Biblio

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2021-02-16
Navabi, S., Nayyar, A..  2020.  A Dynamic Mechanism for Security Management in Multi-Agent Networked Systems. IEEE INFOCOM 2020 - IEEE Conference on Computer Communications. :1628—1637.
We study the problem of designing a dynamic mechanism for security management in an interconnected multi-agent system with N strategic agents and one coordinator. The system is modeled as a network of N vertices. Each agent resides in one of the vertices of the network and has a privately known security state that describes its safety level at each time. The evolution of an agent's security state depends on its own state, the states of its neighbors in the network and on actions taken by a network coordinator. Each agent's utility at time instant t depends on its own state, the states of its neighbors in the network and on actions taken by a network coordinator. The objective of the network coordinator is to take security actions in order to maximize the long-term expected social surplus. Since agents are strategic and their security states are private information, the coordinator needs to incentivize agents to reveal their information. This results in a dynamic mechanism design problem for the coordinator. We leverage the inter-temporal correlations between the agents' security states to identify sufficient conditions under which an incentive compatible expected social surplus maximizing mechanism can be constructed. We then identify two special cases of our formulation and describe how the desired mechanism is constructed in these cases.
2021-03-04
Kostromitin, K. I., Dokuchaev, B. N., Kozlov, D. A..  2020.  Analysis of the Most Common Software and Hardware Vulnerabilities in Microprocessor Systems. 2020 International Russian Automation Conference (RusAutoCon). :1031—1036.

The relevance of data protection is related to the intensive informatization of various aspects of society and the need to prevent unauthorized access to them. World spending on ensuring information security (IS) for the current state: expenses in the field of IS today amount to \$81.7 billion. Expenditure forecast by 2020: about \$105 billion [1]. Information protection of military facilities is the most critical in the public sector, in the non-state - financial organizations is one of the leaders in spending on information protection. An example of the importance of IS research is the Trojan encoder WannaCry, which infected hundreds of thousands of computers around the world, attacks are recorded in more than 116 countries. The attack of the encoder of WannaCry (Wana Decryptor) happens through a vulnerability in service Server Message Block (protocol of network access to file systems) of Windows OS. Then, a rootkit (a set of malware) was installed on the infected system, using which the attackers launched an encryption program. Then each vulnerable computer could become infected with another infected device within one local network. Due to these attacks, about \$70,000 was lost (according to data from 18.05.2017) [2]. It is assumed in the presented work, that the software level of information protection is fundamentally insufficient to ensure the stable functioning of critical objects. This is due to the possible hardware implementation of undocumented instructions, discussed later. The complexity of computing systems and the degree of integration of their components are constantly growing. Therefore, monitoring the operation of the computer hardware is necessary to achieve the maximum degree of protection, in particular, data processing methods.

