Message Segmentation Research Articles

Possibility of Recovering Message Segments Based on Side Information about Original Characters

Possibility of Recovering Message Segments Based on Side Information about Original Characters

MSLFuzzer: black-box fuzzing of SOHO router devices via message segment list inference

The popularity of small office and home office routers has brought convenience, but it also caused many security issues due to vulnerabilities. Black-box fuzzing through network protocols to discover vulnerabilities becomes a viable option. The main drawbacks of state-of-the-art black-box fuzzers can be summarized as follows. First, the feedback process neglects to discover the missing fields in the raw message. Secondly, the guidance of the raw message content in the mutation process is aimless. Finally, the randomized validity of the test case structure can cause most fuzzing tests to end up with an invalid response of the tested device. To address these challenges, we propose a novel black-box fuzzing framework called MSLFuzzer. MSLFuzzer infers the raw message structure according to the response from a tested device and generates a message segment list. Furthermore, MSLFuzzer performs semantic, sequence, and stability analyses on each message segment to enhance the complementation of missing fields in the raw message and guide the mutation process. We construct a dataset of 35 real-world vulnerabilities and evaluate MSLFuzzer. The evaluation results show that MSLFuzzer can find more vulnerabilities and elicit more types of responses from fuzzing targets. Additionally, MSLFuzzer successfully discovered 10 previously unknown vulnerabilities.

An Adaptive IPM-Based HEVC Video Steganography via Minimizing Non-Additive Distortion

Recently, almost all the proposed adaptive video steganographic schemes are based on minimizing an additive embedding distortion. However, they ignore the hard fact that the additive embedding distortion is not quite suitable for video steganography because of the interplay of cover elements in video steganography. In this paper, an adaptive intra prediction mode based (IPM-based) video steganography is proposed by minimizing the non-additive distortion in HEVC. To reduce the complexity of minimizing the non-additive distortion, a multi-layered embedding structure combined with a proposed embed-ding distortion updating strategy is adopted to approximate the non-additive distortion in an additive form. Firstly, all IPMs are decomposed into multiple layers based on the distortion drift graph to offer multi-layered embedding. Each IPM in the same layer is considered to be independent, and syndrome-trellis code (STC) can be applied to embed the message segment into each layer with an additive distortion function sequentially. Then, a distortion function composed of self-distortion and drift-distortion is proposed to initialize the distortion of modifying each IPM. Finally, after embedding the first message segment into the IPMs in the first layer with the initialized distortions, an embedding distortion updating strategy is applied to update the distortions of the IPMs in the remaining layers dynamically. Ex-perimental results demonstrate that the proposed adaptive IPM-based video steganography can achieve much better perceptual quality and security performance than the state-of-the-art.

A Segmented-Edit Error-Correcting Code With Re-Synchronization Function for DNA-Based Storage Systems

As a powerful tool for storing digital information in chemically synthesized molecules, DNA-based data storage has undergone continuous development and received increasingly more attention. Efficiently recovering information from large-scale DNA strands that suffer from insertions, deletions, and substitution errors (collectively referred to as edit errors), is one of the major bottlenecks in DNA-based storage systems. To cope with this challenge, in this paper, we provide a segmented-edit error-correcting code with the re-synchronization function, termed the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DNA-LM</i> code. Compared with the previous segmented-error-correcting codes, it has a systematic structure and does not require the endpoint of the received segment as pre-requisite information for decoding. In the case that the number of edit errors exceeds the edit error-correcting capability of a segment, it can easily regain synchronization to ensure that the subsequent decoding continues. Both encoding and decoding complexity is linear in the codeword length. The redundancy of each segment is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\lceil \log k\rceil +6$</tex-math></inline-formula> quaternary symbols, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> is the length of the message segment. We further generalize the decoding algorithm to deal with duplicated DNA strands, whereas it still maintains linear time complexity in the codeword length and the number of duplications. Simulations under a stochastic edit errors model show that, at a low raw error rate of the “next-gen” sequencing, our code can enable error-free decoding by concatenating with the (255,223) RS code.

