Detecting and mitigating Denial-of-Service (DoS) attacks is crucial for ensuring the availability and security of online services. While various machine learning (ML) models have been utilized for DoS attack detection, there is a need for innovative approaches to improving their performance, especially for the more challenging multi-class detection problem. In this article, we propose adopting a cutting-edge approach called Combinatorial Fusion Analysis (CFA), which leverages a recently developed framework to combine multiple ML models for improved DoS attack detection. Our methodology involves advanced score combination, rank combination, weighted combination techniques, and the diversity strength of scoring systems. Through rigorous performance evaluations, we showcase the efficacy of the combinatorial fusion approach. Our evaluations encompass key metrics such as detection precision, recall, and F1-score, providing comprehensive insights into the interpretability and effectiveness of our approach. We highlight the challenge faced by individual models in classifying low-profiled attacks, while excelling in other attack types. To overcome this limitation, model fusion techniques were used to create a comprehensive model capable of addressing both low-profiled attacks and other traffic types. Furthermore, our findings highlight the potential of this approach for enhancing DoS attack detection capabilities and contributing to the development of more robust defense mechanisms.

Ensemble learning is a well established body of methods for machine learning to enhance predictive performance by combining multiple algorithms/models. Combinatorial Fusion Analysis (CFA) has provided method and practice for combining multiple scoring systems, using rank-score characteristic (RSC) function and cognitive diversity (CD), including ensemble method and model fusion. However, there is no general-purpose Python tool available that incorporate these techniques. In this paper we introduce InFusionLayer, a machine learning architecture inspired by CFA at the system fusion level that uses a moderate set of base models to optimize unsupervised and supervised learning multiclassification problems. We demonstrate InFusionLayer's ease of use for PyTorch, TensorFlow, and Scikit-learn workflows by validating its performance on various computer vision datasets. Our results highlight the practical advantages of incorporating distinctive features of RSC function and CD, paving the way for more sophisticated ensemble learning applications in machine learning. We open-sourced our code to encourage continuing development and community accessibility to leverage CFA on github: https://github.com/ewroginek/Infusion

Mobile target tracking with artificial intelligence (AI) approaches such as deep reinforcement learning (DRL) in edge-assisted Internet of Things (Edge-IoT) platform can be promising. In this article, we proposeDRLTrack, a framework for target tracking with a collaborative DRL called C-DRL in Edge-IoT with the aim to obtain two major objectives: high quality of tracking (QoT) and resource-efficient network performance. InDRLTrack, a huge number of IoT devices are employed to collect data about a target of interest. One or two edge devices in the network coordinate with a group of IoT devices and collaboratively detect the target by using the C-DRL approach and form an area around the target by the group of IoT devices. To maintain such an area during the tracking time, we employ a deep Q-network to track the target from one group to another. An EdgeAI sitting on the top of the edge devices has the control of the C-DRL approach during tracking and can identify a sequence of tracks.DRLTrackis said to betrustworthyas it shows trustworthy performance in terms of QoT, dynamic environments, and even under certain cyberattacks. We validate the performance ofDRLTrackconsidering the objectives through simulations and it demonstrates superior performance compared with existing work.

A combination of multiple retrieval systems can outperform its individual component systems, but it remains a challenging problem to predict whether two systems can be beneficially combined and, if so, the optimal means by which they should be merged. The performance of combined systems is affected by many factors, including the performance of individual systems, the diversity between a pair of systems, and the method for combination. In this paper, we undertake the study of these issues using combinatorial fusion algorithm (CFA) utilizing the rank-score characteristic (RSC) function and the notion of a weighted cognitive diversity. Using the selected eight TREC datasets, we demonstrated that: (a) the combination of two retrieval systems performs better than each individual system only when the individual systems have relatively good performance and they are diverse, (b) a dynamic combination method, using rank vs. score combination based on cognitive diversity which does not display a tight correlation with other statistical diversity measures, can improve the performance of the combined system, even when performance of each individual system is not known or in the context of an unsupervised learning environment. Within the TREC datasets, the proposed dynamic approach offers a potential for substantial improvement with no significant risk. Our results provide a new paradigm of dynamic fusion to the study of the combination of multiple retrieval systems.

Combining multiple retrieval systems is a commonly used method to improve the retrieval performance. However, it is still a challenging problem to figure out when and how the combined system can perform better than its individual systems. In this paper, we study these issues by using an information fusion paradigm: Combinatorial Fusion Analysis (CFA). TREC datasets are used as our experiment data. We measure the cognitive diversity between different individual systems by using a rank-score characteristic (RSC) function. Our results demonstrate that: 1) The performance of combination of p systems does not always increase with p, 2) Rank combination is better than score combination in particular when RSC diversity between two individual systems is large enough, and 3) combination of two systems can improve performance only if the two individual systems have relative good performance and are diverse.

