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Aqil, Azeem, Khalil, Karim, Atya, Ahmed O.F., Papalexakis, Evangelos E., Krishnamurthy, Srikanth V., Jaeger, Trent, Ramakrishnan, K. K., Yu, Paul, Swami, Ananthram.  2017.  Jaal: Towards Network Intrusion Detection at ISP Scale. Proceedings of the 13th International Conference on Emerging Networking EXperiments and Technologies. :134–146.
We have recently seen an increasing number of attacks that are distributed, and span an entire wide area network (WAN). Today, typically, intrusion detection systems (IDSs) are deployed at enterprise scale and cannot handle attacks that cover a WAN. Moreover, such IDSs are implemented at a single entity that expects to look at all packets to determine an intrusion. Transferring copies of raw packets to centralized engines for analysis in a WAN can significantly impact both network performance and detection accuracy. In this paper, we propose Jaal, a framework for achieving accurate network intrusion detection at scale. The key idea in Jaal is to monitor traffic and construct in-network packet summaries. The summaries are then processed centrally to detect attacks with high accuracy. The main challenges that we address are (a) creating summaries that are concise, but sufficient to draw highly accurate inferences and (b) transforming traditional IDS rules to handle summaries instead of raw packets. We implement Jaal on a large scale SDN testbed. We show that on average Jaal yields a detection accuracy of about 98%, which is the highest reported for ISP scale network intrusion detection. At the same time, the overhead associated with transferring summaries to the central inference engine is only about 35% of what is consumed if raw packets are transferred.
Almeida, José Bacelar, Barbosa, Manuel, Barthe, Gilles, Blot, Arthur, Grégoire, Benjamin, Laporte, Vincent, Oliveira, Tiago, Pacheco, Hugo, Schmidt, Benedikt, Strub, Pierre-Yves.  2017.  Jasmin: High-Assurance and High-Speed Cryptography. Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. :1807–1823.
Jasmin is a framework for developing high-speed and high-assurance cryptographic software. The framework is structured around the Jasmin programming language and its compiler. The language is designed for enhancing portability of programs and for simplifying verification tasks. The compiler is designed to achieve predictability and efficiency of the output code (currently limited to x64 platforms), and is formally verified in the Coq proof assistant. Using the supercop framework, we evaluate the Jasmin compiler on representative cryptographic routines and conclude that the code generated by the compiler is as efficient as fast, hand-crafted, implementations. Moreover, the framework includes highly automated tools for proving memory safety and constant-time security (for protecting against cache-based timing attacks). We also demonstrate the effectiveness of the verification tools on a large set of cryptographic routines.
Kishore, Pushkar, Barisal, Swadhin Kumar, Prasad Mohapatra, Durga.  2020.  JavaScript malware behaviour analysis and detection using sandbox assisted ensemble model. 2020 IEEE REGION 10 CONFERENCE (TENCON). :864—869.

Whenever any internet user visits a website, a scripting language runs in the background known as JavaScript. The embedding of malicious activities within the script poses a great threat to the cyberworld. Attackers take advantage of the dynamic nature of the JavaScript and embed malicious code within the website to download malware and damage the host. JavaScript developers obfuscate the script to keep it shielded from getting detected by the malware detectors. In this paper, we propose a novel technique for analysing and detecting JavaScript using sandbox assisted ensemble model. We extract the payload using malware-jail sandbox to get the real script. Upon getting the extracted script, we analyse it to define the features that are needed for creating the dataset. We compute Pearson's r between every feature for feature extraction. An ensemble model consisting of Sequential Minimal Optimization (SMO), Voted Perceptron and AdaBoost algorithm is used with voting technique to detect malicious JavaScript. Experimental results show that our proposed model can detect obfuscated and de-obfuscated malicious JavaScript with an accuracy of 99.6% and 0.03s detection time. Our model performs better than other state-of-the-art models in terms of accuracy and least training and detection time.

Mavroudis, V., Svenda, P..  2020.  JCMathLib: Wrapper Cryptographic Library for Transparent and Certifiable JavaCard Applets. 2020 IEEE European Symposium on Security and Privacy Workshops (EuroS PW). :89—96.