2020-11-09
Karmakar, R., Jana, S. S., Chattopadhyay, S..  2019.  A Cellular Automata Guided Obfuscation Strategy For Finite-State-Machine Synthesis. 2019 56th ACM/IEEE Design Automation Conference (DAC). :1–6.
A popular countermeasure against IP piracy relies on obfuscating the Finite State Machine (FSM), which is assumed to be the heart of a digital system. In this paper, we propose to use a special class of non-group additive cellular automata (CA) called D1 * CA, and it's counterpart D1 * CAdual to obfuscate each state-transition of an FSM. The synthesized FSM exhibits correct state-transitions only for a correct key, which is a designer's secret. The proposed easily testable key-controlled FSM synthesis scheme can thwart reverse engineering attacks, thus offers IP protection.
2020-01-13
Hu, Jizhou, Qu, Hemi, Guo, Wenlan, Chang, Ye, Pang, Wei, Duan, Xuexin.  2019.  Film Bulk Acoustic Wave Resonator for Trace Chemical Warfare Agents Simulants Detection in Micro Chromatography. 2019 20th International Conference on Solid-State Sensors, Actuators and Microsystems Eurosensors XXXIII (TRANSDUCERS EUROSENSORS XXXIII). :45–48.
This paper reported the polymer coated film bulk acoustic resonators (FBAR) as a sensitive detector in micro chromatography for the detection of trace chemical warfare agents (CWA) simulants. The FBAR sensor was enclosed in a microfluidic channel and then coupled with microfabricated separation column. The subsequent chromatographic analysis successfully demonstrated the detection of parts per billion (ppb) concentrations of chemical warfare agents (CWAs) simulants in a five components gas mixture. This work represented an important step toward the realization of FBAR based handheld micro chromatography for CWA detection in the field.
2020-08-07
Yan, Dingyu, Liu, Feng, Jia, Kun.  2019.  Modeling an Information-Based Advanced Persistent Threat Attack on the Internal Network. ICC 2019 - 2019 IEEE International Conference on Communications (ICC). :1—7.
An advanced persistent threat (APT) attack is a powerful cyber-weapon aimed at the specific targets in cyberspace. The sophisticated attack techniques, long dwell time and specific objectives make the traditional defense mechanism ineffective. However, most existing studies fail to consider the theoretical modeling of the whole APT attack. In this paper, we mainly establish a theoretical framework to characterize an information-based APT attack on the internal network. In particular, our mathematical framework includes the initial entry model for selecting the entry points and the targeted attack model for studying the intelligence gathering, strategy decision-making, weaponization and lateral movement. Through a series of simulations, we find the optimal candidate nodes in the initial entry model, observe the dynamic change of the targeted attack model and verify the characteristics of the APT attack.
Pawlick, Jeffrey, Nguyen, Thi Thu Hang, Colbert, Edward, Zhu, Quanyan.  2019.  Optimal Timing in Dynamic and Robust Attacker Engagement During Advanced Persistent Threats. 2019 International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOPT). :1—8.
Advanced persistent threats (APTs) are stealthy attacks which make use of social engineering and deception to give adversaries insider access to networked systems. Against APTs, active defense technologies aim to create and exploit information asymmetry for defenders. In this paper, we study a scenario in which a powerful defender uses honeynets for active defense in order to observe an attacker who has penetrated the network. Rather than immediately eject the attacker, the defender may elect to gather information. We introduce an undiscounted, infinite-horizon Markov decision process on a continuous state space in order to model the defender's problem. We find a threshold of information that the defender should gather about the attacker before ejecting him. Then we study the robustness of this policy using a Stackelberg game. Finally, we simulate the policy for a conceptual network. Our results provide a quantitative foundation for studying optimal timing for attacker engagement in network defense.
2020-02-10
Iftikhar, Jawad, Hussain, Sajid, Mansoor, Khwaja, Ali, Zeeshan, Chaudhry, Shehzad Ashraf.  2019.  Symmetric-Key Multi-Factor Biometric Authentication Scheme. 2019 2nd International Conference on Communication, Computing and Digital systems (C-CODE). :288–292.
Authentication is achieved by using different techniques, like using smart-card, identity password and biometric techniques. Some of the proposed schemes use a single factor for authentication while others combine multiple ways to provide multi-factor authentication for better security. lately, a new scheme for multi-factor authentication was presented by Cao and Ge and claimed that their scheme is highly secure and can withstand against all known attacks. In this paper, it is revealed that their scheme is still vulnerable and have some loopholes in term of reflection attack. Therefore, an improved scheme is proposed to overcome the security weaknesses of Cao and Ge's scheme. The proposed scheme resists security attacks and secure. Formal testing is carried out under a broadly-accepted simulated tool ProVerif which demonstrates that the proposed scheme is well secure.
2020-07-06
Xu, Zhiheng, Ng, Daniel Jun Xian, Easwaran, Arvind.  2019.  Automatic Generation of Hierarchical Contracts for Resilience in Cyber-Physical Systems. 2019 IEEE 25th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA). :1–11.

