Visible to the public Biblio

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2020-05-11
Mirza, Ali H., Cosan, Selin.  2018.  Computer network intrusion detection using sequential LSTM Neural Networks autoencoders. 2018 26th Signal Processing and Communications Applications Conference (SIU). :1–4.
In this paper, we introduce a sequential autoencoder framework using long short term memory (LSTM) neural network for computer network intrusion detection. We exploit the dimensionality reduction and feature extraction property of the autoencoder framework to efficiently carry out the reconstruction process. Furthermore, we use the LSTM networks to handle the sequential nature of the computer network data. We assign a threshold value based on cross-validation in order to classify whether the incoming network data sequence is anomalous or not. Moreover, the proposed framework can work on both fixed and variable length data sequence and works efficiently for unforeseen and unpredictable network attacks. We then also use the unsupervised version of the LSTM, GRU, Bi-LSTM and Neural Networks. Through a comprehensive set of experiments, we demonstrate that our proposed sequential intrusion detection framework performs well and is dynamic, robust and scalable.
2020-04-06
Kumar, Rakesh, Babu, Vignesh, Nicol, David.  2018.  Network Coding for Critical Infrastructure Networks. 2018 IEEE 26th International Conference on Network Protocols (ICNP). :436–437.
The applications in the critical infrastructure systems pose simultaneous resilience and performance requirements to the underlying computer network. To meet such requirements, the networks that use the store-and-forward paradigm poses stringent conditions on the redundancy in the network topology and results in problems that becoming computationally challenging to solve at scale. However, with the advent of programmable data-planes, it is now possible to use linear network coding (NC) at the intermediate network nodes to meet resilience requirements of the applications. To that end, we propose an architecture that realizes linear NC in programmable networks by decomposing the linear NC functions into the atomic coding primitives. We designed and implemented the primitives using the features offered by the P4 ecosystem. Using an empirical evaluation, we show that the theoretical gains promised by linear network coding can be realized with a per-packet processing cost.
2020-03-30
Mao, Huajian, Chi, Chenyang, Yu, Jinghui, Yang, Peixiang, Qian, Cheng, Zhao, Dongsheng.  2019.  QRStream: A Secure and Convenient Method for Text Healthcare Data Transferring. 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). :3458–3462.
With the increasing of health awareness, the users become more and more interested in their daily health information and healthcare activities results from healthcare organizations. They always try to collect them together for better usage. Traditionally, the healthcare data is always delivered by paper format from the healthcare organizations, and it is not easy and convenient for data usage and management. They would have to translate these data on paper to digital version which would probably introduce mistakes into the data. It would be necessary if there is a secure and convenient method for electronic health data transferring between the users and the healthcare organizations. However, for the security and privacy problems, almost no healthcare organization provides a stable and full service for health data delivery. In this paper, we propose a secure and convenient method, QRStream, which splits original health data and loads them onto QR code frame streaming for the data transferring. The results shows that QRStream can transfer text health data smoothly with an acceptable performance, for example, transferring 10K data in 10 seconds.
2020-03-23
Xuewei, Feng, Dongxia, Wang, Zhechao, Lin.  2019.  An Approach of Code Pointer Hiding Based on a Resilient Area. 2019 Seventh International Conference on Advanced Cloud and Big Data (CBD). :204–209.
Code reuse attacks can bypass the DEP mechanism effectively. Meanwhile, because of the stealthy of the operation, it becomes one of the most intractable threats while securing the information system. Although the security solutions of code randomization and diversity can mitigate the threat at a certain extent, attackers can bypass these solutions due to the high cost and coarsely granularity, and the memory disclosure vulnerability is another magic weapon which can be used by attackers to bypass these solutions. After analyzing the principle of memory disclosure vulnerability, we propose a novel code pointer hiding method based on a resilient area. We expatiate how to create the resilient area and achieve code pointer hiding from four aspects, namely hiding return addresses in data pages, hiding function pointers in data pages, hiding target pointers of instruction JUMP in code pages, and hiding target pointers of instruction CALL in code pages. This method can stop attackers from reading and analyzing pages in memory, which is a critical stage in finding and creating ROP chains while executing a code reuse attack. Lastly, we test the method contrastively, and the results show that the method is feasible and effective while defending against ROP attacks.
Pewny, Jannik, Koppe, Philipp, Holz, Thorsten.  2019.  STEROIDS for DOPed Applications: A Compiler for Automated Data-Oriented Programming. 2019 IEEE European Symposium on Security and Privacy (EuroS P). :111–126.
The wide-spread adoption of system defenses such as the randomization of code, stack, and heap raises the bar for code-reuse attacks. Thus, attackers utilize a scripting engine in target programs like a web browser to prepare the code-reuse chain, e.g., relocate gadget addresses or perform a just-in-time gadget search. However, many types of programs do not provide such an execution context that an attacker can use. Recent advances in data-oriented programming (DOP) explored an orthogonal way to abuse memory corruption vulnerabilities and demonstrated that an attacker can achieve Turing-complete computations without modifying code pointers in applications. As of now, constructing DOP exploits requires a lot of manual work-for every combination of application and payload anew. In this paper, we present novel techniques to automate the process of generating DOP exploits. We implemented a compiler called STEROIDS that leverages these techniques and compiles our high-level language SLANG into low-level DOP data structures driving malicious computations at run time. This enables an attacker to specify her intent in an application-and vulnerability-independent manner to maximize reusability. We demonstrate the effectiveness of our techniques and prototype implementation by specifying four programs of varying complexity in SLANG that calculate the Levenshtein distance, traverse a pointer chain to steal a private key, relocate a ROP chain, and perform a JIT-ROP attack. STEROIDS compiles each of those programs to low-level DOP data structures targeted at five different applications including GStreamer, Wireshark and ProFTPd, which have vastly different vulnerabilities and DOP instances. Ultimately, this shows that our compiler is versatile, can be used for both 32-bit and 64-bit applications, works across bug classes, and enables highly expressive attacks without conventional code-injection or code-reuse techniques in applications lacking a scripting engine.
2020-02-26
Kuo, Man-Hsuan, Hu, Chun-Ming, Lee, Kuen-Jong.  2019.  Time-Related Hardware Trojan Attacks on Processor Cores. 2019 IEEE International Test Conference in Asia (ITC-Asia). :43–48.

