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2020-09-21
Marcinkevicius, Povilas, Bagci, Ibrahim Ethem, Abdelazim, Nema M., Woodhead, Christopher S., Young, Robert J., Roedig, Utz.  2019.  Optically Interrogated Unique Object with Simulation Attack Prevention. 2019 Design, Automation Test in Europe Conference Exhibition (DATE). :198–203.
A Unique Object (UNO) is a physical object with unique characteristics that can be measured externally. The usually analogue measurement can be converted into a digital representation - a fingerprint - which uniquely identifies the object. For practical applications it is necessary that measurements can be performed without the need of specialist equipment or complex measurement setup. Furthermore, a UNO should be able to defeat simulation attacks; an attacker may replace the UNO with a device or system that produces the expected measurement. Recently a novel type of UNOs based on Quantum Dots (QDs) and exhibiting unique photo-luminescence properties has been proposed. The uniqueness of these UNOs is based on quantum effects that can be interrogated using a light source and a camera. The so called Quantum Confinement UNO (QCUNO) responds uniquely to different light excitation levels which is exploited for simulation attack protection, as opposed to focusing on features too small to reproduce and therefore difficult to measure. In this paper we describe methods for extraction of fingerprints from the QCUNO. We evaluate our proposed methods using 46 UNOs in a controlled setup. Focus of the evaluation are entropy, error resilience and the ability to detect simulation attacks.
2020-09-18
Guo, Xiaolong, Dutta, Raj Gautam, He, Jiaji, Tehranipoor, Mark M., Jin, Yier.  2019.  QIF-Verilog: Quantitative Information-Flow based Hardware Description Languages for Pre-Silicon Security Assessment. 2019 IEEE International Symposium on Hardware Oriented Security and Trust (HOST). :91—100.
Hardware vulnerabilities are often due to design mistakes because the designer does not sufficiently consider potential security vulnerabilities at the design stage. As a result, various security solutions have been developed to protect ICs, among which the language-based hardware security verification serves as a promising solution. The verification process will be performed while compiling the HDL of the design. However, similar to other formal verification methods, the language-based approach also suffers from scalability issue. Furthermore, existing solutions either lead to hardware overhead or are not designed for vulnerable or malicious logic detection. To alleviate these challenges, we propose a new language based framework, QIF-Verilog, to evaluate the trustworthiness of a hardware system at register transfer level (RTL). This framework introduces a quantified information flow (QIF) model and extends Verilog type systems to provide more expressiveness in presenting security rules; QIF is capable of checking the security rules given by the hardware designer. Secrets are labeled by the new type and then parsed to data flow, to which a QIF model will be applied. To demonstrate our approach, we design a compiler for QIF-Verilog and perform vulnerability analysis on benchmarks from Trust-Hub and OpenCore. We show that Trojans or design faults that leak information from circuit outputs can be detected automatically, and that our method evaluates the security of the design correctly.
Zolanvari, Maede, Teixeira, Marcio A., Gupta, Lav, Khan, Khaled M., Jain, Raj.  2019.  Machine Learning-Based Network Vulnerability Analysis of Industrial Internet of Things. IEEE Internet of Things Journal. 6:6822—6834.
It is critical to secure the Industrial Internet of Things (IIoT) devices because of potentially devastating consequences in case of an attack. Machine learning (ML) and big data analytics are the two powerful leverages for analyzing and securing the Internet of Things (IoT) technology. By extension, these techniques can help improve the security of the IIoT systems as well. In this paper, we first present common IIoT protocols and their associated vulnerabilities. Then, we run a cyber-vulnerability assessment and discuss the utilization of ML in countering these susceptibilities. Following that, a literature review of the available intrusion detection solutions using ML models is presented. Finally, we discuss our case study, which includes details of a real-world testbed that we have built to conduct cyber-attacks and to design an intrusion detection system (IDS). We deploy backdoor, command injection, and Structured Query Language (SQL) injection attacks against the system and demonstrate how a ML-based anomaly detection system can perform well in detecting these attacks. We have evaluated the performance through representative metrics to have a fair point of view on the effectiveness of the methods.
Ameli, Amir, Hooshyar, Ali, El-Saadany, Ehab F..  2019.  Development of a Cyber-Resilient Line Current Differential Relay. IEEE Transactions on Industrial Informatics. 15:305—318.