EBDL: Effective blockchain-based covert storage channel with dynamic labels

Blockchain-based covert communication has opened up a new research direction for the covert communication field. In blockchain-based storage covert communication, reliability can be guaranteed because of the inherent immutable property of blockchain. Besides, enhanced undetectability is also achieved because the sender and the receiver do not need to establish a direct connection or be online simultaneously. However, in existing blockchain-based solutions, pre-negotiated and fixed addresses are used as static labels to scan the specific transaction that embeds covert messages, which increases the risk of channel exposure. Moreover, some of them do not support large covert message transmission. In this paper, we present an effective blockchain-based covert storage channel with dynamic labels. More specifically, a novel dynamic label mechanism is proposed to help the receiver extract the covert message efficiently, whereas adversaries cannot distinguish whether a block contains specific transactions. Besides, a message segmentation mechanism is also proposed to achieve large covert message transmission. Theoretical security analysis and detailed experimental evaluations based on the public Ethereum blockchain network show that the proposed scheme is secure under Chosen Hidden-text Attacks with acceptable efficiency.

Genre Analysis of The Message Segments of An Indonesian-English Tourism Text Translation: Case in Madura

Abstract. Genre as a spectrum of textual manifestation signals its textual presence through linguistic devices. In the tourism text, the signal of its presence is marked by a transition from one movement or organizing activity to another. Translating genre is central to the work of translators. The fact is the translation process often does not take one line and translators often overlook the role of contextual specifications. This difficulty is also compounded by the fact that the readers’ understanding when reading the translation text often has difficulty in obtaining the meaning. Therefore, the purpose of this study is to identify and explain the translation of an Indonesian-English tourism text genre focusing on message segments to determine the moves and rhetorical functions of the discourse structure and how they may contribute to the readability of the text. The study was descriptive qualitative research. The data was extracted from a variety of documents related to Madura tourism. The data collection technique was document review and the data analysis was interactive model analysis. The results of the study showed that the message segments of the Indonesian-English tourism text translation were constructed with three moves (explanation, illustration, and argumentation) and three rhetorical functions (description, definition, and cause and effect). These moves and rhetorical functions in the translation of the Indonesian-English tourism text genre contributed to the results of the readability of the translated texts, i.e., they were fully comprehensible without unnecessary effort in the part of the readers. The findings suggest that genre analysis of the message segments of the tourism text translation can be a good alternative for communicating the intentions of the source texts. Thus, this can be a good practice for translators to consider when translating texts and other related texts as well.Keywords: Genre, Message Segment, Translation, Tourism

Sequential Message Characterization for Early Classification of Encrypted Internet Traffic

Classifying Internet traffic is critical to many network management tasks, including malicious attack detection, usage monitoring, load balancing, etc. As current traffic packets are often transmitted with encryption, at randomized port numbers, and under highly dynamic network conditions, traditional approaches such as port mapping, deep packet inspection, and statistical analysis are no longer effective. In this paper, we first collect extensive traffic flows at the exit router of a university and label them into various source applications. After extracting the message (consisting of multiple consecutive TCP packets) sequence for all collected traffic flows, we find that each application type has distinct sequential message features. By leveraging the message sequential feature, we develop a system, named SMC (Sequential Message Characterization), which can perform online traffic classification with the sequential size information of a few message segments. In SMC, after confirming the long-term dependency among message segments, we create a Long Short-Term Memory (LSTM) neural network to conduct deep learning on message size sequence, and then build a multi-classifier to classify traffic types based on the probability profiles output by deep LSTM models. Extensive experiments are conducted and results demonstrate that the proposed SMC can achieve 97% of classification accuracy on average. Meanwhile, with as few as 6 pieces of message size information as input, SMC enables early online traffic classification especially for heavy-traffic flows with over 35 message segments in median.