Real-time clock circuits are widely used in modern electronic systems to provide time information to the systems at the beginning of the system power-on. In this paper, we present two types of Hardware Trojan designs that employ the time information as the trigger conditions. One is a real-time based Trojan, which will attack a system at some specific realworld time. The other is a relative-time based Trojan, which will be triggered when a specific time period passes after the system is powered on. In either case when a Trojan is triggered its payload may corrupt the system or leakage internal information to the outside world. Experimental results show that the extra power consumption, area overhead and delay time are all quite small and thus the detection of the Trojans is difficult by using traditional side-channel detection methods.

2020-02-24
Maunero, Nicoló, Prinetto, Paolo, Roascio, Gianluca.  2019.  CFI: Control Flow Integrity or Control Flow Interruption? 2019 IEEE East-West Design Test Symposium (EWDTS). :1–6.

Runtime memory vulnerabilities, especially present in widely used languages as C and C++, are exploited by attackers to corrupt code pointers and hijack the execution flow of a program running on a target system to force it to behave abnormally. This is the principle of modern Code Reuse Attacks (CRAs) and of famous attack paradigms as Return-Oriented Programming (ROP) and Jump-Oriented Programming (JOP), which have defeated the previous defenses against malicious code injection such as Data Execution Prevention (DEP). Control-Flow Integrity (CFI) is a promising approach to protect against such runtime attacks. Recently, many CFI solutions have been proposed, with both hardware and software implementations. But how can a defense based on complying with a graph calculated a priori efficiently deal with something unpredictable as exceptions and interrupt requests? The present paper focuses on this dichotomy by analysing some of the CFI-based defenses and showing how the unexpected trigger of an interrupt and the sudden execution of an Interrupt Service Routine (ISR) can circumvent them.

