Set Of Packets Research Articles

On wavefront sets of global Arthur packets of classical groups: Upper bound

We prove a conjecture of the first named author (2014) on the upper bound Fourier coefficients of automorphic forms in Arthur packets of all classical groups over any number field. This conjecture generalizes the global version of the local tempered L -packet conjecture of Shahidi (1990). Under certain assumption, we also compute the wavefront sets of the unramified unitary dual for split classical groups.

Implementation of Load Balancing Using Per Connection Classifier Method Using Mikrotik RB1100

The Implementation of Load Balancing Using Per Connection Classifier Method Using Mikrotik RB1100
 
 By :
 Pedro Maldini Siagian 1)
 Hari Aspriyono 2)
 Eko Prasetiyo Rohmawan 2)
 
 Poltekkes Kemenkes Bengkulu as a university which has 2.000 students and 250 employees, has internet access through 2 providers, namely Icon Dedicated and Icon Broadband. Even though it has 2 internet lines, the distribution of internet line that is evenly distributed still cannot be implemented. Load Balancing carried out on Mikrotik RB1100 at Poltekkes Kemenkes Bengkulu can help distribute internet traffic evenly, and can overcome lost connections due to backing up broken connections with available connections. Load Balancing used Per Connection Classifier is a method that will send a set of packets over several existing links, taking into original account of packets. In this method, packets coming from one communication session will be routed through only one link. Through QoS (Quality of Service) testing that the result was that load balancing carried out was running well and as expected Poltekkes Kemenkes Bengkulu which can evenly distribute the connection traffic used.
 
 Keywords : Load Balancing, Per Connection Classifier, Mikrotik RB1100, Poltekkes Kemenkes Bengkulu
 
 Infomation:
 1) Student
 2) Supervisors

Ab Initio Double-Differential Ionization Cross-Section Calculations in Antiproton–Helium Collisions

We present ionization cross-sections for antiproton and helium collisions based on an ab initio time-dependent coupled channel method. In our calculations, a finite basis set of regular helium Coulomb wave packets and Slater function were used. The semiclassical approximation was applied with the time-dependent Coulomb potential to describe the antiproton–electron interaction. Three different projectile energies were considered as 10, 50 and 100 keV. We found clear evidence for the formation of the anti-cusp in the differential distributions.

Hop-Constrained Oblivious Routing

We prove the existence of an oblivious routing scheme that is -competitive in terms of , thus resolving a well-known question in oblivious routing. Concretely, consider an undirected network and a set of packets each with its own source and destination. The objective is to choose a path for each packet, from its source to its destination, so as to minimize , defined as follows: The dilation is the maximum path hop length, and the congestion is the maximum number of paths that include any single edge. The routing scheme obliviously and randomly selects a path for each packet independent of (the existence of) the other packets. Despite this obliviousness, the selected paths have within a factor of the best possible value. More precisely, for any integer hop constraint , this oblivious routing scheme selects paths of length at most and is -competitive in terms of congestion in comparison to the best possible congestion achievable via paths of length at most hops. These paths can be sampled in polynomial time. This result can be viewed as an analogue of the celebrated oblivious routing results of Räcke [Proceedings of the 43rd Annual IEEE Symposium on Foundations of Computer Science, 2002; Proceedings of the 40th Annual ACM Symposium on Theory of Computing, 2008], which are -competitive in terms of congestion but are not competitive in terms of dilation.

SeMiner: Side-Information-Based Semantics Miner for Proprietary Industrial Control Protocols

Industrial control protocols (ICPs) are critical for Industrial Internet of Things to achieve interconnection and interaction between the industrial devices. To fully understand a large number of nonstandard and proprietary ICPs, protocol reverse engineering (PRE) techniques are commonly used to reconstruct the ICP specifications. However, existing PRE tools face difficulties in inferring the ICP semantics. Accordingly, this article proposes SeMiner as an ICP semantics analysis framework to achieve the packet field identification, protocol semantics inference, and behavior semantics modeling. Based on the collected graphical side information about the industrial processes, a series of semantic channels is identified using image processing techniques, and a modified Apriori algorithm is used to extract the frequent patterns of each semantic channel. Afterward, a heuristic method based on sequence alignment is designed to simultaneously identify the set of relevant packets and the position of packet fields relevant to the semantic channels. Finally, relying on the packet field semantics, the behavior semantics of industrial processes are modeled and the association rules between the semantic channels are extracted. Thorough experimental results reported herein verify the effectiveness of SeMiner and show the superior performance of SeMiner compared with the several other state-of-the-art algorithms.