Investigation of real-time optimization based on CAN bus

Aiming at the problem of CAN bus network transmission delay which affects the transmission rate, a bus network transmission delay model is established. The main indicators of network transmission delay are modeled. A hybrid optimization combining message offset compensation and identifier optimization is proposed. In terms of overall optimization, an optimization method for message segmentation and encoding scheme is designed, and a method for offset compensation of delayed messages is designed for partial parts. Symtavision software is used to simulate network conditions of the two CAN buses. Analyse the main indicators of delay after the optimization plan is adopted. The simulation results show that the hybrid optimization scheme can reduce the delay by more than 16% compared with before, which significantly improves the network communication quality, and improves the network transmission rate and scalability.

Investigating Connections between Need for Cognitive Closure and Climate Change Concern in College Students.

Understanding how people’s worldviews and individual personality differences affect their thinking about anthropogenic climate change is critical to communication efforts regarding this issue. This study surveyed University of Georgia students to investigate the role that need for cognitive closure plays in level of climate change worry. The relationship between these two was found to involve suppression—a subset of mediation—by the social dimension of political conservatism. Political conservatism was also found to play a mediating role in the relationship between need for cognitive closure and support for governmental and personal climate solutions. However, social conservatism played this mediator role in women, and functioned as a suppressor for men. These findings help inform audience segmentation and creation of climate-related messages based on audience worldview and personality.

Selecting Optimal Submatrix for Syndrome-Trellis Codes (STCs)-Based Steganography With Segmentation

Syndrome-Trellis Codes (STC), as the de facto state-of-the-art steganography framework, has drawn huge attention recently. However, the original STC merely developed with the general form of cover and message, and almost all the subsequent improvements for STC focused on the cover selection and design of the distortion cost function. How to select the optimal submatrix by the features for the given cover and message is an important issue and requires to be solved urgently. In this work, we propose to divide both the cover and message into segments and embed each message segment into its optimally matched cover segment. We name the proposed method as Segment-STC steganography. Specifically, by investigating the features of the submatrix used in STC, we first select the optimal submatrix, which could effectively reduce the number of distorted elements during the embedding. Then, the given cover and message will be split into a series of segments. Each message segment is adaptively matched with an optimal submatrix. We conduct the experiments on both the benchmark BOSS dataset and our collected on-line songs dataset. The experimental results show that, compared with the original STC, our method could effectively reduce the distortion and improve the undetectability of the stego.

Data embedding in high efficiency video coding (HEVC) videos by modifying the partitioning of coding units

A data embedding solution in HEVC videos is proposed by modifying the partitioning of coding units (CUs). The partitions of a CU are first represented as a sequence of binary flags. The flags pertaining to 16 × 16 sub CUs are used as a cover for data embedding, where 6 or 4 message bits are embedded per CU. The data embedding algorithm guarantees that a maximum of one partition is modified per message segment, therefore, in a given CU, either 0, 1 or 2 partitions are modified. The proposed solution is assessed in terms of message payload, number of modified partitions, loss in video quality as indicated by the PSNR results, mean objective scores and excessive bitrate. The proposed solution can embed messages with up to an average payload of 32.6 kbit/s with a corresponding average distortion of <0.5 dB. Comparisons with existing solutions reveal that the proposed solution maintains similar message payloads with less modifications of CU partitioning and at the same time resulting in less distortions for the cover video.

Unsupervised field segmentation of unknown protocol messages

In network security systems working on intrusion detection, deep packet inspection, and protocol fuzzing, protocol specifications analyzed by Protocol Reverse Engineering(PRE) play an important role as fundamental input. For binary protocols having fixed-length fields, the location of those field boundaries has great impact on the subsequent analysis as well as the final performance. In this paper, we discuss the field segmentation problem formally, and develop a reasonable method ProSeg by introducing and optimize statistics(self-information and mutual information) from Information Theory. By analyzing the format structure of messages from unknown protocol vertically, the boundaries of fixed-length fields could be located by an expert voting strategy successfully. In experiments and analysis on several common protocols, our method turns out to be effective relatively and the results of ProSeg are consistent with standard segmentations to a great extent.