The application of line current differential relays (LCDRs) to protect transmission lines has recently proliferated. However, the reliance of LCDRs on digital communication channels has raised growing cyber-security concerns. This paper investigates the impacts of false data injection attacks (FDIAs) on the performance of LCDRs. It also develops coordinated attacks that involve multiple components, including LCDRs, and can cause false line tripping. Additionally, this paper proposes a technique for detecting FDIAs against LCDRs and differentiating them from actual faults in two-terminal lines. In this method, when an LCDR detects a fault, instead of immediately tripping the line, it calculates and measures the superimposed voltage at its local terminal, using the proposed positive-sequence (PS) and negative-sequence (NS) submodules. To calculate this voltage, the LCDR models the protected line in detail and replaces the rest of the system with a Thevenin equivalent that produces accurate responses at the line terminals. Afterwards, remote current measurement is utilized by the PS and NS submodules to compute each sequence's superimposed voltage. A difference between the calculated and the measured superimposed voltages in any sequence reveals that the remote current measurements are not authentic. Thus, the LCDR's trip command is blocked. The effectiveness of the proposed method is corroborated using simulation results for the IEEE 39-bus test system. The performance of the proposed method is also tested using an OPAL real-time simulator.
Hong, Junho, Nuqui, Reynaldo F., Kondabathini, Anil, Ishchenko, Dmitry, Martin, Aaron.  2019.  Cyber Attack Resilient Distance Protection and Circuit Breaker Control for Digital Substations. IEEE Transactions on Industrial Informatics. 15:4332—4341.
This paper proposes new concepts for detecting and mitigating cyber attacks on substation automation systems by domain-based cyber-physical security solutions. The proposed methods form the basis of a distributed security domain layer that enables protection devices to collaboratively defend against cyber attacks at substations. The methods utilize protection coordination principles to cross check protection setting changes and can run real-time power system analysis to evaluate the impact of the control commands. The transient fault signature (TFS)-based cross-correlation coefficient algorithm has been proposed to detect the false sampled values data injection attack. The proposed functions were verified in a hardware-in-the-loop (HIL) simulation using commercial relays and a real-time digital simulator (RTDS). Various types of cyber intrusions are tested using this test bed to evaluate the consequences and impacts of cyber attacks to power grid as well as to validate the performance of the proposed research-grade cyber attack mitigation functions.
Tanrıverdi, Mustafa, Tekerek, Adem.  2019.  Implementation of Blockchain Based Distributed Web Attack Detection Application. 2019 1st International Informatics and Software Engineering Conference (UBMYK). :1—6.
In last decades' web application security has become one of the most important case study of information security studies. Business processes are transferred to web platforms. So web application usage is increased very fast. Web-based attacks have also increased due to the increased use of web applications. In order to ensure the security of web applications, intrusion detection and prevention systems and web application firewalls are used against web based attacks. Blockchain technology, which has become popular in recent years, enables reliable and transparent sharing of data with all stakeholders. In this study, in order to detect web-based attacks, a blockchain based web attack detection model that uses the signature based detection method is proposed. The signature based detection refers to the detection of attacks by looking for specific patterns against known web based attack types, such as Structured Query Language (SQL) Injection, Cross Site Scripting (XSS), Command Injection. Three web servers were used for the experimental study. A blockchain node has been installed with the MultiChain application for each server. Attacks on web applications are detected using the signature list found in the web application as well as detected using the signature list updated on the blockchain. According to the experimental results, the attacks signature detected and defined by a web application are updated in the blockchain lists and used by all web applications.
Zhang, Fan, Kodituwakku, Hansaka Angel Dias Edirisinghe, Hines, J. Wesley, Coble, Jamie.  2019.  Multilayer Data-Driven Cyber-Attack Detection System for Industrial Control Systems Based on Network, System, and Process Data. IEEE Transactions on Industrial Informatics. 15:4362—4369.
