Causal Adversarial Channels With Feedback Snooping

Since the Internet of Things (IoT) will be entwined with everything we use in our daily life, the consequence of security flaws escalates. Smart objects will govern most of the home appliances and car engines yielding potential disaster scenarios. In this context, successful attacks could lead to chaos and scary scenarios (www.darkreading.com). Unprotected personal information may expose sensitive and embarrassing data to the public and attacks may threaten not only our computers and smart devices, but our intimacy and perhaps our lives too. Because persons and objects will be bonded with each other, user consent becomes critical. Therefore, thing, object, and user Identity will be the focus of future IoT security solutions, yielding a Trust, Security, and Privacy (TSP) paradigm, which may constitute the Achilles' heel of IoT. While security issues are quite straightforward, mainly from background knowledge, privacy issues are far more complex. Privacy constitutes a rather challenging task, even for the m...

This paper considers the problem of bit-wise channel coding over a Binary Symmetric Channel (BSC) with feedback. While it is known that feedback does not increase the capacity of a memoryless channel, it is believed to simplify the coding schemes for some channels. The most significant one is the Binary Erasure Channel (BEC) where capacity is achieved by a simple sequential bit-wise repetition code under which each bit is (re-)transmitted until it is received. This sequential bit-wise feedback code has the added advantage that it can be used for streaming applications over BECs. In contrast, there is no known sequential bit-wise code with feedback that can achieve nonzero transmission rate for a BSC. For example, under Posterior Matching for a binary input channel with feedback, also known as Horstein scheme, each message is considered in its entirety in a block coding manner. This paper proposes a sequential feedback coding scheme with a nested bit-wise structure that generalizes repetition codes. This scheme is shown to achieve strictly positive rate for a large class of binary input channels including a BSC with arbitrary cross-over probability $p \in(0, \frac{1}{2})$ . The analysis relies on characterizing a lower bound on the step-wise Extrinsic Jensen Shannon divergence.

Adversarial robust image processing in medical digital twin

Technology advancements blend to masses with diverse and heterogeneous environments. The Internet of Things (IoT) and Cloud Computing has broken through reachability and availability of services. The security, privacy requirements and challenges manifest with the heterogeneity and the wide range of application domains posing a major concern in adoption of IoT. The literature addresses these changing requirements individually. The paper presents a top-down survey starting with general security challenges in IoT moving to security and privacy challenges, and later exploring security frameworks of IoT. The millions of wide range of devices produce a huge amount of data. Identifying and authenticating these devices and allowing them to process their data locally improves the performance, reduces the bandwidth usage, increases the battery usage and reduces the security risk of attacks making the devices self reliant. The survey identifies the less explored research gaps to group them and address the concerns related to device management, user management, key management, trust management and data management. The surveyed secured frameworks in IoT address the multiple dimensions of security concerns and their aspects.

Cloud computing is the new bending curve in the line of Information Technology and computing paradigm. Cloud computing has simultaneously revolutionized business and government sectors by stretching its arms in every possible field, from not only to Information communication technology to medical advancements to a more diverse agriculture field which was untouched by technology for a decade. Cloud computing enlightens path for another dimension of computing which is expected to rise even more than mobile computing, i.e., Internet of Things (IOT) or internet of everything. As new dimensions are being added up to the cloud domain every day this gives a window to hackers and intruders to breach the doors. The security problem is amplified under cloud computing as it introduces new problem domains. One of the major problem domain identified is data security, where security of user’s data is the utmost priority. Providing privacy to the user and the data stored by the user is to be ensured by the cloud service provider. This paper aims at the security model for cloud computing which ensures the data security and integrity of user’s data stored in the cloud using cryptography.

With the rapid advancements and notable achievements across various application domains, Machine Learning (ML) has become a vital element within the Internet of Things (IoT) ecosystem. Among these use cases is IoT security, where numerous systems are deployed to identify or thwart attacks, including intrusion detection systems (IDSs), malware detection systems (MDSs), and device identification systems (DISs). Machine Learning-based (ML-based) IoT security systems can fulfill several security objectives, including detecting attacks, authenticating users before they gain access to the system, and categorizing suspicious activities. Nevertheless, ML faces numerous challenges, such as those resulting from the emergence of adversarial attacks crafted to mislead classifiers. This paper provides a comprehensive review of the body of knowledge about adversarial attacks and defense mechanisms, with a particular focus on three prominent IoT security systems: IDSs, MDSs, and DISs. The paper starts by establishing a taxonomy of adversarial attacks within the context of IoT. Then, various methodologies employed in the generation of adversarial attacks are described and classified within a two-dimensional framework. Additionally, we describe existing countermeasures for enhancing IoT security against adversarial attacks. Finally, we explore the most recent literature on the vulnerability of three ML-based IoT security systems to adversarial attacks.

A walkthrough of the emerging IoT paradigm: Visualizing inside functionalities, key features, and open issues

On the interplay of Internet of Things and Cloud Computing: A systematic mapping study

When several independent channels are coupled by a parity check constraint on their inputs, the mutual information between the input of one channel and the outputs of all other channels can be expressed as a combination of the mutual information between the input and the output of each individual channel. This concept is denoted as information combining. For binary-input symmetric discrete memoryless channels, we present bounds on the combined information which are only based on the mutual information of the channels. Furthermore, we show that these bounds cannot be further improved. Exact expressions are provided for the case that all channels are binary symmetric channels and for the case that all channels are binary erasure channels.