PVO-Based Multiple Message Segment Reversible Data Hiding

In this article, a reversible data hiding technique is proposed to embed multiple segments of a single message into a single cover image. This multiple message segment technique uses a pixel value ordering approach to embed the secret message. The splitting and randomization of the original secret message provides security from an attacker There are many digital formats for data hiding, like images, audio, and video, of which the digital image is the simplest format. Data hiding in image processing refers to inserting the secret message into digital images. Reversible data hiding (RDH) is a lossless technique, in which both the embedded secret message and the cover image is extracted by the receiver. The applications of RDH include medical and military imaging.

Generalized Compute-Compress-and-Forward

Compute-and-forward (CF) harnesses interference in wireless communications by exploiting structured coding. The key idea of CF is to compute integer combinations of code words from multiple source nodes, rather than to decode individual code words by treating others as noise. Compute-compress-and-forward (CCF) can further enhance the network performance by introducing compression operations at receivers. In this paper, we develop a more general compression framework, termed generalized CCF (GCCF), where the compression function involves the selection of message segments over finite fields. We show that GCCF achieves a broader compression rate region than CCF. We also compare our compression rate region with the fundamental Slepian–Wolf (SW) region. We show that GCCF is optimal in the sense of achieving the minimum total compression rate. We also establish the criteria under which GCCF achieves the SW region. In addition, we consider a two-hop relay network employing the GCCF scheme. We formulate a sum-rate maximization problem and develop an approximate algorithm to solve the problem. Numerical results are presented to demonstrate the performance superiority of GCCF over CCF and other schemes.

Critical success factors of public health sponsorship in Australia.

Public health sponsorship is a unique phenomenon in Australia. The current research examines the critical success factors of Western Australian Health Promotion Foundation's (Healthway) sponsorship program, Australia's largest public health sponsorship program. Using stakeholder interviews and expert observational studies, two studies present five key success factors: (i) effective segmentation and targeting of health messages; (ii) collaboration between Healthway and partnering organization to leverage sponsored events; (iii) displacement of unhealth sponsorship; (iv) use of leveraging strategies to raise awareness of health messages; and (v) environmental changes that facilitate behavioural change. The current research provides insights into how and why sponsorship is an effective public health promotion tool.

Response Selection and Automatic Message-Response Expansion in Retrieval-Based QA Systems using Semantic Dependency Pair Model

This article presents an approach to response selection and message-response (MR) database expansion from the unstructured data on the psychological consultation websites for a retrieval-based question answering (QA) system in a constrained domain for emotional support and comforting. First, we manually construct an initial MR database based on the articles collected from the psychological consultation websites. The Chinese Knowledge and Information Processing probabilistic context-free grammar is adopted to obtain the semantic dependency graphs (SDGs) of all the messages and responses in the initial MR database. For each sentence in the MR database, all the semantic dependencies, each composed of two words and their semantic relation, are extracted from the SDG of the sentence to form a semantic dependency set. Finally, a matrix with the element representing the correlation between the semantic dependencies of the messages and their corresponding responses is constructed as a semantic dependency pair model (SDPM) for response selection. Moreover, as the number of MR pairs in the psychological consultation websites is increasing day by day, the MR database in the QA system should be expanded to meet the needs of the users. For MR database expansion, the unstructured data from the message board are automatically collected. For the collected data, the supervised latent Dirichlet allocation is adopted for event detection and then the event-based delta Bayesian Information Criterion is used for message and response article segmentation. Each extracted message segment is then fed to the constructed retrieval-based QA system to find the best matched response segment and the matching score is also estimated to verify if the new MR pair is suitable to be included in the expanded MR database. Fivefold cross validation was employed to evaluate the performance of the proposed retrieval-based QA system over the expanded MR database based on SDPM. Compared to the vector space model-based method, the Okapi BM25 model, and the deep learning-based sequence-to-sequence with attention model, the proposed approach achieved a more favorable performance according to a statistical significance test. The retrieval accuracy based on MR expansion was also evaluated and a satisfactory result was obtained confirming the effectiveness of the expanded MR database. In addition, the user's satisfaction score of the proposed system was evaluated using the Cronbach's alpha value and the satisfaction score of the proposed SDPM was higher than those of the methods for comparison.

