon entropy measurement. We first analyze this measure as a function of the block-size. We provide then a full description of our blind error localization and removal process. Experimental results show that the proposed approach, based on local entropy, can be used in practice to correct noisy encrypted images, even with blocks of very small size.
Kin-Cleaves, Christy, Ker, Andrew D..  2018.  Adaptive Steganography in the Noisy Channel with Dual-Syndrome Trellis Codes. 2018 IEEE International Workshop on Information Forensics and Security (WIFS). :1–7.
Adaptive steganography aims to reduce distortion in the embedding process, typically using Syndrome Trellis Codes (STCs). However, in the case of non-adversarial noise, these are a bad choice: syndrome codes are fragile by design, amplifying the channel error rate into unacceptably-high payload error rates. In this paper we examine the fragility of STCs in the noisy channel, and consider how this can be mitigated if their use cannot be avoided altogether. We also propose an extension called Dual-Syndrome Trellis Codes, that combines error correction and embedding in the same Viterbi process, which slightly outperforms a straight-forward combination of standard forward error correction and STCs.
2020-06-02
Ostrev, Dimiter.  2019.  Composable, Unconditionally Secure Message Authentication without any Secret Key. 2019 IEEE International Symposium on Information Theory (ISIT). :622—626.

We consider a setup in which the channel from Alice to Bob is less noisy than the channel from Eve to Bob. We show that there exist encoding and decoding which accomplish error correction and authentication simultaneously; that is, Bob is able to correctly decode a message coming from Alice and reject a message coming from Eve with high probability. The system does not require any secret key shared between Alice and Bob, provides information theoretic security, and can safely be composed with other protocols in an arbitrary context.

2020-04-20
Wang, Chong Xiao, Song, Yang, Tay, Wee Peng.  2018.  PRESERVING PARAMETER PRIVACY IN SENSOR NETWORKS. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP). :1316–1320.
We consider the problem of preserving the privacy of a set of private parameters while allowing inference of a set of public parameters based on observations from sensors in a network. We assume that the public and private parameters are correlated with the sensor observations via a linear model. We define the utility loss and privacy gain functions based on the Cramér-Rao lower bounds for estimating the public and private parameters, respectively. Our goal is to minimize the utility loss while ensuring that the privacy gain is no less than a predefined privacy gain threshold, by allowing each sensor to perturb its own observation before sending it to the fusion center. We propose methods to determine the amount of noise each sensor needs to add to its observation under the cases where prior information is available or unavailable.
Wang, Chong Xiao, Song, Yang, Tay, Wee Peng.  2018.  PRESERVING PARAMETER PRIVACY IN SENSOR NETWORKS. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP). :1316–1320.
We consider the problem of preserving the privacy of a set of private parameters while allowing inference of a set of public parameters based on observations from sensors in a network. We assume that the public and private parameters are correlated with the sensor observations via a linear model. We define the utility loss and privacy gain functions based on the Cramér-Rao lower bounds for estimating the public and private parameters, respectively. Our goal is to minimize the utility loss while ensuring that the privacy gain is no less than a predefined privacy gain threshold, by allowing each sensor to perturb its own observation before sending it to the fusion center. We propose methods to determine the amount of noise each sensor needs to add to its observation under the cases where prior information is available or unavailable.
2020-03-16
Ren, Wenyu, Yu, Tuo, Yardley, Timothy, Nahrstedt, Klara.  2019.  CAPTAR: Causal-Polytree-based Anomaly Reasoning for SCADA Networks. 2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm). :1–7.
The Supervisory Control and Data Acquisition (SCADA) system is the most commonly used industrial control system but is subject to a wide range of serious threats. Intrusion detection systems are deployed to promote the security of SCADA systems, but they continuously generate tremendous number of alerts without further comprehending them. There is a need for an efficient system to correlate alerts and discover attack strategies to provide explainable situational awareness to SCADA operators. In this paper, we present a causal-polytree-based anomaly reasoning framework for SCADA networks, named CAPTAR. CAPTAR takes the meta-alerts from our previous anomaly detection framework EDMAND, correlates the them using a naive Bayes classifier, and matches them to predefined causal polytrees. Utilizing Bayesian inference on the causal polytrees, CAPTAR can produces a high-level view of the security state of the protected SCADA network. Experiments on a prototype of CAPTAR proves its anomaly reasoning ability and its capabilities of satisfying the real-time reasoning requirement.
2020-03-04
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.

2019-12-30
Tabakhpour, Adel, Abdelaziz, Morad M. A..  2019.  Neural Network Model for False Data Detection in Power System State Estimation. 2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE). :1-5.

False data injection is an on-going concern facing power system state estimation. In this work, a neural network is trained to detect the existence of false data in measurements. The proposed approach can make use of historical data, if available, by using them in the training sets of the proposed neural network model. However, the inputs of perceptron model in this work are the residual elements from the state estimation, which are highly correlated. Therefore, their dimension could be reduced by preserving the most informative features from the inputs. To this end, principal component analysis is used (i.e., a data preprocessing technique). This technique is especially efficient for highly correlated data sets, which is the case in power system measurements. The results of different perceptron models that are proposed for detection, are compared to a simple perceptron that produces identical result to the outlier detection scheme. For generating the training sets, state estimation was run for different false data on different measurements in 13-bus IEEE test system, and the residuals are saved as inputs of training sets. The testing results of the trained network show its good performance in detection of false data in measurements.