The growing number of attacks against cyber-physical systems in recent years elevates the concern for cybersecurity of industrial control systems (ICSs). The current efforts of ICS cybersecurity are mainly based on firewalls, data diodes, and other methods of intrusion prevention, which may not be sufficient for growing cyber threats from motivated attackers. To enhance the cybersecurity of ICS, a cyber-attack detection system built on the concept of defense-in-depth is developed utilizing network traffic data, host system data, and measured process parameters. This attack detection system provides multiple-layer defense in order to gain the defenders precious time before unrecoverable consequences occur in the physical system. The data used for demonstrating the proposed detection system are from a real-time ICS testbed. Five attacks, including man in the middle (MITM), denial of service (DoS), data exfiltration, data tampering, and false data injection, are carried out to simulate the consequences of cyber attack and generate data for building data-driven detection models. Four classical classification models based on network data and host system data are studied, including k-nearest neighbor (KNN), decision tree, bootstrap aggregating (bagging), and random forest (RF), to provide a secondary line of defense of cyber-attack detection in the event that the intrusion prevention layer fails. Intrusion detection results suggest that KNN, bagging, and RF have low missed alarm and false alarm rates for MITM and DoS attacks, providing accurate and reliable detection of these cyber attacks. Cyber attacks that may not be detectable by monitoring network and host system data, such as command tampering and false data injection attacks by an insider, are monitored for by traditional process monitoring protocols. In the proposed detection system, an auto-associative kernel regression model is studied to strengthen early attack detection. The result shows that this approach detects physically impactful cyber attacks before significant consequences occur. The proposed multiple-layer data-driven cyber-attack detection system utilizing network, system, and process data is a promising solution for safeguarding an ICS.
Rasapour, Farhad, Serra, Edoardo, Mehrpouyan, Hoda.  2019.  Framework for Detecting Control Command Injection Attacks on Industrial Control Systems (ICS). 2019 Seventh International Symposium on Computing and Networking (CANDAR). :211—217.
This paper focuses on the design and development of attack models on the sensory channels and an Intrusion Detection system (IDS) to protect the system from these types of attacks. The encoding/decoding formulas are defined to inject a bit of data into the sensory channel. In addition, a signal sampling technique is utilized for feature extraction. Further, an IDS framework is proposed to reside on the devices that are connected to the sensory channels to actively monitor the signals for anomaly detection. The results obtained based on our experiments have shown that the one-class SVM paired with Fourier transformation was able to detect new or Zero-day attacks.
Kaji, Shugo, Kinugawa, Masahiro, Fujimoto, Daisuke, Hayashi, Yu-ichi.  2019.  Data Injection Attack Against Electronic Devices With Locally Weakened Immunity Using a Hardware Trojan. IEEE Transactions on Electromagnetic Compatibility. 61:1115—1121.
Intentional electromagnetic interference (IEMI) of information and communication devices is based on high-power electromagnetic environments far exceeding the device immunity to electromagnetic interference. IEMI dramatically alters the electromagnetic environment throughout the device by interfering with the electromagnetic waves inside the device and destroying low-tolerance integrated circuits (ICs) and other elements, thereby reducing the availability of the device. In contrast, in this study, by using a hardware Trojan (HT) that is quickly mountable by physically accessing the devices, to locally weaken the immunity of devices, and then irradiating electromagnetic waves of a specific frequency, only the attack targets are intentionally altered electromagnetically. Therefore, we propose a method that uses these electromagnetic changes to rewrite or generate data and commands handled within devices. Specifically, targeting serial communication systems used inside and outside the devices, the installation of an HT on the communication channel weakens local immunity. This shows that it is possible to generate an electrical signal representing arbitrary data on the communication channel by applying electromagnetic waves of sufficiently small output compared with the conventional IEMI and letting the IC process the data. In addition, we explore methods for countering such attacks.
Chakrabarty, Shantanu, Sikdar, Biplab.  2019.  A Methodology for Detecting Stealthy Transformer Tap Command Injection Attacks in Smart Grids. 2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm). :1—6.
On-Load Tap Changing transformers are a widely used voltage regulation device. In the context of modern or smart grids, the control signals, i.e., the tap change commands are sent through SCADA channels. It is well known that the power system SCADA networks are prone to attacks involving injection of false data or commands. While false data injection is well explored in existing literature, attacks involving malicious control signals/commands are relatively unexplored. In this paper, an algorithm is developed to detect a stealthily introduced malicious tap change command through a compromised SCADA channel. This algorithm is based on the observation that a stealthily introduced false data or command masks the true estimation of only a few state variables. This leaves the rest of the state variables to show signs of a change in system state brought about by the attack. Using this observation, an index is formulated based on the ratios of injection or branch currents to voltages of the terminal nodes of the tap changers. This index shows a significant increase when there is a false tap command injection, resulting in easy classification from normal scenarios where there is no attack. The algorithm is computationally light, easy to implement and reliable when tested extensively on several tap changers placed in an IEEE 118-bus system.
Taggu, Amar, Marchang, Ningrinla.  2019.  Random-Byzantine Attack Mitigation in Cognitive Radio Networks using a Multi-Hidden Markov Model System. 2019 International Conference on Electrical and Computing Technologies and Applications (ICECTA). :1—5.