With the growing scale of Cyber-Physical Systems (CPSs), it is challenging to maintain their stability under all operating conditions. How to reduce the downtime and locate the failures becomes a core issue in system design. In this paper, we employ a hierarchical contract-based resilience framework to guarantee the stability of CPS. In this framework, we use Assume Guarantee (A-G) contracts to monitor the non-functional properties of individual components (e.g., power and latency), and hierarchically compose such contracts to deduce information about faults at the system level. The hierarchical contracts enable rapid fault detection in large-scale CPS. However, due to the vast number of components in CPS, manually designing numerous contracts and the hierarchy becomes challenging. To address this issue, we propose a technique to automatically decompose a root contract into multiple lower-level contracts depending on I/O dependencies between components. We then formulate a multi-objective optimization problem to search the optimal parameters of each lower-level contract. This enables automatic contract refinement taking into consideration the communication overhead between components. Finally, we use a case study from the manufacturing domain to experimentally demonstrate the benefits of the proposed framework.

2020-12-11
Huang, N., Xu, M., Zheng, N., Qiao, T., Choo, K. R..  2019.  Deep Android Malware Classification with API-Based Feature Graph. 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :296—303.

The rapid growth of Android malware apps poses a great security threat to users thus it is very important and urgent to detect Android malware effectively. What's more, the increasing unknown malware and evasion technique also call for novel detection method. In this paper, we focus on API feature and develop a novel method to detect Android malware. First, we propose a novel selection method for API feature related with the malware class. However, such API also has a legitimate use in benign app thus causing FP problem (misclassify benign as malware). Second, we further explore structure relationships between these APIs and map to a matrix interpreted as the hand-refined API-based feature graph. Third, a CNN-based classifier is developed for the API-based feature graph classification. Evaluations of a real-world dataset containing 3,697 malware apps and 3,312 benign apps demonstrate that selected API feature is effective for Android malware classification, just top 20 APIs can achieve high F1 of 94.3% under Random Forest classifier. When the available API features are few, classification performance including FPR indicator can achieve effective improvement effectively by complementing our further work.

2020-04-17
Alim, Adil, Zhao, Xujiang, Cho, Jin-Hee, Chen, Feng.  2019.  Uncertainty-Aware Opinion Inference Under Adversarial Attacks. 2019 IEEE International Conference on Big Data (Big Data). :6—15.

Inference of unknown opinions with uncertain, adversarial (e.g., incorrect or conflicting) evidence in large datasets is not a trivial task. Without proper handling, it can easily mislead decision making in data mining tasks. In this work, we propose a highly scalable opinion inference probabilistic model, namely Adversarial Collective Opinion Inference (Adv-COI), which provides a solution to infer unknown opinions with high scalability and robustness under the presence of uncertain, adversarial evidence by enhancing Collective Subjective Logic (CSL) which is developed by combining SL and Probabilistic Soft Logic (PSL). The key idea behind the Adv-COI is to learn a model of robust ways against uncertain, adversarial evidence which is formulated as a min-max problem. We validate the out-performance of the Adv-COI compared to baseline models and its competitive counterparts under possible adversarial attacks on the logic-rule based structured data and white and black box adversarial attacks under both clean and perturbed semi-synthetic and real-world datasets in three real world applications. The results show that the Adv-COI generates the lowest mean absolute error in the expected truth probability while producing the lowest running time among all.

2020-08-28
He, Chengkang, Cui, Aijiao, Chang, Chip-Hong.  2019.  Identification of State Registers of FSM Through Full Scan by Data Analytics. 2019 Asian Hardware Oriented Security and Trust Symposium (AsianHOST). :1—6.

