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The advancement in technology has transformed Cyber Physical Systems and their interface with IoT into a more sophisticated and challenging paradigm. As a result, vulnerabilities and potential attacks manifest themselves considerably more than before, forcing researchers to rethink the conventional strategies that are currently in place to secure such physical systems. This manuscript studies the complex interweaving of sensor networks and physical systems and suggests a foundational innovation in the field. In sharp contrast with the existing IDS and IPS solutions, in this paper, a preventive and proactive method is employed to stay ahead of attacks by constantly monitoring network data patterns and identifying threats that are imminent. Here, by capitalizing on the significant progress in processing power (e.g. petascale computing) and storage capacity of computer systems, we propose a deep learning approach to predict and identify various security breaches that are about to occur. The learning process takes place by collecting a large number of files of different types and running tests on them to classify them as benign or malicious. The prediction model obtained as such can then be used to identify attacks. Our project articulates a new framework for interactions between physical systems and sensor networks, where malicious packets are repeatedly learned over time while the system continually operates with respect to imperfect security mechanisms.

Today, the significance of biometrics is more pronounced than ever in accurately allowing access to valuable resources, from personal devices to highly sensitive buildings, as well as classified information. Researchers are pushing forward toward devising robust biometric systems with higher accuracy, fewer false positives and false negatives, and better performance. On the other hand, machine learning (ML) has been shown to play a key role in improving such systems. By constantly learning and adapting to users' changing biometric patterns, ML algorithms can improve accuracy and performance over time. The integration of ML algorithms with biometrics, however, introduces vulnerabilities in such systems. This article investigates the new issues of concern that come about because of the adoption of ML methods in biometric systems. Specifically, techniques to breach biometric systems, namely, data poisoning, model inversion, bias injection, and deepfakes, are discussed. Here, the methodology consisted of conducting a detailed review of the literature in which ML techniques have been adopted in biometrics. In this study, we included all works that have successfully applied ML and reported favorable results after this adoption. These articles not only reported improved numerical results but also provided sound technical justification for this improvement. There were many isolated, unsupported, and unjustified works about the major advantages of ML techniques in improving security, which were excluded from this review. Though briefly mentioned, we did not touch upon encryption/decryption aspects, and, accordingly, cybersecurity was excluded from this study. At the end, recommendations are made to build stronger and more secure systems that benefit from ML adoption while closing the door to adversarial attacks. [ABSTRACT FROM AUTHOR]

An algorithm, which performs connected components detection in the course of decoding ITU-T (former CCITT) facsimile Group 3/4, i.e., MH/MR/MMR compressed images is presented. New definitions of mode color and a new transition element are introduced that allow MR/MMR codes to analyze and derive information about connection of black runs in two adjacent scan lines in the course of decoding. The experiments on the standard set of eight CCITT documents have shown that, on the average, the complexity of direct processing of MR/MMR codes is lower by a factor of 20 and 2.5 than that for raster images and MH codes processing respectively. Data structures for image vector description are discussed. Index Terms--Document image, CCITT (ITU) Group 3/4 compression, MH/MR/MMR, connected components.

Fault tolerance of star-graph networks is investigated. Star-graph network robustness under node failures, link failures, and combined node and link failures, is shown.

The ability to digitize images has led to the prospect of reducing the physical space requirements, material costs, and manual labor of traditional film handling tasks in hospitals. The system which handles the acquisition, storage, and transmission of medical images is called a Picture Archiving and Communication System (PACS). One of the most important components of a PACS is the network, which connects the various sub-systems of a PACS. Several high-speed networking technologies have evolved in the last decade, Asynchronous Transfer Mode (ATM) is one of them. This paper discusses some of the issues to be resolved for rapid and effective deployment of ATM into PACS. The work is useful in providing the latest information for any future research on ATM-based PACS. Keywords: ATM; Picture archiving; Communication system; Quality of service; LANS; Internet.

A study was conducted to examine the problem of packing hypercubes into an n-dimensional star graph characterized by the embedding of a disjoint union of hypercubes. Various combinations of node mapping functions and routing algorithms were considered in the study while the load of a packing was denoted as the maximum number of nodes mapped to any single node. Some of the metric addressed in the study include load, expansion, congestion, and expansion.

The concept of reflected link label sequence helps in defining generalized gray codes (GGC) in order to map linear arrays and rings on a multiply-twisted cube. Hamiltonian paths and cycles can be identified using GGCs. The potential of GGCs can be extended to embedding other networks into the n-dimensional multiply-twisted cube. Besides providing a method of mapping multi-dimensional meshes, GGCs can be used to work out simple and useful programs for dynamic processor allocations of the multiply-twisted cube.

Star graphs have been shown to have Hamilton cycles. The useful properties of star graphs have been listed. The star graph is a Cayley graph which serves as an alternative interconnection network for multiprocessors instead of the hypercube. Hypercubes have the advantage of being enumerated. A limit on the diameter is given with prefixes or suffixes resulting in an O(n) interval broadcast algorithm.

Hypercube networks offer a feasible cost-effective solution to parallel computing. Here, a large number of low-cost processors with their own local memories are connected to form an n-cube ([B.sub.n]) or one of its variants; an the inter-processor communication takes place by message passing instead of shared variables. This paper addresses a constrained two-terminal reliability measure referred to as distance reliability (DR) as it considers the probabilit that a message can be delivered in optimal time from a given node s to a node t The problem is equivalent to that of having an operational optimal path (not just any path) between the two nodes. In [B.sub.n], the Hamming distance betwee labels of s and t or H(s, t) determines the length of the optimal path between the two nodes. The shortest distance restriction guarantees optimal communication delay between processors and high link/node utilization across th network. Moreover, it provides a measure for the robustness of symmetric networks. In particular, when H(s, t) = n in [B.sub.n], DR will yield the probability of degradation in the diameter, a concept which directly relates to fault-diameter. The paper proposes two schemes to evaluate DR in [B.sub.n]. The first scheme uses a combinatorial approach by limiting the number of faulty components to (2H(s, t) - 2), while the second outlines paths of length H(s, t) and, then generates a recursive closed-form solution to compute DR. The theoretical results have been verified by simulation. The discrepancy between the theoretical and simulation results is in most cases below 1% and in the worst case 4.6%. Key Words: Disjoint paths, distance reliability, hamming distance, hypercube networks, node/link failure, physical distance.

The use of a new code, the star code (SC), makes possible the efficient identification of substars in a star network and the optimal static allocation of jobs. This code in the star network plays a role similar to that of the Gray code in the hypercube network. Complete substar recognition needs a set of SCs with a cardinality that is exponential in the dimension of the network.

The design of large circuits to interconnect many processors with low number of I/O ports and short diameter has been one of the major goals of researchers. Unfortunately, the underlying topologies of most of the popular circuits have a degree of the node which is a function of the network size. From the implementation viewpoint, networks with unbounded degree of the node pose two problems. First, there is a limit on the number of I/O channels allocated to a processor. Second, the I/O unit of the processor modules may need to be modified as the result of network expansion. In this paper, the design of high performance networks with constant degree of the node is addressed. A transformation applied to a circuit with a varying degree is essentially the replacement of each node with a topology with constant degree. The resulting hybrid circuit usually yields an efficient realization while preserving the advantageous features of designs with unbounded degree. New circuits such as: star-connected cycles, pancake-connected cycles, and bubble-sort connected cycles are introduced as examples of large circuits with constant degree of the node. The proposed networks lend themselves to an efficient VLSI implementation without compromising their efficiency in performing certain parallel algorithms.