Cognitive Radio Networks (CRN) are opportunistic networks which aim to harness the white space in the television frequency spectrum, on a need-to-need basis, without interfering the incumbent, called the Primary User (PU). Cognitive radios (CR) that sense the spectrum periodically for sensing the PU activity, are called Secondary Users (SU). CRNs are susceptible to two major attacks, Byzantine attacks and Primary User Emulation Attack (PUEA). Both the attacks are capable of rendering a CRN useless, by either interfering with the PU itself or capturing the entire channel for themselves. Byzantine attacks detection and mitigation is an important security issue in CRN. Hence, the current work proposes using a multi-Hidden Markov Model system with an aim to detect different types of random-Byzantine attacks. Simulation results show good detection rate across all the attacks.
Ling, Mee Hong, Yau, Kok-Lim Alvin.  2019.  Can Reinforcement Learning Address Security Issues? an Investigation into a Clustering Scheme in Distributed Cognitive Radio Networks 2019 International Conference on Information Networking (ICOIN). :296—300.
This paper investigates the effectiveness of reinforcement learning (RL) model in clustering as an approach to achieve higher network scalability in distributed cognitive radio networks. Specifically, it analyzes the effects of RL parameters, namely the learning rate and discount factor in a volatile environment, which consists of member nodes (or secondary users) that launch attacks with various probabilities of attack. The clusterhead, which resides in an operating region (environment) that is characterized by the probability of attacks, countermeasures the malicious SUs by leveraging on a RL model. Simulation results have shown that in a volatile operating environment, the RL model with learning rate α= 1 provides the highest network scalability when the probability of attacks ranges between 0.3 and 0.7, while the discount factor γ does not play a significant role in learning in an operating environment that is volatile due to attacks.
Pham-Thi-Dan, Ngoc, Do-Dac, Thiem, Ho-Van, Khuong, Vo-Que, Son, Pham-Ngoc, Son.  2019.  On Security Capability of Cooperative Communications in Energy Scavenging Cognitive Radio Networks. 2019 International Conference on Advanced Technologies for Communications (ATC). :89—93.
In this paper, secrecy outage probability (SOP) of cooperative communications in ESCRNs is numerically evaluated by the recommended precise closed-form formula which is corroborated by numerous computer simulations. Results expose that the relay's location, energy scavenging time, message recovering time, and power division for energy scavenging and message recovering dramatically impact the SOP of the cooperative communications in ESCRNs. Moreover, results infer that the SOP is constant either in energy scavenging non-cognitive networks (ES-nonCRNs) or in ESCRNs with infinite power transmitters.
Sureka, N., Gunaseelan, K..  2019.  Detection Defense against Primary User Emulation Attack in Dynamic Cognitive Radio Networks. 2019 Fifth International Conference on Science Technology Engineering and Mathematics (ICONSTEM). 1:505—510.
Cognitive radio is a promising technology that intends on solving the spectrum scarcity problem by allocating free spectrum dynamically to the unlicensed Secondary Users (SUs) in order to establish coexistence between the licensed Primary User (PU) & SUs, without causing any interference to the incumbent transmission. Primary user emulation attack (PUEA) is one such major threat posed on spectrum sensing, which decreases the spectrum access probability. Detection and defense against PUEA is realized using Yardstick based Threshold Allocation technique (YTA), by assigning threshold level to the base station thereby efficiently enhancing the spectrum sensing ability in a dynamic CR network. The simulation is performed using NS2 and analysis by using X-graph. The results shows minimum interference to primary transmissions by letting SUs spontaneously predict the prospective spectrum availability and aiding in effective prevention of potential emulation attacks along with proficient improvement of throughput in a dynamic cognitive radio environment.
Simpson, Oluyomi, Sun, Yichuang.  2019.  A Stochastic based Physical Layer Security in Cognitive Radio Networks: Cognitive Relay to Fusion Center. 2019 IEEE 38th International Performance Computing and Communications Conference (IPCCC). :1—7.
Cognitive radio networks (CRNs) are found to be, without difficulty wide-open to external malicious threats. Secure communication is an important prerequisite for forthcoming fifth-generation (5G) systems, and CRs are not exempt. A framework for developing the accomplishable benefits of physical layer security (PLS) in an amplify-and-forward cooperative spectrum sensing (AF-CSS) in a cognitive radio network (CRN) using a stochastic geometry is proposed. In the CRN the spectrum sensing data from secondary users (SU) are collected by a fusion center (FC) with the assistance of access points (AP) as cognitive relays, and when malicious eavesdropping SU are listening. In this paper we focus on the secure transmission of active APs relaying their spectrum sensing data to the FC. Closed expressions for the average secrecy rate are presented. Analytical formulations and results substantiate our analysis and demonstrate that multiple antennas at the APs is capable of improving the security of an AF-CSSCRN. The obtained numerical results also show that increasing the number of FCs, leads to an increase in the secrecy rate between the AP and its correlated FC.