In this paper, we study the problem of simulating a DMC channel from another DMC channel under an average-case and an exact model. We present several achievability and infeasibility results, with tight characterizations in special cases. In particular for the exact model, we fully characterize when a BSC channel can be simulated from a BEC channel when there is no shared randomness. We also provide infeasibility and achievability results for simulation of a binary channel from another binary channel in the case of no shared randomness. To do this, we use properties of Rényi capacity of a given order. We also introduce a notion of "channel diameter" which is shown to be additive and satisfy a data processing inequality.

In this paper, we put forward the tree-structured expectation propagation (TEP) algorithm for decoding block and convolutional low-density parity-check codes over any binary channel. We have already shown that TEP improves belief propagation (BP) over the binary erasure channel (BEC) by imposing marginal constraints over a set of pairs of variables that form a tree or a forest. The TEP decoder is a message-passing algorithm that sequentially builds a tree/forest of erased variables to capture additional information disregarded by the standard BP decoder, which leads to a noticeable reduction of the error rate for finite-length codes. In this paper, we show how the TEP can be extended to any channel, specifically to binary memoryless symmetric (BMS) channels. We particularly focus on how the TEP algorithm can be adapted for any channel model and, more importantly, how to choose the tree/forest to keep the gains observed for block and convolutional LDPC codes over the BEC.

Big data to use JAVA, group software engineering, networking, cloud computing knowledge and technology. The purpose of this paper is Research on Wireless sensor network of the Internet of things. The Internet of things, mainly includes multicast network, ZIGBEE network, WSN network, Bluetooth network, infrared network and so on. Swarm software engineering is a way to implement cloud computing, Internet of things and big data. Cloud computing comes from big data, the Internet of things can be achieved through cloud computing. This paper mainly studies, computers, software, networks, smart cities, and the use of Excel and Matlab and Microsoft Office Visio 2003 mapping and so forth. And through the practice of research methods, including automotive networking and smart city. What is the Internet of things, objects connected to the Internet is the Internet of things, cup networking, car networking. Things better than other networks, is composed of what objects, what composition, what nature, what innovation and superiority. The four key technologies of the Internet are widely used, and these four technologies are mainly RFID, WSN, M2M, two kinds of integration. RFID can be implemented using MATLAB, NS2, and JAVA, and WSN can be implemented using NS2, and M2M can be developed using JAVA. The research results are that the Internet of things originated and developed in the Internet; on the contrary, the development of the Internet of things further promoted the Internet to a more widespread “Internet” evolution. The Internet of things and the Internet are the relationships between the parent and the child. Wireless networks are just like wireless WSN networks, but wireless nodes are fixed and moved into sensors. The Internet of things includes Internet technology, including wired and wireless networks. The research conclusion is the wireless internet of things are just like the wired internet of things, wired provides the basis for Wireless Research. Baidu Tiangong Internet of things platform, Amazon AWS platform, ERP and so on are also important applications of the Internet of things. The use of mirroring and replication software also requires Internet of things. Video is composed of multi frame images. H.264 and JM encoder are also the future directions. Where to go network Ctrip network is the application of the Internet of things. Recalling what we do today, planning tomorrow, how to spend the university stage and predicting the future of university development can be the application of the Internet of things. I visited the Tangshan Rd. Mart store in Qian’an from January 2017 to March. The feeling is that Beijing is still Carrefour and Jing Kelong and WAL-MART and Hualian. The electricity supplier which Ma Yun’s Alibaba still feels more powerful. Now Hebei province and all parts of the country have venture capital and subsidy, which will undoubtedly promote the Internet of things.

Technologies such as cloud, fog, and edge computing offer exceptional solutions for many technical problems in the Internet of Things (IoT). Cloud computing offers enormous flexibility in terms of resource availability and demand. In this paper, the role of cloud, edge, and fog computing in IoT was discussed along with the emergence of numerous IoT smart scenarios which are difficult to implement. This research is important in guiding future network paradigms that require faster processing with less delay and disruption, and the advanced technologies in them can address the challenges of IoT. It also reviews cloud, fog, and edge computing and their relevance to the IoT.

Internet of things intrusion detection model and algorithm based on cloud computing and multi-feature extraction extreme learning machine

One of the popular articulations in the information technology is the Internet of Things (IoT). The IoT plans to unite everything in our world under an average establishment, giving us control of things around us, yet furthermore keeping us taught in regard to the state of the things. This process needs a large amount of data to feed into the IoT devices; also to represent the intelligence of the IoT devices in a readable format, added equipment is required. Technology has been evolving, and there is a solution for every problem. The IoT in healthcare is a primary application space, which draws gigantic interest from the industry, research organization, and public sectors. The improvement of the IoT and cloud computing is working on tolerant security, staff satisfaction, and functional adequacy in the clinical business. Cloud computing is a savior for the issue of handling large data that is associated with processing reality with the IoT devices. Cloud computing is transforming information technology. It takes care of various issues of customary processing, including top burdens, introducing programming updates, and using vast prediction stages. In this context, it is important to segregate the data based on its significance, which needs the technology of natural language processing to process data. This research paper discusses on how natural language processing can help the IoT along with cloud computing that aids for data storage.KeywordsInternet of Things (IoT)HealthcareNatural language processing