2020-02-18
Chaturvedi, Shilpa, Simmhan, Yogesh.  2019.  Toward Resilient Stream Processing on Clouds Using Moving Target Defense. 2019 IEEE 22nd International Symposium on Real-Time Distributed Computing (ISORC). :134–142.
Big data platforms have grown popular for real-time stream processing on distributed clusters and clouds. However, execution of sensitive streaming applications on shared computing resources increases their vulnerabilities, and may lead to data leaks and injection of spurious logic that can compromise these applications. Here, we adopt Moving Target Defense (MTD) techniques into Fast Data platforms, and propose MTD strategies by which we can mitigate these attacks. Our strategies target the platform, application and data layers, which make these reusable, rather than the OS, virtual machine, or hardware layers, which are environment specific. We use Apache Storm as the canonical distributed stream processing platform for designing our MTD strategies, and offer a preliminary evaluation that indicates the feasibility and evaluates the performance overheads.
2020-02-10
Sharifzadeh, Mehdi, Aloraini, Mohammed, Schonfeld, Dan.  2019.  Quantized Gaussian Embedding Steganography. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). :2637–2641.

In this paper, we develop a statistical framework for image steganography in which the cover and stego messages are modeled as multivariate Gaussian random variables. By minimizing the detection error of an optimal detector within the generalized adopted statistical model, we propose a novel Gaussian embedding method. Furthermore, we extend the formulation to cost-based steganography, resulting in a universal embedding scheme that works with embedding costs as well as variance estimators. Experimental results show that the proposed approach avoids embedding in smooth regions and significantly improves the security of the state-of-the-art methods, such as HILL, MiPOD, and S-UNIWARD.

Rashid, Rasber Dh., Majeed, Taban F..  2019.  Edge Based Image Steganography: Problems and Solution. 2019 International Conference on Communications, Signal Processing, and Their Applications (ICCSPA). :1–5.

Steganography means hiding secrete message in cover object in a way that no suspicious from the attackers, the most popular steganography schemes is image steganography. A very common questions that asked in the field are: 1- what is the embedding scheme used?, 2- where is (location) the secrete messages are embedded?, and 3- how the sender will tell the receiver about the locations of the secrete message?. Here in this paper we are deal with and aimed to answer questions number 2 and 3. We used the popular scheme in image steganography which is least significant bits for embedding in edges positions in color images. After we separate the color images into its components Red, Green, and Blue, then we used one of the components as an index to find the edges, while other one or two components used for embedding purpose. Using this technique we will guarantee the same number and positions of edges before and after embedding scheme, therefore we are guaranteed extracting the secrete message as it's without any loss of secrete messages bits.

2020-01-21
He, Lin, Ren, Gang, Liu, Ying.  2019.  Bootstrapping Accountability and Privacy to IPv6 Internet without Starting from Scratch. IEEE INFOCOM 2019 - IEEE Conference on Computer Communications. :1486–1494.

Accountability and privacy are considered valuable but conflicting properties in the Internet, which at present does not provide native support for either. Past efforts to balance accountability and privacy in the Internet have unsatisfactory deployability due to the introduction of new communication identifiers, and because of large-scale modifications to fully deployed infrastructures and protocols. The IPv6 is being deployed around the world and this trend will accelerate. In this paper, we propose a private and accountable proposal based on IPv6 called PAVI that seeks to bootstrap accountability and privacy to the IPv6 Internet without introducing new communication identifiers and large-scale modifications to the deployed base. A dedicated quantitative analysis shows that the proposed PAVI achieves satisfactory levels of accountability and privacy. The results of evaluation of a PAVI prototype show that it incurs little performance overhead, and is widely deployable.

2019-12-02
Burow, Nathan, Zhang, Xinping, Payer, Mathias.  2019.  SoK: Shining Light on Shadow Stacks. 2019 IEEE Symposium on Security and Privacy (SP). :985–999.

Control-Flow Hijacking attacks are the dominant attack vector against C/C++ programs. Control-Flow Integrity (CFI) solutions mitigate these attacks on the forward edge, i.e., indirect calls through function pointers and virtual calls. Protecting the backward edge is left to stack canaries, which are easily bypassed through information leaks. Shadow Stacks are a fully precise mechanism for protecting backwards edges, and should be deployed with CFI mitigations. We present a comprehensive analysis of all possible shadow stack mechanisms along three axes: performance, compatibility, and security. For performance comparisons we use SPEC CPU2006, while security and compatibility are qualitatively analyzed. Based on our study, we renew calls for a shadow stack design that leverages a dedicated register, resulting in low performance overhead, and minimal memory overhead, but sacrifices compatibility. We present case studies of our implementation of such a design, Shadesmar, on Phoronix and Apache to demonstrate the feasibility of dedicating a general purpose register to a security monitor on modern architectures, and Shadesmar's deployability. Our comprehensive analysis, including detailed case studies for our novel design, allows compiler designers and practitioners to select the correct shadow stack design for different usage scenarios. Shadow stacks belong to the class of defense mechanisms that require metadata about the program's state to enforce their defense policies. Protecting this metadata for deployed mitigations requires in-process isolation of a segment of the virtual address space. Prior work on defenses in this class has relied on information hiding to protect metadata. We show that stronger guarantees are possible by repurposing two new Intel x86 extensions for memory protection (MPX), and page table control (MPK). Building on our isolation efforts with MPX and MPK, we present the design requirements for a dedicated hardware mechanism to support intra-process memory isolation, and discuss how such a mechanism can empower the next wave of highly precise software security mitigations that rely on partially isolated information in a process.