As the scales of parallel applications and platforms increase the negative impact of communication latencies on performance becomes large. Fortunately, modern High Performance Computing (HPC) systems can exploit low-latency topologies of high-radix switches. In this context, we propose the use of random shortcut topologies, which are generated by augmenting classical topologies with random links. Using graph analysis we find that these topologies, when compared to non-random topologies of the same degree, lead to drastically reduced diameter and average shortest path length. The best results are obtained when adding random links to a ring topology, meaning that good random shortcut topologies can easily be generated for arbitrary numbers of switches. Using flit-level discrete event simulation we find that random shortcut topologies achieve throughput comparable to and latency lower than that of existing non-random topologies such as hypercubes and tori. Finally, we discuss and quantify practical challenges for random shortcut topologies, including routing scalability and larger physical cable lengths.

Microarray technology is a popular and informative technique widely used in experimental molecular biology, which can produce quantitative expression measurements for thousands of genes in a single cellular mRNA sample. Analysis methods for determining significant genes are essential to extracting information from the multitude of data generated from a single microarray experiment. Raw gene expression measurements alone, most often, do not indicate significant genes for the given condition. While analysis methods are abundant, there is a need for enhanced performance when attempting to identify significant genes from such experiments. Additionally, the ability to more accurately predict informative genes from cross-laboratory and/or cross-experiment data can certainly aid in disease detection. We propose the application of combinatorial fusion analysis (CFA) in order to enhance and expedite the identification of significant genes in a cross-experiment analysis. Previous methods to identify significant genes applied SAM to analyze the data sets and then took the intersection of top ranked genes. In this paper, we used CFA to combine the scoring functions of two data sets produced by SAM. Moreover, both score and rank combinations are used. Both combinations can achieve better results than the previous approach of taking the intersection. In addition, by using the rank-score characteristic function as a diversity measure, we are able to show that rank combination performed better than score combination. CFA can robustly identify significant genes from multiple microarray data sets so that experimental biology researchers can efficiently perform the next phase of analysis on a smaller subset of genes.

The increase in autism prevalence has been the motivation for much research which has produced various theories for its causation. Genetic and environmental factors have been investigated. An area of focus is the affect of exposure to neurotoxins, such as mercury and lead, during critical stages in a childpsilas early development. In this study we apply Combinatorial Fusion Analysis (CFA) and Association Rule Mining (ARM) to autism prevalence, mercury, and lead data to generate hypotheses and explore possible associations.

Mathematical models in systems biology can use quantitative techniques to study the integrated behaviors of biological systems in macro level. Agent-based modeling provides a qualitative framework, focusing on how each individual molecule behaves in micro level. We are motivated to combine their features together to describe the gene regulatory networks, in particular the emergence of macro phenomena from micro interactions. An agent-based model will be well defined as general as possible, including of many agents of various molecular species interacting in it. The mathematical premises, which are grounded in the micro behaviors of agents, can derive the chemical master equations and the stochastic differential equations for modeling the integrated behaviors of systems in macro level. Combing agent-based models with stochastic differential equations affords a more complete perspective on gent regulatory networks. In addition, the sources and magnitudes of deterministic dynamics and random noises are explicitly characterized.

Feature selection is an important method for improving the efficiency and accuracy of text categorization algorithms by removing redundant and irrelevant terms from the corpus.Extensive researches have been done to improve the performance of individual feature selection methods, but not much on their combinations.In this paper, we propose a method of combining multiple feature selection methods by using the combinatorial fusion analysis (CFA). A rank-score function and its graph, called rank-score graph,are adopted to measure the diversity of different feature selection methods.We have shown that a combination of multiple feature selection methods can outperform a single method only if each individual feature selection method has unique scoring behavior and relatively high performance. Moreover, it is shown that the rank-score function and rank-score graph are useful for the selection of a combination of feature selection methods.