Inverse packet fuzzy sets and its characteristics

Based on inverse packet sets and fuzzy set, the concept of inverse packet fuzzy sets is proposed in this paper by means of introducing dynamic characteristics into a fuzzy set A and improving the fuzzy set A. The inverse packet fuzzy sets is composed of a pair of fuzzy sets generated by the fuzzy set A and has dynamic characteristic and fuzzy characteristic. Furthermore, on certain conditions, it can be restored to the fuzzy set A or the inverse packet sets generated by the Cantor set X=ker⁡(A). As a result, an inverse packet fuzzy sets can be expressed as a series of inverse packet sets by defining its scalar multiplication and its cut sets. Meanwhile some attribute-dependent characteristics of inverse packet fuzzy sets are obtained. The new set model will both enrich the set theory and provide a new basic tool for further practical applications.

Improvement of packet transmission scheduling and delivery rate in wireless Ad-Hoc networks

The principal purpose of Ad-Hoc wireless networks is to increase service efficiency in terms of transmission scheduling and packet transfer rate. The approaches that assume frame unicity to satisfy a given set of packets minimize the end-to-end delay. However, they do not guarantee a maximum packet delivery rate due to the difficulty of establishing robust paths for packet transfer across nodes deployed in the network, especially in a three-dimensional (3D) environment. The objective is to minimize the end-to-end delay by ensuring the maximum delivery of packets to their destinations. Furthermore, the signal-to-interference-and-noise-ratio (SINR) model is considered to optimize transmission scheduling. In this paper, an optimal node coordinates optimization approach is proposed to extend two recently investigated schemes in the literature (S-RDSP and I-RDSP). The developed algorithms, named S-MPDR and I-MPDR, seek to reduce the end-to-end delay by delivering a collection of inserted packets over a 3D environment while also maximizing the delivery rate of these packets. Desirability functions are used to evaluate the network’s performance in various scenarios involving two different environments, Level 0 and Level 1. Numerical results demonstrate that the developed algorithms outperform both schemes in terms of end-to-end delay and packet delivery rate. In the Level 0 environment, the overall minimum delay and packet delivery rate scores provided by S-MPDR are increased by 28% and 88% compared to S-RDSP, respectively. In comparison, those provided by I-MPDR are increased by 24% and 16% compared to I-RDSP. Similarly, in the Level 1 environment, the scores provided by S-MPDR are increased by 25% and 100% compared to S-RDSP, respectively, while those provided by I-MPDR are increased by 23% and 25% compared to I-RDSP.

Capacitive Sensor and Alternating Drive Mixing for Microfluidic Applications Using Micro Diaphragm Pumps.

Microfluidic systems are of paramount importance in various fields such as medicine, biology, and pharmacy. Despite the plethora of methods, accurate dosing and mixing of small doses of liquid reagents remain challenges for microfluidics. In this paper, we present a microfluidic device that uses two micro pumps and an alternating drive pattern to fill a microchannel. With a capacitive sensor system, we monitored the fluid process and controlled the micro pumps. In a first experiment, the system was set up to generate a 1:1 mixture between two fluids while using a range of fluid packet sizes from 0.25 to 2 µL and pumping frequencies from 50 to 100 Hz. In this parameter range, a dosing accuracy of 50.3 ± 0.9% was reached, validated by a gravimetric measurement. Other biased mixing ratios were tested as well and showed a deviation of 0.3 ± 0.3% from the targeted mixing ratio. In a second experiment, Trypan blue was used to study the mixing behavior of the system. Within one to two dosed packet sets, the two reagents were reliably mixed. The results are encouraging for future use of micro pumps and capacitive sensing in demanding microfluidic applications.