2019-07-01
Perez, R. Lopez, Adamsky, F., Soua, R., Engel, T..  2018.  Machine Learning for Reliable Network Attack Detection in SCADA Systems. 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). :633–638.

Critical Infrastructures (CIs) use Supervisory Control And Data Acquisition (SCADA) systems for remote control and monitoring. Sophisticated security measures are needed to address malicious intrusions, which are steadily increasing in number and variety due to the massive spread of connectivity and standardisation of open SCADA protocols. Traditional Intrusion Detection Systems (IDSs) cannot detect attacks that are not already present in their databases. Therefore, in this paper, we assess Machine Learning (ML) for intrusion detection in SCADA systems using a real data set collected from a gas pipeline system and provided by the Mississippi State University (MSU). The contribution of this paper is two-fold: 1) The evaluation of four techniques for missing data estimation and two techniques for data normalization, 2) The performances of Support Vector Machine (SVM), and Random Forest (RF) are assessed in terms of accuracy, precision, recall and F1score for intrusion detection. Two cases are differentiated: binary and categorical classifications. Our experiments reveal that RF detect intrusions effectively, with an F1score of respectively \textbackslashtextgreater 99%.

2019-06-10
Jiang, J., Yin, Q., Shi, Z., Li, M..  2018.  Comprehensive Behavior Profiling Model for Malware Classification. 2018 IEEE Symposium on Computers and Communications (ISCC). :00129-00135.

In view of the great threat posed by malware and the rapid growing trend about malware variants, it is necessary to determine the category of new samples accurately for further analysis and taking appropriate countermeasures. The network behavior based classification methods have become more popular now. However, the behavior profiling models they used usually only depict partial network behavior of samples or require specific traffic selection in advance, which may lead to adverse effects on categorizing advanced malware with complex activities. In this paper, to overcome the shortages of traditional models, we raise a comprehensive behavior model for profiling the behavior of malware network activities. And we also propose a corresponding malware classification method which can extract and compare the major behavior of samples. The experimental and comparison results not only demonstrate our method can categorize samples accurately in both criteria, but also prove the advantage of our profiling model to two other approaches in accuracy performance, especially under scenario based criteria.

2019-04-05
Bapat, R., Mandya, A., Liu, X., Abraham, B., Brown, D. E., Kang, H., Veeraraghavan, M..  2018.  Identifying Malicious Botnet Traffic Using Logistic Regression. 2018 Systems and Information Engineering Design Symposium (SIEDS). :266-271.

An important source of cyber-attacks is malware, which proliferates in different forms such as botnets. The botnet malware typically looks for vulnerable devices across the Internet, rather than targeting specific individuals, companies or industries. It attempts to infect as many connected devices as possible, using their resources for automated tasks that may cause significant economic and social harm while being hidden to the user and device. Thus, it becomes very difficult to detect such activity. A considerable amount of research has been conducted to detect and prevent botnet infestation. In this paper, we attempt to create a foundation for an anomaly-based intrusion detection system using a statistical learning method to improve network security and reduce human involvement in botnet detection. We focus on identifying the best features to detect botnet activity within network traffic using a lightweight logistic regression model. The network traffic is processed by Bro, a popular network monitoring framework which provides aggregate statistics about the packets exchanged between a source and destination over a certain time interval. These statistics serve as features to a logistic regression model responsible for classifying malicious and benign traffic. Our model is easy to implement and simple to interpret. We characterized and modeled 8 different botnet families separately and as a mixed dataset. Finally, we measured the performance of our model on multiple parameters using F1 score, accuracy and Area Under Curve (AUC).