The topological explorations of on-chip networks are important for efficiently using their enormous wire resources for low-latency and high-throughput communications using a modest silicon budget. In this paper, we propose a novel tree-based interconnection network called Fat H-Tree that meets these requirements. A Fat H-Tree provides a torus structure by combining two folded H-Tree networks and is an attractive alternative to tree-based networks such as the Fat Trees in a microarchitecture domain. We introduce its chip layout schemes based on a folding technique for 2D and 3D ICs. Three deadlock-free routing schemes are proposed for Fat H-Tree. We evaluate the performance of Fat H-Tree and other tree-based networks using real application traces. In addition, the network logic area, wire resource, and energy consumption of Fat H-Tree are compared with other topologies, based on a typical implementation of on-chip routers synthesized with a 90-nm standard cell library. The results show that (1) a Fat H-Tree outperforms a Fat Tree with two upward and four downward connections in terms of the throughput and average hop count, (2) a Fat H-Tree requires 19.8 percent-27.8 percent smaller network logic area than the Fat Tree, (3) a Fat H-Tree consumes slightly less energy than the Fat Tree does, and (4) a Fat H-Tree uses slightly more wire resources than the Fat Tree, but the current process technology can provide sufficient wire resources for implementing Fat-H-Tree-based on-chip networks.

We address the problem of sensor fusion for stereo and ultrasound depth measurements for map building for a robot operating in a cluttered environment. In such a situation it's difficult to make useful and realistic assumptions about the sensor or environment statistics. Combinatorial Fusion Analysis is used to develop an approach to fusion with unknown sensor and environment statistics. A metric is proposed that shows when fusion from a set of fusion alternatives will produce a more accurate estimation of depth than either sonar or stereo alone and when not. The metric consists of two criteria: (a) the performance ratio PR(A,B) between sensors A and B, and (b) the diversity d(A,B) between A and B as captured by the rank-score function fA and fB. Experimental results are reported to illustrate that these two CFA criteria are viable predictors to distinguish between positive cases (the combined system performs better than or equal to the individual systems) and negative cases.

We address the problem of automated video tracking of targets when targets undergo multiple mutual occlusions. Our approach is based on the idea that as targets are occluded, selection of feature subsets and combinations of those features are effective in identifying the target and improving tracking performance. We use combinatorial fusion analysis to develop a metric to select which subset of features will produce the most accurate tracking. In particular we show that the combination of a pair of features A and B will improve the accuracy only if (a) A and B have relative high performance, and (b) A and B are diverse. We present experimental results to illustrate the performance of the proposed metric

In many useful video tracking situations, targets move through repeated mutual occlusions. As targets undergo occlusions, the feature subsets and combinations of those features that are effective in identifying the target and improving tracking performance may change. We use combinatorial fusion analysis to select and evaluate criteria by which to identify the combination of features that will produce the most accurate tracking. In particular we show that the combination of a pair of features A and B will improve the accuracy only if (a) A and B have relative high performance, and (b) A and B are diverse. We present experimental results from three diverse video sequences to illustrate the performance of the proposed criteria

Abstract Letuandvbe any two distinct nodes of an undirected graphG, which isk‐connected. A containerC(u,v) betweenuandvis a set of internally disjoint paths {P1,P2,…,Pw} betweenuandvwhere 1 ≤w≤k. The width ofC(u,v) iswand the length ofC(u,v) {written aslC(u,v) is max {l(Pi) ∣ 1 ≤i≤w}. Aw‐containerC(u,v) is a container with widthw. Thew‐wide distance betweenuandv,dw(u,v), is min {l(C(u,v)) ∣C(u,v) is aw‐container}. Aw‐containerC(u,v) of the graphGis aw*‐container if every node ofGis incident with a path inC(u,v). That means that thew‐containerC(u,v) spans the whole graph. LetSnbe then‐dimensional star graph withn≥ 5. It is known thatSnis bipartite. In this article, we show that, for any pair of distinct nodesuandvin different partite sets ofSn, there exists an (n− 1)*‐containerC(u,v) and the (n− 1)‐wide distanced(n− 1)(u,v) is less than or equal to${n!\over n-2}+1$ . In addition, we also show the existence of a 2*‐containerC(u,v) and the 2‐wide distanced2(u,v) is bounded above by${n!\over 2}+1$ . © 2006 Wiley Periodicals, Inc. NETWORKS, Vol. 48(4), 235–249 2006

Automated video tracking is useful in a number of applications such as surveillance, multisensor networks, robotics and virtual reality. In this paper we investigate an approach to tracking based on fusing the output of a collection of video trackers, each attending to a different feature or cue on the target. We show both theoretically and experimentally that the method used to prune the growth of target hypotheses can have a great impact on the trackers performance, and indirectly, change the benefit of using linear score combination as opposed to a non-linear rank combination for fusion. We also show that the rank-score graph defined by Hsu and Taksa can be used to select a subset of features to fuse to reduce classification error.