Finite-state machine (FSM) is widely used as control unit in most digital designs. Many intellectual property protection and obfuscation techniques leverage on the exponential number of possible states and state transitions of large FSM to secure a physical design with the reason that it is challenging to retrieve the FSM design from its downstream design or physical implementation without knowledge of the design. In this paper, we postulate that this assumption may not be sustainable with big data analytics. We demonstrate by applying a data mining technique to analyze sufficiently large amount of data collected from a full scan design to identify its FSM state registers. An impact metric is introduced to discriminate FSM state registers from other registers. A decision tree algorithm is constructed from the scan data for the regression analysis of the dependency of other registers on a chosen register to deduce its impact. The registers with the greater impact are more likely to be the FSM state registers. The proposed scheme is applied on several complex designs from OpenCores. The experiment results show the feasibility of our scheme in correctly identifying most FSM state registers with a high hit rate for a large majority of the designs.

2020-01-20
Wu, Di, Chen, Tianen, Chen, Chienfu, Ahia, Oghenefego, Miguel, Joshua San, Lipasti, Mikko, Kim, Younghyun.  2019.  SECO: A Scalable Accuracy Approximate Exponential Function Via Cross-Layer Optimization. 2019 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED). :1–6.

From signal processing to emerging deep neural networks, a range of applications exhibit intrinsic error resilience. For such applications, approximate computing opens up new possibilities for energy-efficient computing by producing slightly inaccurate results using greatly simplified hardware. Adopting this approach, a variety of basic arithmetic units, such as adders and multipliers, have been effectively redesigned to generate approximate results for many error-resilient applications.In this work, we propose SECO, an approximate exponential function unit (EFU). Exponentiation is a key operation in many signal processing applications and more importantly in spiking neuron models, but its energy-efficient implementation has been inadequately explored. We also introduce a cross-layer design method for SECO to optimize the energy-accuracy trade-off. At the algorithm level, SECO offers runtime scaling between energy efficiency and accuracy based on approximate Taylor expansion, where the error is minimized by optimizing parameters using discrete gradient descent at design time. At the circuit level, our error analysis method efficiently explores the design space to select the energy-accuracy-optimal approximate multiplier at design time. In tandem, the cross-layer design and runtime optimization method are able to generate energy-efficient and accurate approximate EFU designs that are up to 99.7% accurate at a power consumption of 3.73 pJ per exponential operation. SECO is also evaluated on the adaptive exponential integrate-and-fire neuron model, yielding only 0.002% timing error and 0.067% value error compared to the precise neuron model.