The JavaCard multi-application platform is now deployed to over twenty billion smartcards, used in various applications ranging from banking payments and authentication tokens to SIM cards and electronic documents. In most of those use cases, access to various cryptographic primitives is required. The standard JavaCard API provides a basic level of access to such functionality (e.g., RSA encryption) but does not expose low-level cryptographic primitives (e.g., elliptic curve operations) and essential data types (e.g., Integers). Developers can access such features only through proprietary, manufacturer-specific APIs. Unfortunately, such APIs significantly reduce the interoperability and certification transparency of the software produced as they require non-disclosure agreements (NDA) that prohibit public sharing of the applet's source code.We introduce JCMathLib, an open library that provides an intermediate layer realizing essential data types and low-level cryptographic primitives from high-level operations. To achieve this, we introduce a series of optimization techniques for resource-constrained platforms that make optimal use of the underlying hardware, while having a small memory footprint. To the best of our knowledge, it is the first generic library for low-level cryptographic operations in JavaCards that does not rely on a proprietary API.Without any disclosure limitations, JCMathLib has the potential to increase transparency by enabling open code sharing, release of research prototypes, and public code audits. Moreover, JCMathLib can help resolve the conflict between strict open-source licenses such as GPL and proprietary APIs available only under an NDA. This is of particular importance due to the introduction of JavaCard API v3.1, which targets specifically IoT devices, where open-source development might be more common than in the relatively closed world of government-issued electronic documents.

Dong, Yao, Milanova, Ana, Dolby, Julian.  2016.  JCrypt: Towards Computation over Encrypted Data. Proceedings of the 13th International Conference on Principles and Practices of Programming on the Java Platform: Virtual Machines, Languages, and Tools. :8:1–8:12.

Cloud computing allows clients to upload data and computation to untrusted servers, which leads to potential violations to the confidentiality of client data. We propose JCrypt, a static program analysis which transforms a Java program into an equivalent one, so that it performs computation over encrypted data and preserves data confidentiality. JCrypt minimizes computation over encrypted data. It consists of two stages. The first stage is a type-based information flow analysis which partitions the program so that only sensitive parts need to be encrypted. The second stage is an inter-procedural data-flow analysis, similar to the classical Available Expressions. It deduces the appropriate encryption scheme for sensitive variables. We implemented JCrypt for Java and showed that our analysis is effective and practical using five benchmark suites. JCrypt encrypts a significantly larger percentage of benchmarks compared to MrCrypt, the closest related work.

Mangaokar, N., Pu, J., Bhattacharya, P., Reddy, C. K., Viswanath, B..  2020.  Jekyll: Attacking Medical Image Diagnostics using Deep Generative Models. 2020 IEEE European Symposium on Security and Privacy (EuroS P). :139–157.
Advances in deep neural networks (DNNs) have shown tremendous promise in the medical domain. However, the deep learning tools that are helping the domain, can also be used against it. Given the prevalence of fraud in the healthcare domain, it is important to consider the adversarial use of DNNs in manipulating sensitive data that is crucial to patient healthcare. In this work, we present the design and implementation of a DNN-based image translation attack on biomedical imagery. More specifically, we propose Jekyll, a neural style transfer framework that takes as input a biomedical image of a patient and translates it to a new image that indicates an attacker-chosen disease condition. The potential for fraudulent claims based on such generated `fake' medical images is significant, and we demonstrate successful attacks on both X-rays and retinal fundus image modalities. We show that these attacks manage to mislead both medical professionals and algorithmic detection schemes. Lastly, we also investigate defensive measures based on machine learning to detect images generated by Jekyll.
Pooja, B. P., Manish, M. P., Megha, B. P..  2017.  Jellyfish attack detection and prevention in MANET. 2017 Third International Conference on Sensing, Signal Processing and Security (ICSSS). :54–60.