Pham-Thi-Dan, Ngoc, Ho-Van, Khuong, Do-Dac, Thiem, Vo-Que, Son, Pham-Ngoc, Son.  2019.  Security Analysis for Cognitive Radio Network with Energy Scavenging Capable Relay over Nakagami-m Fading Channels. 2019 International Symposium on Electrical and Electronics Engineering (ISEE). :68—72.
In this paper, we propose an exact closed-form expression of secrecy outage probability (SOP) for underlay cognitive network with energy scavenging capable relay over Nakagami-m fading channels and under both (maximum transmit and interference) power constraints. Various results validated the proposed expression and shed insights into the security performance of this network in key specifications.
Torabi, Mohammad, Pouri, Alireza Baghaei.  2019.  Physical Layer Security of a Two-Hop Mixed RF-FSO System in a Cognitive Radio Network. 2019 2nd West Asian Colloquium on Optical Wireless Communications (WACOWC). :167—170.
In this paper, the physical layer (PHY)security performance of a dual-hop cooperative relaying in a cognitive-radio system in the presence of an eavesdropper is investigated. The dual-hop transmission is composed of an asymmetric radio frequency (RF)link and a free space optical (FSO)link. In the considered system, an unlicensed secondary user (SU)uses the spectrum which is shared by a licensed primary user (PU)in a controlled manner to keep the interference at PU receiver, below a predefined value. Furthermore, among M available relays, one relay with the best end-to-end signal-to-noise-ratio (SNR)is selected for transmission. It is assumed that all of the RF links follow Rayleigh fading and all of the FSO links follow Gamma-Gamma distribution. Simulations results for some important security metrics, such as the average secrecy capacity (SC), and secrecy outage probability (SOP)are presented, where some practical issues of FSO links such as atmospheric turbulence, and pointing errors are taken into consideration.
Yao, Bing, Zhao, Meimei, Mu, Yarong, Sun, Yirong, Zhang, Xiaohui, Zhang, Mingjun, Yang, Sihua.  2019.  Matrices From Topological Graphic Coding of Network Security. 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). 1:1992—1996.
Matrices as mathematical models have been used in each branch of scientific fields for hundred years. We propose a new type of matrices, called topological coding matrices (Topcode-matrices). Topcode-matrices show us the following advantages: Topcode-matrices can be saved in computer easily and run quickly in computation; since a Topcode-matrix corresponds two or more Topsnut-gpws, so Topcode-matrices can be used to encrypt networks such that the encrypted networks have higher security; Topcode-matrices can be investigated and applied by people worked in more domains; Topcode-matrices can help us to form new operations, new parameters and new topics of graph theory, such as vertex/edge splitting operations and connectivities of graphs. Several properties and applications on Topcode-matrices, and particular Topcode-matrices, as well as unknown problems are introduced.
Yudin, Oleksandr, Ziubina, Ruslana, Buchyk, Serhii, Frolov, Oleg, Suprun, Olha, Barannik, Natalia.  2019.  Efficiency Assessment of the Steganographic Coding Method with Indirect Integration of Critical Information. 2019 IEEE International Conference on Advanced Trends in Information Theory (ATIT). :36—40.
The presented method of encoding and steganographic embedding of a series of bits for the hidden message was first developed by modifying the digital platform (bases) of the elements of the image container. Unlike other methods, steganographic coding and embedding is accomplished by changing the elements of the image fragment, followed by the formation of code structures for the established structure of the digital representation of the structural elements of the image media image. The method of estimating quantitative indicators of embedded critical data is presented. The number of bits of the container for the developed method of steganographic coding and embedding of critical information is estimated. The efficiency of the presented method is evaluated and the comparative analysis of the value of the embedded digital data in relation to the method of weight coefficients of the discrete cosine transformation matrix, as well as the comparative analysis of the developed method of steganographic coding, compared with the Koch and Zhao methods to determine the embedded data resistance against attacks of various types. It is determined that for different values of the quantization coefficient, the most critical are the built-in containers of critical information, which are built by changing the part of the digital video data platform depending on the size of the digital platform and the number of bits of the built-in container.