An attractive approach to improve tracking performance for visual surveillance is to use information from multiple visual sensory cues such as position, color, shape, etc. Previous work in fusion for tracking has tended to focus on fusion by numerically combining the scores assigned by each cue. We argue that for video scenes with many targets in a crowded situation, the splitting and merging of regions associated with targets, and the subsequent dramatic changes in cue values and reliabilities, renders this form of fusion less effective. In this paper we present experimental results showing that use of cue rank information in fusion produces a significantly better tracking result in crowded scenes. We also present a formalization of this fusion problem as a step in understanding why this effect occurs and how to build a tracking system that exploits it.

The classification of protein structures is essential for their function determination in bioinformatics. The success of the protein structure classification depends on two factors: the computational methods used and the features selected. In this paper, we use a combinatorial fusion analysis technique to facilitate feature selection and combination for improving predictive accuracy in protein structure classification. When applying these criteria to our previous work, the resulting classification has an overall prediction accuracy rate of 87% for four classes and 69.6% for 27 folding categories. These rates are significantly higher than our previous work and demonstrate that combinatorial fusion is a valuable method for protein structure classification.

This paper introduces a novel scheduling problem called the active interval scheduling problem in hierarchical wireless sensor networks for long-term periodical monitoring applications. To improve the report sensitivity of the hierarchical wireless sensor networks, an efficient scheduling algorithm is desired. In this paper, we propose a compact genetic algorithm (CGA) to optimize the solution quality for sensor network maintenance. The experimental result shows that the proposed CGA brings better solutions in acceptable calculation time.

We compare three kinds of information networks arising from artificial engineering systems (AES'), and biological and neural systems (BNS'). Graphical modeling systems (GMS') are then used to define the concept of a "graph container" indicating the existence of parallel paths in an AES or BNS. Graph containers are used to study information delay and network vulnerability. Various properties related to a graph container are defined and studied in an information network. Design and construction of networks with optimal properties are discussed. We also survey recent results on graph containers and their applications.

Microarray technology is a recent development in experimental molecular biology which can produce quantitative expression measurements for thousands of genes in a single, cellular mRNA sample. These many gene expression measurements form a composite profile of the sample, which can be used to differentiate samples from different classes such as tissue types or treatments. However, for the gene expression profile data obtained in a specific comparison, most likely only some of the genes will, be differentially expressed between the classes, while many other genes have similar expression levels. Selecting a list of informative differential genes from these data is important for microarray data analysis. In this paper, we describe a framework for selecting informative genes, called ranking and combination analysis (RAC), which combines various existing informative gene selection methods. We conducted experiments using three data sets and six existing feature selection methods. The results show that the RAC framework is a robust and efficient approach to identify informative gene for microarray data. The combination approach on two selecting methods almost always performed better than the less efficient individual, and in many cases, better than both. More significantly, when considering all three data sets together, the combination approach, on average, outperforms each individual feature selection method. All of these indicate that RCA might be a viable and feasible approach for the microarray gene expression analysis.

We study a novel approach to the problem of fusion of sensory information in tracking multiple targets in CCTV surveillance video. The approach, called "rank and fuse" (RAF) is based on multiple feature ranking and merging as opposed to a more typical combination of all scores (similarity or probability) in a single ranking. This has the advantages of low computational complexity, easy scalability to multiple features, and low-latency. Experimental results are presented to illustrate two aspects of the RAF approach for a "difficult" example from CCTV surveillance: the advantage of rank versus score combination, and the use of the rank versus score curve to decide which features to fuse.

In this paper, we study the pyramid network (also called pyramid), one of the important architectures in parallel computing, network computing, and image processing. Some properties of pyramid networks are investigated. We determine the line connectivity and the fault diameters in pyramid networks. We show how to construct a path between two nodes in the faulty pyramid networks in polynomial time. A polynomial-time algorithm is also given for generating the containers in pyramid networks. Our results show that pyramid networks have very good fault tolerance properties.

We propose a secure auction-like negotiation protocol for agent based Internet trading, which not only retains the agent's mobility and flexibility, but also takes secure measures to prevent attacks from malicious hosts during the negotiation process. The particular features of the proposed protocol are: (1) negotiation for agent based trading is performed through a novel pattern of electronic auction; (2) negotiation results between two hosts are ensured to be valid with their signatures; (3) malicious actions can be detected and the breeder can be dug out by the help of sociological factors; (4) information gathering and negotiation processes are combined together while few communications are needed.