Jellyfish attack is type of DoS attack which is difficult to detect and prevent. Jellyfish attack is categorized as JF Reorder Attack, JF Periodic Dropping Attack and JF Delay Variance Attack. JF attack delay data packets for some amount of time before forwarding and after reception which results high end-to-end delay in the network. JF Attack disrupts whole functionality of transmission and reduces the performance of network. In this paper difference of receive time and sending time greater than threshold value then delay occur due to congestion or availability of JF nodes that confirm by checking load of network. This way detect and prevent jellyfish attack.

Petrić, Jean, Bowes, David, Hall, Tracy, Christianson, Bruce, Baddoo, Nathan.  2016.  The Jinx on the NASA Software Defect Data Sets. Proceedings of the 20th International Conference on Evaluation and Assessment in Software Engineering. :13:1–13:5.

Background: The NASA datasets have previously been used extensively in studies of software defects. In 2013 Shepperd et al. presented an essential set of rules for removing erroneous data from the NASA datasets making this data more reliable to use. Objective: We have now found additional rules necessary for removing problematic data which were not identified by Shepperd et al. Results: In this paper, we demonstrate the level of erroneous data still present even after cleaning using Shepperd et al.'s rules and apply our new rules to remove this erroneous data. Conclusion: Even after systematic data cleaning of the NASA MDP datasets, we found new erroneous data. Data quality should always be explicitly considered by researchers before use.

Ignacio X. Dominguez, Jayant Dhawan, Robert St. Amant, David L. Roberts.  2016.  JIVUI: JavaScript Interface for Visualization of User Interaction. Proceedings of the International Conference on Cognitive Modeling (ICCM). :125–130.

In this paper we describe the JavaScript Interface for Visu- alization of User Interaction (JIVUI): a modular, Web-based, and customizable visualization tool that shows an animation of the trace of a user interaction with a graphical interface, or of predictions made by cognitive models of user interaction. Any combination of gaze, mouse, and keyboard data can be repro- duced within a user-provided interface. Although customiz- able, the tool includes a series of plug-ins to support common visualization tasks, including a timeline of input device events and perceptual and cognitive operators based on the Model Hu- man Processor and TYPIST. We talk about our use of this tool to support hypothesis generation, assumption validation, and to guide our modeling efforts. 

Thomé, Julian, Shar, Lwin Khin, Bianculli, Domenico, Briand, Lionel C..  2017.  JoanAudit: A Tool for Auditing Common Injection Vulnerabilities. Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering. :1004–1008.

JoanAudit is a static analysis tool to assist security auditors in auditing Web applications and Web services for common injection vulnerabilities during software development. It automatically identifies parts of the program code that are relevant for security and generates an HTML report to guide security auditors audit the source code in a scalable way. JoanAudit is configured with various security-sensitive input sources and sinks relevant to injection vulnerabilities and standard sanitization procedures that prevent these vulnerabilities. It can also automatically fix some cases of vulnerabilities in source code — cases where inputs are directly used in sinks without any form of sanitization — by using standard sanitization procedures. Our evaluation shows that by using JoanAudit, security auditors are required to inspect only 1% of the total code for auditing common injection vulnerabilities. The screen-cast demo is available at https://github.com/julianthome/joanaudit.

Zhang, Y., Deng, L., Chen, M., Wang, P..  2018.  Joint Bidding and Geographical Load Balancing for Datacenters: Is Uncertainty a Blessing or a Curse? IEEE/ACM Transactions on Networking. 26:1049—1062.

We consider the scenario where a cloud service provider (CSP) operates multiple geo-distributed datacenters to provide Internet-scale service. Our objective is to minimize the total electricity and bandwidth cost by jointly optimizing electricity procurement from wholesale markets and geographical load balancing (GLB), i.e., dynamically routing workloads to locations with cheaper electricity. Under the ideal setting where exact values of market prices and workloads are given, this problem reduces to a simple linear programming and is easy to solve. However, under the realistic setting where only distributions of these variables are available, the problem unfolds into a non-convex infinite-dimensional one and is challenging to solve. One of our main contributions is to develop an algorithm that is proven to solve the challenging problem optimally, by exploring the full design space of strategic bidding. Trace-driven evaluations corroborate our theoretical results, demonstrate fast convergence of our algorithm, and show that it can reduce the cost for the CSP by up to 20% as compared with baseline alternatives. This paper highlights the intriguing role of uncertainty in workloads and market prices, measured by their variances. While uncertainty in workloads deteriorates the cost-saving performance of joint electricity procurement and GLB, counter-intuitively, uncertainty in market prices can be exploited to achieve a cost reduction even larger than the setting without price uncertainty.