2019-12-05
Akhtar, Nabeel, Matta, Ibrahim, Raza, Ali, Wang, Yuefeng.  2018.  EL-SEC: ELastic Management of Security Applications on Virtualized Infrastructure. IEEE INFOCOM 2018 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). :778-783.

The concept of Virtualized Network Functions (VNFs) aims to move Network Functions (NFs) out of dedicated hardware devices into software that runs on commodity hardware. A single NF consists of multiple VNF instances, usually running on virtual machines in a cloud infrastructure. The elastic management of an NF refers to load management across the VNF instances and the autonomic scaling of the number of VNF instances as the load on the NF changes. In this paper, we present EL-SEC, an autonomic framework to elastically manage security NFs on a virtualized infrastructure. As a use case, we deploy the Snort Intrusion Detection System as the NF on the GENI testbed. Concepts from control theory are used to create an Elastic Manager, which implements various controllers - in this paper, Proportional Integral (PI) and Proportional Integral Derivative (PID) - to direct traffic across the VNF Snort instances by monitoring the current load. RINA (a clean-slate Recursive InterNetwork Architecture) is used to build a distributed application that monitors load and collects Snort alerts, which are processed by the Elastic Manager and an Attack Analyzer, respectively. Software Defined Networking (SDN) is used to steer traffic through the VNF instances, and to block attack traffic. Our results show that virtualized security NFs can be easily deployed using our EL-SEC framework. With the help of real-time graphs, we show that PI and PID controllers can be used to easily scale the system, which leads to quicker detection of attacks.

Hayashi, Masahito.  2018.  Secure Physical Layer Network Coding versus Secure Network Coding. 2018 IEEE Information Theory Workshop (ITW). :1-5.

Secure network coding realizes the secrecy of the message when the message is transmitted via noiseless network and a part of edges or a part of intermediate nodes are eavesdropped. In this framework, if the channels of the network has noise, we apply the error correction to noisy channel before applying the secure network coding. In contrast, secure physical layer network coding is a method to securely transmit a message by a combination of coding operation on nodes when the network is given as a set of noisy channels. In this paper, we give several examples of network, in which, secure physical layer network coding realizes a performance that cannot be realized by secure network coding.

2019-09-30
Hohlfeld, J., Czoschke, P., Asselin, P., Benakli, M..  2019.  Improving Our Understanding of Measured Jitter (in HAMR). IEEE Transactions on Magnetics. 55:1–11.

The understanding of measured jitter is improved in three ways. First, it is shown that the measured jitter is not only governed by written-in jitter and the reader resolution along the cross-track direction but by remanence noise in the vicinity of transitions and the down-track reader resolution as well. Second, a novel data analysis scheme is introduced that allows for an unambiguous separation of these two contributions. Third, based on data analyses involving the first two learnings and micro-magnetic simulations, we identify and explain the root causes for variations of jitter with write current (WC) (write field), WC overshoot amplitude (write-field rise time), and linear disk velocity measured for heat-assisted magnetic recording.

2019-09-05
Nasseralfoghara, M., Hamidi, H..  2019.  Web Covert Timing Channels Detection Based on Entropy. 2019 5th International Conference on Web Research (ICWR). :12-15.

Todays analyzing web weaknesses and vulnerabilities in order to find security attacks has become more urgent. In case there is a communication contrary to the system security policies, a covert channel has been created. The attacker can easily disclosure information from the victim's system with just one public access permission. Covert timing channels, unlike covert storage channels, do not have memory storage and they draw less attention. Different methods have been proposed for their identification, which generally benefit from the shape of traffic and the channel's regularity. In this article, an entropy-based detection method is designed and implemented. The attacker can adjust the amount of channel entropy by controlling measures such as changing the channel's level or creating noise on the channel to protect from the analyst's detection. As a result, the entropy threshold is not always constant for detection. By comparing the entropy from different levels of the channel and the analyst, we conclude that the analyst must investigate traffic at all possible levels.

2019-06-24
Wang, J., Zhang, X., Zhang, H., Lin, H., Tode, H., Pan, M., Han, Z..  2018.  Data-Driven Optimization for Utility Providers with Differential Privacy of Users' Energy Profile. 2018 IEEE Global Communications Conference (GLOBECOM). :1–6.

Smart meters migrate conventional electricity grid into digitally enabled Smart Grid (SG), which is more reliable and efficient. Fine-grained energy consumption data collected by smart meters helps utility providers accurately predict users' demands and significantly reduce power generation cost, while it imposes severe privacy risks on consumers and may discourage them from using those “espionage meters". To enjoy the benefits of smart meter measured data without compromising the users' privacy, in this paper, we try to integrate distributed differential privacy (DDP) techniques into data-driven optimization, and propose a novel scheme that not only minimizes the cost for utility providers but also preserves the DDP of users' energy profiles. Briefly, we add differential private noises to the users' energy consumption data before the smart meters send it to the utility provider. Due to the uncertainty of the users' demand distribution, the utility provider aggregates a given set of historical users' differentially private data, estimates the users' demands, and formulates the data- driven cost minimization based on the collected noisy data. We also develop algorithms for feasible solutions, and verify the effectiveness of the proposed scheme through simulations using the simulated energy consumption data generated from the utility company's real data analysis.