In this paper, we consider the problem of constructing the shortest two-connected Steiner network on the Euclidean plane. For a given set P of points on the Euclidean plane, let l2(P) denote the length of the shortest two-connected Steiner network on P divided by the length of the shortest two-connected spanning network on P. We prove that l2(P) = 1, if any one of the following conditions is satisfied: (1) All points in P are on the sides of the convex hull of P; (2) all points except one in P are on the sides of the convex hull of P; or (3) the cardinality of P is no greater than 5. Moreover, we obtain general lower and upper bounds for l2(P) as follows: (√3/2) ≤ inf{l2(P) | P} ≤ [(√3 + 2)/(√3 + 6)]. We also show that Christofides' heuristic for the traveling salesman problem can be used to design a polynomial-time algorithm for finding the shortest two-connected Steiner and spanning network with guaranteed worst-case performance ratio of √3 and 3/2;, respectively. © 1998 John Wiley & Sons, Inc. Networks 32: 133–140, 1998

We consider the problems of routing and sorting on a de Bruijn network. First, we show that any deterministic oblivious routing scheme for permutation routing on a d-ary de Bruijn network with N=d/sup n/ nodes, in the worst case, will take /spl Omega/(/spl radic/N) steps under the single-port model. This improves the existing lower bounds provided d is not a constant. We also show that the lower bound is indeed a tight one. Second, we present a deterministic nonoblivious permutation routing algorithm which runs in O(d.n/sup 2/) time on a d-ary de Bruijn network with N=d/sup n/ nodes. This algorithm is currently the fastest known nonoblivious deterministic routing algorithm for de Bruijn networks of arbitrary degree. Finally, we present an efficient general sorting algorithm for the de Bruijn networks of arbitrary degree. This algorithm is the best sorting algorithm known so far. It runs in O((log d).d.n/sup 2/) time for directed de Bruijn network with d/sup n/ nodes, degree d, and diameter n. As a corollary, we show that on a binary de Bruijn network of Nnodes, our sorting scheme requires at most 2 log/sup 2/ Nsteps.

Let $G( N,A )$ be the Cayley digraph associated with $Z/( N )$ and A, where N is a positive integer and A is a subset of $\{ 1,2, \ldots ,N - 1 \}$. Let $N( d,k )$ be the maximum N such that the diameter of $G( N,A )$ is less than or equal to d for some $A = \{ a_1 ,a_2 , \ldots , a_k \}$ with $1 = a_1 < a_2 < \cdots < a_k $. An exact formula for $N( d,2 )$ is given, and $N( d,k )$ is estimated for $k \geq 3$. These results provide new bounds for minimal diameter in the construction of loop networks. A relation between this problem and the postage stamp problem in additive number theory is established to enhance the study of these problems.

A graph has spread (m, k, l) if for any m+1 distinct nodes x, y/sub 1/, . . ., y/sub m/ and m positive integers r/sub 1/, . . ., r/sub m/, such that Sigma /sub i/r/sub i/=k, there exist k node-disjoint paths of length at most 1 from x to the y/sub i/, where r/sub i/ of them end at y/sub i/. This concept contains, and is related to many important concepts used in communications and graph theory. The authors prove an optimal general theorem about the spreads of digraphs generated by line digraph iterations. Useful graphs, like the de Bruijn and Kautz digraphs, can be thus generated. The theorem is applied to the de Bruijn and Kautz digraphs to derive optimal bounds on their spreads, which implies previous results and resolves open questions on their connectivity, diameter, k-diameter, vulnerability, and some other measures related to length-bound disjoint paths.>

Abstract The problem under consideration arises from studies on local networks and multimodule memory organizations. The ring network has been one of the popular network topologies used in the design and implementation of local area networks and other configurations. We consider here a generalization of the ring network by adding two fixed‐step links to each node. The resulting networks have low diameter, easy routing, and switching structure and therefore are suitable for implementation in the design of reliable networks. Let N denote the number of nodes in the network. For a given N, we are concerned with the problem of determining the best topologies to minimize the diameter (and, hence, the transmission delay) of the network. We obtain new classes of values of N for which topologies can be found that achieve the lower bound lb = [(√2N ‐ 1 ‐ 1)/2] for the minimum diameter. We also show that for some infinite classes of N this lower bound lb is not achievable.

We consider networks of processors where each processor either has one in-link and one out-link, or two in-links and two out-links. We study three properties of such networks: 1) diameter, 2) connectivity, and 3) the ring property. We propose a class of networks which seem to achieve the optimum as far as these three properties are concerned.