2020-09-21
Arrieta, Miguel, Esnaola, Iñaki, Effros, Michelle.  2019.  Universal Privacy Guarantees for Smart Meters. 2019 IEEE International Symposium on Information Theory (ISIT). :2154–2158.
Smart meters enable improvements in electricity distribution system efficiency at some cost in customer privacy. Users with home batteries can mitigate this privacy loss by applying charging policies that mask their underlying energy use. A battery charging policy is proposed and shown to provide universal privacy guarantees subject to a constraint on energy cost. The guarantee bounds our strategy's maximal information leakage from the user to the utility provider under general stochastic models of user energy consumption. The policy construction adapts coding strategies for non-probabilistic permuting channels to this privacy problem.
2020-02-24
Lisec, Thomas, Bodduluri, Mani Teja, Schulz-Walsemann, Arne-Veit, Blohm, Lars, Pieper, Isa, Gu-Stoppel, Shanshan, Niekiel, Florian, Lofink, Fabian, Wagner, Bernhard.  2019.  Integrated High Power Micro Magnets for MEMS Sensors and Actuators. 2019 20th International Conference on Solid-State Sensors, Actuators and Microsystems Eurosensors XXXIII (TRANSDUCERS EUROSENSORS XXXIII). :1768–1771.
Back-end-of-line compatible integration of NdFeB-based micro magnets onto 8 inch Si substrates is presented. Substrate conditioning procedures to enable further processing in a cleanroom environment are discussed. It is shown that permanent magnetic structures with lateral dimensions between 25μm and 2000μm and a depth up to 500μm can be fabricated reliably and reproducibly with a remanent magnetization of 340mT at a standard deviation as low as 5% over the substrate. To illustrate post-processing capabilities, the fabrication of micro magnet arrangements embedded in silicon frames is described.
2020-09-28
Andreoletti, Davide, Rottondi, Cristina, Giordano, Silvia, Verticale, Giacomo, Tornatore, Massimo.  2019.  An Open Privacy-Preserving and Scalable Protocol for a Network-Neutrality Compliant Caching. ICC 2019 - 2019 IEEE International Conference on Communications (ICC). :1–6.
The distribution of video contents generated by Content Providers (CPs) significantly contributes to increase the congestion within the networks of Internet Service Providers (ISPs). To alleviate this problem, CPs can serve a portion of their catalogues to the end users directly from servers (i.e., the caches) located inside the ISP network. Users served from caches perceive an increased QoS (e.g., average retrieval latency is reduced) and, for this reason, caching can be considered a form of traffic prioritization. Hence, since the storage of caches is limited, its subdivision among several CPs may lead to discrimination. A static subdivision that assignes to each CP the same portion of storage is a neutral but ineffective appraoch, because it does not consider the different popularities of the CPs' contents. A more effective strategy consists in dividing the cache among the CPs proportionally to the popularity of their contents. However, CPs consider this information sensitive and are reluctant to disclose it. In this work, we propose a protocol based on Shamir Secret Sharing (SSS) scheme that allows the ISP to calculate the portion of cache storage that a CP is entitled to receive while guaranteeing network neutrality and resource efficiency, but without violating its privacy. The protocol is executed by the ISP, the CPs and a Regulator Authority (RA) that guarantees the actual enforcement of a fair subdivision of the cache storage and the preservation of privacy. We perform extensive simulations and prove that our approach leads to higher hit-rates (i.e., percentage of requests served by the cache) with respect to the static one. The advantages are particularly significant when the cache storage is limited.
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.
2019-11-25
Kışlal, Ahmet Oguz, Pusane, Ali Emre, Tuğcu, Tuna.  2018.  A comparative analysis of channel coding for molecular communication. 2018 26th Signal Processing and Communications Applications Conference (SIU). :1–4.
Networks established among nanomachines, also called nanonetworks, are crucial since, a single nanomachine most likely cannot handle task by itself. At the nano scale, electromagnetic waves lose their effectiveness. Molecular communication via diffusion (MCvD) is a new concept that aims to solve this problem. Information is carried out by either the type of molecules, or their concentration. The robustness of this communication method, as in the example of classical communication, is very important. Channel coding is the component that make communication less erroneous. If the desired error performance is high, channel coding is mandatory. In this paper, the performance of Bose-Chaudhuri-Hocquenghem (BCH) and Reed-Solomon (RS) codes for MCvD are evaluated by simulation and results are analyzed.
2021-04-08
Roy, P., Mazumdar, C..  2018.  Modeling of Insider Threat using Enterprise Automaton. 2018 Fifth International Conference on Emerging Applications of Information Technology (EAIT). :1—4.
Substantial portions of attacks on the security of enterprises are perpetrated by Insiders having authorized privileges. Thus insider threat and attack detection is an important aspect of Security management. In the published literature, efforts are on to model the insider threats based on the behavioral traits of employees. The psycho-social behaviors are hard to encode in the software systems. Also, in some cases, there are privacy issues involved. In this paper, the human and non-human agents in a system are described in a novel unified model. The enterprise is described as an automaton and its states are classified secure, safe, unsafe and compromised. The insider agents and threats are modeled on the basis of the automaton and the model is validated using a case study.
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.
2019-06-28
Kulik, T., Tran-Jørgensen, P. W. V., Boudjadar, J., Schultz, C..  2018.  A Framework for Threat-Driven Cyber Security Verification of IoT Systems. 2018 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW). :89-97.