Benamira, Elias, Merazka, Fatiha, Kurt, Gunes Karabulut.  2018.  Joint Channel Coding and Cooperative Network Coding on PSK Constellations in Wireless Networks. 2018 International Conference on Smart Communications in Network Technologies (SaCoNeT). :132–137.
In this paper, we consider the application of Reed-Solomon (RS) channel coding for joint error correction and cooperative network coding on non-binary phase shift keying (PSK) modulated signals. The relay first decodes the RS channel coded messages received each in a time slot from all sources before applying network coding (NC) by the use of bit-level exclusive OR (XOR) operation. The network coded resulting message is then channel encoded before its transmission to the next relay or to the destination according to the network configuration. This scenario shows superior performance in comparison with the case where the relay does not perform channel coding/decoding. For different orders of PSK modulation and different wireless configurations, simulation results demonstrate the improvements resulting from the use of RS channel codes in terms of symbol error rate (SER) versus signal-to-noise ratio (SNR).
K, S., Devi, K. Suganya, Srinivasan, P., Dheepa, T., Arpita, B., singh, L. Dolendro.  2020.  Joint Correlated Compressive Sensing based on Predictive Data Recovery in WSNs. 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE). :1–5.
Data sampling is critical process for energy constrained Wireless Sensor Networks. In this article, we proposed a Predictive Data Recovery Compressive Sensing (PDR-CS) procedure for data sampling. PDR-CS samples data measurements from the monitoring field on the basis of spatial and temporal correlation and sparse measurements recovered at the Sink. Our proposed algorithm, PDR-CS extends the iterative re-weighted -ℓ1(IRW - ℓ1) minimization and regularization on the top of Spatio-temporal compressibility for enhancing accuracy of signal recovery and reducing the energy consumption. The simulation study shows that from the less number of samples are enough to recover the signal. And also compared with the other compressive sensing procedures, PDR-CS works with less time.
Zayene, M., Habachi, O., Meghdadi, V., Ezzeddine, T., Cances, J. P..  2017.  Joint delay and energy minimization for Wireless Sensor Networks using instantly decodable network coding. 2017 International Conference on Internet of Things, Embedded Systems and Communications (IINTEC). :21–25.

Most of Wireless Sensor Networks (WSNs) are usually deployed in hostile environments where the communications conditions are not stable and not reliable. Hence, there is a need to design an effective distributed schemes to enable the sensors cooperating in order to recover the sensed data. In this paper, we establish a novel cooperative data exchange (CDE) scheme using instantly decodable network coding (IDNC) across the sensor nodes. We model the problem using the cooperative game theory in partition form. We develop also a distributed merge-and-split algorithm in order to form dynamically coalitions that maximize their utilities in terms of both energy consumption and IDNC delay experienced by all sensors. Indeed, the proposed algorithm enables these sensors to self-organize into stable clustered network structure where all sensors do not have incentives to change the cluster he is part of. Simulation results show that our cooperative scheme allows nodes not only to reduce the energy consumption, but also the IDNC completion time.