Chen, S., Chen, Y., Tzeng, W..  2018.  Effective Botnet Detection Through Neural Networks on Convolutional Features. 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). :372-378.

Botnet is one of the major threats on the Internet for committing cybercrimes, such as DDoS attacks, stealing sensitive information, spreading spams, etc. It is a challenging issue to detect modern botnets that are continuously improving for evading detection. In this paper, we propose a machine learning based botnet detection system that is shown to be effective in identifying P2P botnets. Our approach extracts convolutional version of effective flow-based features, and trains a classification model by using a feed-forward artificial neural network. The experimental results show that the accuracy of detection using the convolutional features is better than the ones using the traditional features. It can achieve 94.7% of detection accuracy and 2.2% of false positive rate on the known P2P botnet datasets. Furthermore, our system provides an additional confidence testing for enhancing performance of botnet detection. It further classifies the network traffic of insufficient confidence in the neural network. The experiment shows that this stage can increase the detection accuracy up to 98.6% and decrease the false positive rate up to 0.5%.

2019-03-15
Crouch, A., Hunter, E., Levin, P. L..  2018.  Enabling Hardware Trojan Detection and Prevention through Emulation. 2018 IEEE International Symposium on Technologies for Homeland Security (HST). :1-5.

Hardware Trojans, implantable at a myriad of points within the supply chain, are difficult to detect and identify. By emulating systems on programmable hardware, the authors have created a tool from which to create and evaluate Trojan attack signatures and therefore enable better Trojan detection (for in-service systems) and prevention (for in-design systems).

2019-02-22
Liao, X., Yu, Y., Li, B., Li, Z., Qin, Z..  2019.  A New Payload Partition Strategy in Color Image Steganography. IEEE Transactions on Circuits and Systems for Video Technology. :1-1.

In traditional steganographic schemes, RGB three channels payloads are assigned equally in a true color image. In fact, the security of color image steganography relates not only to data-embedding algorithms but also to different payload partition. How to exploit inter-channel correlations to allocate payload for performance enhancement is still an open issue in color image steganography. In this paper, a novel channel-dependent payload partition strategy based on amplifying channel modification probabilities is proposed, so as to adaptively assign the embedding capacity among RGB channels. The modification probabilities of three corresponding pixels in RGB channels are simultaneously increased, and thus the embedding impacts could be clustered, in order to improve the empirical steganographic security against the channel co-occurrences detection. Experimental results show that the new color image steganographic schemes incorporated with the proposed strategy can effectively make the embedding changes concentrated mainly in textured regions, and achieve better performance on resisting the modern color image steganalysis.

2019-02-08
Park, W., Hwang, D., Kim, K..  2018.  A TOTP-Based Two Factor Authentication Scheme for Hyperledger Fabric Blockchain. 2018 Tenth International Conference on Ubiquitous and Future Networks (ICUFN). :817-819.

In this paper, we propose a new authentication method to prevent authentication vulnerability of Claim Token method of Membership Service provide in Private BlockChain. We chose Hyperledger Fabric v1.0 using JWT authentication method of membership service. TOTP, which generate OTP tokens and user authentication codes that generate additional time-based password on existing authentication servers, has been applied to enforce security and two-factor authentication method to provide more secure services.

2019-01-21
Nicho, M., Oluwasegun, A., Kamoun, F..  2018.  Identifying Vulnerabilities in APT Attacks: A Simulated Approach. 2018 9th IFIP International Conference on New Technologies, Mobility and Security (NTMS). :1–4.

This research aims to identify some vulnerabilities of advanced persistent threat (APT) attacks using multiple simulated attacks in a virtualized environment. Our experimental study shows that while updating the antivirus software and the operating system with the latest patches may help in mitigating APTs, APT threat vectors could still infiltrate the strongest defenses. Accordingly, we highlight some critical areas of security concern that need to be addressed.

2018-09-12
Domínguez, A., Carballo, P. P., Núñez, A..  2017.  Programmable SoC platform for deep packet inspection using enhanced Boyer-Moore algorithm. 2017 12th International Symposium on Reconfigurable Communication-centric Systems-on-Chip (ReCoSoC). :1–8.