Kleckler, Michelle, Mohajer, Soheil.  2019.  Secure Determinant Codes: A Class of Secure Exact-Repair Regenerating Codes. 2019 IEEE International Symposium on Information Theory (ISIT). :211—215.
{1 We present a construction for exact-repair regenerating codes with an information-theoretic secrecy guarantee against an eavesdropper with access to the content of (up to) ℓ nodes. The proposed construction works for the entire range of per-node storage and repair bandwidth for any distributed storage system with parameters (n
Hao, Jie, Shum, Kenneth W., Xia, Shu-Tao, Yang, Yi-Xian.  2019.  Classification of Optimal Ternary (r, δ)-Locally Repairable Codes Attaining the Singleton-like Bound. 2019 IEEE International Symposium on Information Theory (ISIT). :2828—2832.
In a linear code, a code symbol with (r, δ)-locality can be repaired by accessing at most r other code symbols in case of at most δ - 1 erasures. A q-ary (n, k, r, δ) locally repairable codes (LRC) in which every code symbol has (r, δ)-locality is said to be optimal if it achieves the Singleton-like bound derived by Prakash et al.. In this paper, we study the classification of optimal ternary (n, k, r, δ)-LRCs (δ \textbackslashtextgreater 2). Firstly, we propose an upper bound on the minimum distance of optimal q-ary LRCs in terms of the field size. Then, we completely determine all the 6 classes of possible parameters with which optimal ternary (n, k, r, δ)-LRCs exist. Moreover, explicit constructions of all these 6 classes of optimal ternary LRCs are proposed in the paper.
Jayapalan, Avila, Savarinathan, Prem, Priya, Apoorva.  2019.  SystemVue based Secure data transmission using Gold codes. 2019 International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN). :1—4.
Wireless technology has seen a tremendous growth in the recent past. Orthogonal Frequency Division Multiplexing (OFDM) modulation scheme has been utilized in almost all the advanced wireless techniques because of the advantages it offers. Hence in this aspect, SystemVue based OFDM transceiver has been developed with AWGN as the channel noise. To mitigate the channel noise Convolutional code with Viterbi decoder has been depicted. Further to protect the information from the malicious users the data is scrambled with the aid of gold codes. The performance of the transceiver is analysed through various Bit Error Rate (BER) versus Signal to Noise Ratio (SNR) graphs.
Besser, Karl-Ludwig, Janda, Carsten R., Lin, Pin-Hsun, Jorswieck, Eduard A..  2019.  Flexible Design of Finite Blocklength Wiretap Codes by Autoencoders. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). :2512—2516.
With an increasing number of wireless devices, the risk of being eavesdropped increases as well. From information theory, it is well known that wiretap codes can asymptotically achieve vanishing decoding error probability at the legitimate receiver while also achieving vanishing leakage to eavesdroppers. However, under finite blocklength, there exists a tradeoff among different parameters of the transmission. In this work, we propose a flexible wiretap code design for Gaussian wiretap channels under finite blocklength by neural network autoencoders. We show that the proposed scheme has higher flexibility in terms of the error rate and leakage tradeoff, compared to the traditional codes.
2020-09-11
Shukla, Ankur, Katt, Basel, Nweke, Livinus Obiora.  2019.  Vulnerability Discovery Modelling With Vulnerability Severity. 2019 IEEE Conference on Information and Communication Technology. :1—6.
Web browsers are primary targets of attacks because of their extensive uses and the fact that they interact with sensitive data. Vulnerabilities present in a web browser can pose serious risk to millions of users. Thus, it is pertinent to address these vulnerabilities to provide adequate protection for personally identifiable information. Research done in the past has showed that few vulnerability discovery models (VDMs) highlight the characterization of vulnerability discovery process. In these models, severity which is one of the most crucial properties has not been considered. Vulnerabilities can be categorized into different levels based on their severity. The discovery process of each kind of vulnerabilities is different from the other. Hence, it is essential to incorporate the severity of the vulnerabilities during the modelling of the vulnerability discovery process. This paper proposes a model to assess the vulnerabilities present in the software quantitatively with consideration for the severity of the vulnerabilities. It is possible to apply the proposed model to approximate the number of vulnerabilities along with vulnerability discovery rate, future occurrence of vulnerabilities, risk analysis, etc. Vulnerability data obtained from one of the major web browsers (Google Chrome) is deployed to examine goodness-of-fit and predictive capability of the proposed model. Experimental results justify the fact that the model proposed herein can estimate the required information better than the existing VDMs.