Wu, Jie, Li, Hongchun, Xu, Yi, Tian, Jun.  2018.  Joint Design of WiFi Mesh Network for Video Surveillance Application. Proceedings of the 14th ACM International Symposium on QoS and Security for Wireless and Mobile Networks. :140–146.
The ability to transmit high volumes of data over a long distance makes WiFi mesh networks an ideal transmission solution for remote video surveillance. Instead of independently manipulating the node deployment, channel and interface assignment, and routing to improve the network performance, we propose a joint network design using multi-objective genetic algorithm to take into account the interplay of them. Moreover, we found a performance evaluation method based on the transmission capability of the WiFi mesh networks for the first time. The good agreement of our obtained multiple optimized solutions to the extensive simulation results by NS-3 demonstrates the effectiveness of our design.
Kaghaz-Garan, S., Umbarkar, A., Doboli, A..  2014.  Joint localization and fingerprinting of sound sources for auditory scene analysis. Robotic and Sensors Environments (ROSE), 2014 IEEE International Symposium on. :49-54.

In the field of scene understanding, researchers have mainly focused on using video/images to extract different elements in a scene. The computational as well as monetary cost associated with such implementations is high. This paper proposes a low-cost system which uses sound-based techniques in order to jointly perform localization as well as fingerprinting of the sound sources. A network of embedded nodes is used to sense the sound inputs. Phase-based sound localization and Support-Vector Machine classification are used to locate and classify elements of the scene, respectively. The fusion of all this data presents a complete “picture” of the scene. The proposed concepts are applied to a vehicular-traffic case study. Experiments show that the system has a fingerprinting accuracy of up to 97.5%, localization error less than 4 degrees and scene prediction accuracy of 100%.

Wang, X., Teng, Y., Song, M., Wang, X., Yuan, H..  2015.  Joint Optimization of Coverage and Capacity Based on Power Density Distribution in Heterogeneous Cellular Networks. 2015 Fifth International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC). :251–255.

The paper presents a joint optimization algorithm for coverage and capacity in heterogeneous cellular networks. A joint optimization objective related to capacity loss considering both coverage hole and overlap area based on power density distribution is proposed. The optimization object is a NP problem due to that the adjusting parameters are mixed with discrete and continuous, so the bacterial foraging (BF) algorithm is improved based on network performance analysis result to find a more effective direction than randomly selected. The results of simulation show that the optimization object is feasible gains a better effect than traditional method.

Xia, Huiyun, Han, Shuai, Li, Cheng, Meng, Weixiao.  2019.  Joint PHY/MAC Layer AN-Assisted Security Scheme in SVD-Based MIMO HARQ system. 2019 IEEE/CIC International Conference on Communications in China (ICCC). :328–333.
With the explosive data growth arise from internet of things, how to ensure information security is facing unprecedented challenges. In this paper, a joint PHY/MAC layer security scheme with artificial noise design in singular value decomposition (SVD) based multiple input multiple output hybrid automatic retransmission request (MIMO HARQ) system is proposed to resolve the problem of low data rates in existing cross-layer security design and further adapt to the high data rate requirement of 5G. First, the SVD was applied to simplify MIMO systems into several parallel sub-channels employing HARQ protocol. Then, different from traditional null space based artificial noise design, the artificial noise design, which is dependent on the characteristics of channel states and transmission rounds, is detailed presented. Finally, the analytical and simulation results proved that with the help of the proposed artificial noise, both the information security and data rate performance can be significantly improved compared with that in single input single output (SISO) system.
Deka, Surajit, Sarma, Kandarpa Kumar.  2018.  Joint Source Channel Coding with Bandwidth Compression. 2018 5th International Conference on Signal Processing and Integrated Networks (SPIN). :286–290.
In this paper, we have considered the broadcasting of a memoryless bivariate Gaussian source over a Gaussian broadcast channel with respect to bandwidth compression. We have analysed the performance of a hybrid digital-analog (HDA) coding system in combination with joint source channel coding (JSCC) to measure the distortion regions. The transmission advantages due to the combination of both the analog and digital techniques, a class of HDA schemes that yields better performance in distortion is discussed. The performance of source and channel coding for the possible better outcome of the system is measured by employing Wyner-Ziv and Costa coding. In our model, we have considered the upper layer to be a combination of a hybrid layer in the sense of both the analog and digital processing is done. This is executed in presence of quantization error and performance of the system is measured with two conditions: 1) HDA scheme with quantization scaling factor α = 0, i.e. the input of the channel have only the analog information which is considered as the scaled quantization error βS 2) The analog information from the first layer S is suppressed by setting error scaling factor β = 0 and 3) Inclusion of recursive mode with JSCC in each of the three layers for the possible better outcome is considered here.
Weng, Jian-Jian, Alajaji, Fady, Linder, Tamás.  2019.  Joint Source-Channel Coding for the Transmission of Correlated Sources over Two-Way Channels. 2019 IEEE International Symposium on Information Theory (ISIT). :1322–1326.
A joint source-channel coding (JSCC) scheme based on hybrid digital/analog coding is proposed for the transmission of correlated sources over discrete-memoryless two-way channels (DM-TWCs). The scheme utilizes the correlation between the sources in generating channel inputs, thus enabling the users to coordinate their transmission to combat channel noise. The hybrid scheme also subsumes prior coding methods such as rate-one separate source-channel coding and uncoded schemes for two-way lossy transmission, as well as the correlation-preserving coding scheme for (almost) lossless transmission. Moreover, we derive a distortion outer bound for the source-channel system using a genie-aided argument. A complete JSSC theorem for a class of correlated sources and DM-TWCs whose capacity region cannot be enlarged via interactive adaptive coding is also established. Examples that illustrate the theorem are given.
Ali, Waqas, Abbas, Ghulam, Abbas, Ziaul Haq.  2019.  Joint Sybil Attack Prevention and Energy Conservation in Wireless Sensor Networks. 2019 International Conference on Frontiers of Information Technology (FIT). :179–1795.