IoT Application-Layer Protocol Vulnerability Detection using Reverse Engineering

Fuzzing is regarded as the most promising method for protocol vulnerabilities discovering in network security of Internet of Things (IoT). However, one fatal drawback of existing fuzzing methods is that a huge number of test files are required to maintain a high test coverage. In this paper, a novel method based on protocol reverse engineering is proposed to reduce the amount of test files for fuzzing. The proposed method uses techniques in the field of protocol reverse engineering to identify message formats of IoT application-layer protocol and create test files by generating messages with error fields according to message formats. The protocol message treated as a sequence of bytes is assumed to obey a statistic process with change-points indicating the boundaries of message fields. Then, a multi-change-point detection procedure is introduced to identify change-points of byte sequences according to their statistic properties and divide them into segments according to their change-points. The message segments are further processed via a position-based occurrence probability test analysis to identify keyword fields, data fields and uncertain fields. Finally, a message generation procedure with mutation operation on message fields is applied to construct test files for fuzzing test. The results show that the proposed method can effectively find out the message fields and significantly reduce the amount of test files for fuzzing test.

Adaptive multilevel technique based on histogram shifting for reversible data hiding

This paper presents spatial steganography scheme based on adaptive group modification (AGM) (Rad et al. in Signal Process 125:315–328, 2016). First we study AGM. Then we criticize and improve it. AGM works based on embedding segments of secret message bits on triplet of prediction errors. It also employs multilayer embedding mechanism to increase the capacity. We have improved it by exploiting more number of host bins to increase the hiding capacity. Furthermore, by changing the procedure of secret message segmentation and altering modification groups of prediction errors, the proposed method preserves the same stego image quality. Moreover, the required side information for correct extraction is 32 bit (too much less than the one required for AGM). The experimental results confirm that the proposed method increases the embedding capacity up to 22 K bits while PSNR (peak signal to noise ratio) almost remains the same in comparison with AGM method. Also, the results prove that the proposed method outperforms recent methods.

Advanced Visualization and Analysis of Data Quality for Syndromic Surveillance Systems

ObjectiveTo extend an open source analytics and visualization platform for measuring the quality of electronic health data transmitted to syndromic surveillance systems.IntroductionEffective clinical and public health practice in the twenty-first century requires access to data from an increasing array of information systems. However, the quality of data in these systems can be poor or “unfit for use.” Therefore measuring and monitoring data quality is an essential activity for clinical and public health professionals as well as researchers1. Current methods for examining data quality largely rely on manual queries and processes conducted by epidemiologists. Better, automated tools for examining data quality are desired by the surveillance community.MethodsUsing the existing, open-source platform Atlas developed by the Observational Health Data Sciences and Informatics collaborative (OHDSI; www.ohdsi.org), we added new functionality to measure and visualize the quality of data electronically reported from disparate information systems. Our extensions focused on analysis of data reported electronically to public health agencies for disease surveillance. Specifically, we created methods for examining the completeness and timeliness of data reported as well as the information entropy of the data within syndromic surveillance messages sent from emergency department information systems.ResultsTo date we transformed 111 million syndromic surveillance message segments pertaining to 16.4 million emergency department encounters representing 6 million patients into the OHDSI common data model. We further measured completeness, timeliness and entropy of the syndromic surveillance data. In Figure-1, the OHDSI tool Atlas summarizes the analysis of data completeness for key fields in over one million syndromic surveillance messages sent to Indiana’s health department in 2014. Completeness is reported by age category (e.g., 0-10, 20-30, 60+). Gender is generally complete, but both race and ethnicity fields are often complete for less than half of the patients in the cohort. These results suggest areas for improvement with respect to data quality that could be actionable by the syndromic surveillance coordinator at the state health department.ConclusionsOur project remains a work-in-progress. While functions that assess completeness, timeliness and entropy are complete, there may be other functions important to public health that need to be developed. We are currently soliciting feedback from syndromic surveillance stakeholders to gather ideas for what other functions would be useful to epidemiologists. Suggestions could be developed into functions over the next year. We are further working with the OHDSI collaborative to distribute the Atlas enhancements to other platforms, including the National Syndromic Surveillance Platform (NSSP). Our goal is to enable epidemiologists to quickly analyze data quality at scale.