This paper describes the work done to design a SoC platform for real-time on-line pattern search in TCP packets for Deep Packet Inspection (DPI) applications. The platform is based on a Xilinx Zynq programmable SoC and includes an accelerator that implements a pattern search engine that extends the original Boyer-Moore algorithm with timing and logical rules, that produces a very complex set of rules. Also, the platform implements different modes of operation, including SIMD and MISD parallelism, which can be configured on-line. The platform is scalable depending of the analysis requirement up to 8 Gbps. High-Level synthesis and platform based design methodologies have been used to reduce the time to market of the completed system.

2018-07-06
Liu, T., Wen, W., Jin, Y..  2018.  SIN2: Stealth infection on neural network \#x2014; A low-cost agile neural Trojan attack methodology. 2018 IEEE International Symposium on Hardware Oriented Security and Trust (HOST). :227–230.

Deep Neural Network (DNN) has recently become the “de facto” technique to drive the artificial intelligence (AI) industry. However, there also emerges many security issues as the DNN based intelligent systems are being increasingly prevalent. Existing DNN security studies, such as adversarial attacks and poisoning attacks, are usually narrowly conducted at the software algorithm level, with the misclassification as their primary goal. The more realistic system-level attacks introduced by the emerging intelligent service supply chain, e.g. the third-party cloud based machine learning as a service (MLaaS) along with the portable DNN computing engine, have never been discussed. In this work, we propose a low-cost modular methodology-Stealth Infection on Neural Network, namely “SIN2”, to demonstrate the novel and practical intelligent supply chain triggered neural Trojan attacks. Our “SIN2” well leverages the attacking opportunities built upon the static neural network model and the underlying dynamic runtime system of neural computing framework through a bunch of neural Trojaning techniques. We implement a variety of neural Trojan attacks in Linux sandbox by following proposed “SIN2”. Experimental results show that our modular design can rapidly produce and trigger various Trojan attacks that can easily evade the existing defenses.

2018-06-07
Bresch, C., Michelet, A., Amato, L., Meyer, T., Hély, D..  2017.  A red team blue team approach towards a secure processor design with hardware shadow stack. 2017 IEEE 2nd International Verification and Security Workshop (IVSW). :57–62.

Software attacks are commonly performed against embedded systems in order to access private data or to run restricted services. In this work, we demonstrate some vulnerabilities of commonly use processor which can be leveraged by hackers to attack a system. The targeted devices are based on open processor architectures OpenRISC and RISC-V. Several software exploits are discussed and demonstrated while a hardware countermeasure is proposed and validated on OpenRISC against Return Oriented Programming attack.

2018-05-09
Andy, S., Rahardjo, B., Hanindhito, B..  2017.  Attack scenarios and security analysis of MQTT communication protocol in IoT system. 2017 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI). :1–6.
Various communication protocols are currently used in the Internet of Things (IoT) devices. One of the protocols that are already standardized by ISO is MQTT protocol (ISO / IEC 20922: 2016). Many IoT developers use this protocol because of its minimal bandwidth requirement and low memory consumption. Sometimes, IoT device sends confidential data that should only be accessed by authorized people or devices. Unfortunately, the MQTT protocol only provides authentication for the security mechanism which, by default, does not encrypt the data in transit thus data privacy, authentication, and data integrity become problems in MQTT implementation. This paper discusses several reasons on why there are many IoT system that does not implement adequate security mechanism. Next, it also demonstrates and analyzes how we can attack this protocol easily using several attack scenarios. Finally, after the vulnerabilities of this protocol have been examined, we can improve our security awareness especially in MQTT protocol and then implement security mechanism in our MQTT system to prevent such attack.
2018-05-01
Cogranne, R., Sedighi, V., Fridrich, J..  2017.  Practical Strategies for Content-Adaptive Batch Steganography and Pooled Steganalysis. 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). :2122–2126.

This paper investigates practical strategies for distributing payload across images with content-adaptive steganography and for pooling outputs of a single-image detector for steganalysis. Adopting a statistical model for the detector's output, the steganographer minimizes the power of the most powerful detector of an omniscient Warden, while the Warden, informed by the payload spreading strategy, detects with the likelihood ratio test in the form of a matched filter. Experimental results with state-of-the-art content-adaptive additive embedding schemes and rich models are included to show the relevance of the results.