Sybil attacks, wherein a network is subverted by forging node identities, remains an open issue in wireless sensor networks (WSNs). This paper proposes a scheme, called Location and Communication ID (LCID) based detection, which employs residual energy, communication ID and location information of sensor nodes for Sybil attacks prevention. Moreover, LCID takes into account the resource constrained nature of WSNs and enhances energy conservation through hierarchical routing. Sybil nodes are purged before clusters formation to ensure that only legitimate nodes participate in clustering and data communication. CH selection is based on the average energy of the entire network to load-balance energy consumption. LCID selects a CH if its residual energy is greater than the average network energy. Furthermore, the workload of CHs is equally distributed among sensor nodes. A CH once selected cannot be selected again for 1/p rounds, where p is the CH selection probability. Simulation results demonstrate that, as compared to an eminent scheme, LCID has a higher Sybil attacks detection ratio, higher network lifetime, higher packet reception rate at the BS, lower energy consumption, and lower packet loss ratio.

Mesodiakaki, Agapi, Zola, Enrica, Kassler, Andreas.  2017.  Joint User Association and Backhaul Routing for Green 5G Mesh Millimeter Wave Backhaul Networks. Proceedings of the 20th ACM International Conference on Modelling, Analysis and Simulation of Wireless and Mobile Systems. :179–186.

With the advance of fifth generation (5G) networks, network density needs to grow significantly in order to meet the required capacity demands. A massive deployment of small cells may lead to a high cost for providing fiber connectivity to each node. Consequently, many small cells are expected to be connected through wireless links to the umbrella eNodeB, leading to a mesh backhaul topology. This backhaul solution will most probably be composed of high capacity point-to-point links, typically operating in the millimeter wave (mmWave) frequency band due to its massive bandwidth availability. In this paper, we propose a mathematical model that jointly solves the user association and backhaul routing problem in the aforementioned context, aiming at the energy efficiency maximization of the network. Our study considers the energy consumption of both the access and backhaul links, while taking into account the capacity constraints of all the nodes as well as the fulfillment of the service-level agreements (SLAs). Due to the high complexity of the optimal solution, we also propose an energy efficient heuristic algorithm (Joint), which solves the discussed joint problem, while inducing low complexity in the system. We numerically evaluate the algorithm performance by comparing it not only with the optimal solution but also with reference approaches under different traffic load scenarios and backhaul parameters. Our results demonstrate that Joint outperforms the state-of-the-art, while being able to find good solutions, close to optimal, in short time.