Industrial control systems are changing from monolithic to distributed and interconnected architectures, entering the era of industrial IoT. One fundamental issue is that security properties of such distributed control systems are typically only verified empirically, during development and after system deployment. We propose a novel modelling framework for the security verification of distributed industrial control systems, with the goal of moving towards early design stage formal verification. In our framework we model industrial IoT infrastructures, attack patterns, and mitigation strategies for countering attacks. We conduct model checking-based formal analysis of system security through scenario execution, where the analysed system is exposed to attacks and implement mitigation strategies. We study the applicability of our framework for large systems using a scalability analysis.

2019-03-15
Ye, J., Yang, Y., Gong, Y., Hu, Y., Li, X..  2018.  Grey Zone in Pre-Silicon Hardware Trojan Detection. 2018 IEEE International Test Conference in Asia (ITC-Asia). :79-84.

Pre-Silicon hardware Trojan detection has been studied for years. The most popular benchmark circuits are from the Trust-Hub. Their common feature is that the probability of activating hardware Trojans is very low. This leads to a series of machine learning based hardware Trojan detection methods which try to find the nets with low signal probability of 0 or 1. On the other hand, it is considered that, if the probability of activating hardware Trojans is high, these hardware Trojans can be easily found through behaviour simulations or during functional test. This paper explores the "grey zone" between these two opposite scenarios: if the activation probability of a hardware Trojan is not low enough for machine learning to detect it and is not high enough for behaviour simulation or functional test to find it, it can escape from detection. Experiments show the existence of such hardware Trojans, and this paper suggests a new set of hardware Trojan benchmark circuits for future study.

2019-03-06
Man, Y., Ding, L., Xiaoguo, Z..  2018.  Nonlinear System Identification Method Based on Improved Deep Belief Network. 2018 Chinese Automation Congress (CAC). :2379-2383.

Accurate model is very important for the control of nonlinear system. The traditional identification method based on shallow BP network is easy to fall into local optimal solution. In this paper, a modeling method for nonlinear system based on improved Deep Belief Network (DBN) is proposed. Continuous Restricted Boltzmann Machine (CRBM) is used as the first layer of the DBN, so that the network can more effectively deal with the actual data collected from the real systems. Then, the unsupervised training and supervised tuning were combine to improve the accuracy of identification. The simulation results show that the proposed method has a higher identification accuracy. Finally, this improved algorithm is applied to identification of diameter model of silicon single crystal and the simulation results prove its excellent ability of parameters identification.

2019-12-05
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.

2019-03-15
Xue, M., Bian, R., Wang, J., Liu, W..  2018.  A Co-Training Based Hardware Trojan Detection Technique by Exploiting Unlabeled ICs and Inaccurate Simulation Models. 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :1452-1457.

Integrated circuits (ICs) are becoming vulnerable to hardware Trojans. Most of existing works require golden chips to provide references for hardware Trojan detection. However, a golden chip is extremely difficult to obtain. In previous work, we have proposed a classification-based golden chips-free hardware Trojan detection technique. However, the algorithm in the previous work are trained by simulated ICs without considering that there may be a shift which occurs between the simulation and the silicon fabrication. It is necessary to learn from actual silicon fabrication in order to obtain an accurate and effective classification model. We propose a co-training based hardware Trojan detection technique exploiting unlabeled fabricated ICs and inaccurate simulation models, to provide reliable detection capability when facing fabricated ICs, while eliminating the need of fabricated golden chips. First, we train two classification algorithms using simulated ICs. During test-time, the two algorithms can identify different patterns in the unlabeled ICs, and thus be able to label some of these ICs for the further training of the another algorithm. Moreover, we use a statistical examination to choose ICs labeling for the another algorithm in order to help prevent a degradation in performance due to the increased noise in the labeled ICs. We also use a statistical technique for combining the hypotheses from the two classification algorithms to obtain the final decision. The theoretical basis of why the co-training method can work is also described. Experiment results on benchmark circuits show that the proposed technique can detect unknown Trojans with high accuracy (92% 97%) and recall (88% 95%).