Improving Syndromic Data Quality through Implementation of Error Capture Module

Objective: To streamline emergency department data processing in Oregon ESSENCE (Oregon’s statewide syndromic surveillance) by systematically and efficiently addressing data quality issues among submitting hospital systems.Introduction: Oregon Public Health Division (OPHD), in collaboration with The Johns Hopkins University Applied Physics Laboratory, implemented Oregon ESSENCE in 2011. ESSENCE is an automated, electronic syndromic surveillance system that captures emergency department data from hospitals across Oregon. While each hospital system sends HL7 2.5.1-formatted messages, each uses a uniquely configured interface to capture, extract, and send data. Consequently, ESSENCE receives messages that vary greatly in content and structure. Emergency department data are ingested using the Rhapsody Integration Engine 6.2.1 (Orion Health, Auckland, NZ), which standardizes messages before entering ESSENCE. Mechanisms in the ingestion route (error-handling filters) identify messages that do not completely match accepted standards for submission. A sub-set of these previously-identified messages with errors are corrected within the route as they emerge.Existence of errors does not preclude a message’s insertion into ESSENCE. However, the quality and quantity of errors determine the quality of the data that ESSENCE uses. Unchecked, error accumulation also can cause strain to the integration engine. Despite ad-hoc processes to address errors, backlogs accrue. With no meta-data to assess the importance and source of backlogged errors, the ESSENCE team had no guide with which to mitigate errors. The ESSENCE team needed a way to determine which errors could be fixed by updating the Rhapsody Integration Engine and which required consultation with partner health systems and their data vendors. To formally address these issues, the ESSENCE team developed an error-capture module within Rhapsody to identify and quantify all errors identified in syndromic messages and to use as a guide to prioritize fixing new errors.Methods: Members of OPHD’s informatics team and the Oregon ESSENCE team met to brainstorm solutions to error accumulation and message-processing inefficiencies. The team agreed that existing infrastructure and resources were sufficient to accomplish this project. Using Rhapsody, the team created filters that generated error messages each time an HL7 message failed to validate pre-determined message parameters (a standard HL7 2.5.1 syndromic message definition). In order to capture information about errors that were currently being fixed by the ingestion route, two filters were inserted into the processing route: one before and one after previously-existing error-fixing message-modifiers. The team created a FileMaker database to collect information about each error identified, including submitter, location of error (segment, field, component), type of error (too long for field, not in value set, doesn’t adhere to correct message structure). The team enabled error-capture for 81 days (June 16 – September 5, 2017) at which point they evaluated error data so as to guide repair of message modifying filters within the integration engine.Results: The module captured 16,273,963 error messages over 81 days. The two error capture filters (before and after existing modifiers) each generated 50% of the error messages. The module identified errors across seven HL7 message segments (DG1, EVN, OBX, PID, PR1, PV1, PV2). One submitter produced 87% of the error messages. Of those, 93% were errors in two fields in the PID segment. Based on the results, ESSENCE team members contacted this submitter and resolved this error, greatly reducing the ingestion and error assessment burden of Rhapsody and the development team. Currently the ESSENCE team is still analyzing the rest of the errors, applying fixes and contacting submitting facilities as needed. Once completed, the ESSENCE team will enable the error capture module bi-annually to continue refinement of its system strategically guide data quality work.Conclusions: Oregon ESSENCE developed a tool to evaluate errors in emergency department HL7 messages it receives for syndromic surveillance. Its quick development and reusability make it a cost-effective and sustainable data quality solution for focusing effort in regional installations of syndromic surveillance.