Goudos, S. K., Diamantoulakis, P. D., Boursianis, A. D., Papanikolaou, V. K., Karagiannidis, G. K..  2020.  Joint User Association and Power Allocation Using Swarm Intelligence Algorithms in Non-Orthogonal Multiple Access Networks. 2020 9th International Conference on Modern Circuits and Systems Technologies (MOCAST). :1–4.
In this paper, we address the problem of joint user association and power allocation for non-orthogonal multiple access (NOMA) networks with multiple base stations (BSs). A user grouping procedure into orthogonal clusters, as well as an allocation of different physical resource blocks (PRBs) is considered. The problem of interest is mathematically described using the maximization of the weighted sum rate. We apply two different swarm intelligence algorithms, namely, the recently introduced Grey Wolf Optimizer (GWO), and the popular Particle Swarm Optimization (PSO), in order to solve this problem. Numerical results demonstrate that the above-described problem can be satisfactorily addressed by both algorithms.
Feng, Qi, Huang, Jianjun, Yang, Zhaocheng.  2019.  Jointly Optimized Target Detection and Tracking Using Compressive Samples. IEEE Access. 7:73675–73684.
In this paper, we consider the problem of joint target detection and tracking in compressive sampling and processing (CSP-JDT). CSP can process the compressive samples of sparse signals directly without signal reconstruction, which is suitable for handling high-resolution radar signals. However, in CSP, the radar target detection and tracking problems are usually solved separately or by a two-stage strategy, which cannot obtain a globally optimal solution. To jointly optimize the target detection and tracking performance and inspired by the optimal Bayes joint decision and estimation (JDE) framework, a jointly optimized target detection and tracking algorithm in CSP is proposed. Since detection and tracking are highly correlated, we first develop a measurement matrix construction method to acquire the compressive samples, and then a joint CSP Bayesian approach is developed for target detection and tracking. The experimental results demonstrate that the proposed method outperforms the two-stage algorithms in terms of the joint performance metric.
Abate, Carmine, Blanco, Roberto, Garg, Deepak, Hritcu, Catalin, Patrignani, Marco, Thibault, Jérémy.  2019.  Journey Beyond Full Abstraction: Exploring Robust Property Preservation for Secure Compilation. 2019 IEEE 32nd Computer Security Foundations Symposium (CSF). :256–25615.
Good programming languages provide helpful abstractions for writing secure code, but the security properties of the source language are generally not preserved when compiling a program and linking it with adversarial code in a low-level target language (e.g., a library or a legacy application). Linked target code that is compromised or malicious may, for instance, read and write the compiled program's data and code, jump to arbitrary memory locations, or smash the stack, blatantly violating any source-level abstraction. By contrast, a fully abstract compilation chain protects source-level abstractions all the way down, ensuring that linked adversarial target code cannot observe more about the compiled program than what some linked source code could about the source program. However, while research in this area has so far focused on preserving observational equivalence, as needed for achieving full abstraction, there is a much larger space of security properties one can choose to preserve against linked adversarial code. And the precise class of security properties one chooses crucially impacts not only the supported security goals and the strength of the attacker model, but also the kind of protections a secure compilation chain has to introduce. We are the first to thoroughly explore a large space of formal secure compilation criteria based on robust property preservation, i.e., the preservation of properties satisfied against arbitrary adversarial contexts. We study robustly preserving various classes of trace properties such as safety, of hyperproperties such as noninterference, and of relational hyperproperties such as trace equivalence. This leads to many new secure compilation criteria, some of which are easier to practically achieve and prove than full abstraction, and some of which provide strictly stronger security guarantees. For each of the studied criteria we propose an equivalent “property-free” characterization that clarifies which proof techniques apply. For relational properties and hyperproperties, which relate the behaviors of multiple programs, our formal definitions of the property classes themselves are novel. We order our criteria by their relative strength and show several collapses and separation results. Finally, we adapt existing proof techniques to show that even the strongest of our secure compilation criteria, the robust preservation of all relational hyperproperties, is achievable for a simple translation from a statically typed to a dynamically typed language.