An Empirical Evaluation of Supervised Learning Methods for Network Malware Identification Based on Feature Selection

Malware is a sophisticated, malicious, and sometimes unidentifiable application on the network. The classifying network traffic method using machine learning shows to perform well in detecting malware. In the literature, it is reported that this good performance can depend on a reduced set of network features. This study presents an empirical evaluation of two statistical methods of reduction and selection of features in an Android network traffic dataset using six supervised algorithms: Naïve Bayes, support vector machine, multilayer perceptron neural network, decision tree, random forest, and K‐nearest neighbors. The principal component analysis (PCA) and logistic regression (LR) methods with p value were applied to select the most representative features related to the time properties of flows and features of bidirectional packets. The selected features were used to train the algorithms using binary and multiclass classification. For performance evaluation and comparison metrics, precision, recall, F‐measure, accuracy, and area under the curve (AUC‐ROC) were used. The empirical results show that random forest obtains an average accuracy of 96% and an AUC‐ROC of 0.98 in binary classification. For the case of multiclass classification, again random forest achieves an average accuracy of 87% and an AUC‐ROC over 95%, exhibiting better performance than the other machine learning algorithms. In both experiments, the 13 most representative features of a mixed set of flow time properties and bidirectional network packets selected by LR were used. In the case of the other five classifiers, their results in terms of precision, recall, and accuracy, are competitive with those obtained in related works, which used a greater number of input features. Therefore, it is empirically evidenced that the proposed method for the selection of features, based on statistical techniques of reduction and extraction of attributes, allows improving the identification performance of malware traffic, discriminating it from the benign traffic of Android applications.

Investigating the Effect of Traffic Sampling on Machine Learning-Based Network Intrusion Detection Approaches

Machine Learning (ML) based Network Intrusion Systems (NIDSs) operate on flow features which are obtained from flow exporting protocols (<i>i.e.,</i> NetFlow). Recent success of ML and Deep Learning (DL) based NIDS solutions assume such flow information (<i>e.g.,</i> avg. packet size) is obtained from all packets of the flow. However, often in practice flow exporter is deployed on commodity devices where packet sampling is inevitable. As a result, applicability of such ML based NIDS solutions in the presence of sampling (<i>i.e.,</i> when flow information is obtained from sampled set of packets instead of full traffic) is an open question. In this study, we explore the impact of packet sampling on the performance and efficiency of ML-based NIDSs. Unlike previous work, our proposed evaluation procedure is immune to different settings of flow export stage. Hence, it can provide a robust evaluation of NIDS even in the presence of sampling. Through sampling experiments we established that malicious flows with shorter size (<i>i.e.,</i> number of packets) are likely to go unnoticed even with mild sampling rates such as 1/10 and 1/100. Next, using the proposed evaluation procedure we investigated the impact of various sampling techniques on NIDS detection rate and false alarm rate. Detection rate and false alarm rate is computed for three sampling rates (<i>i.e.,</i> 1/10, 1/100, 1/1000), for four different sampling techniques and for three (two tree-based, one deep learning based) classifiers. Experimental results show that systematic linear sampler - SketFlow performs better compared to non-linear samplers such as Sketch Guided and Fast Filtered sampling. We also found that random forest classifier with SketchFlow sampling was a better combination. The combination showed higher detection rate and lower false alarm rate across multiple sampling rates compared to other sampler-classifier combinations. Our results are consistent in multiple sampling rates, exceptional case is observed for Sketch Guided Sampling (SGS) as it caused a drastic performance drop when sampling rate was changed from 1/100 to 1/1000. Our results provide valuable insights for network practitioners and researchers regarding on how packet sampling effects ML-based NIDS performance. In this regard full source code for sampling and ML experiments has been released: github.com/Jumabek/sampledFlowMeter and github.com/Jumabek/nids-with-sampling

A complete set of Lorentz-invariant wave packets and modified uncertainty relation

We define a set of fully Lorentz-invariant wave packets and show that it spans the corresponding one-particle Hilbert subspace, and hence the whole Fock space as well, with a manifestly Lorentz-invariant completeness relation (resolution of identity). The position–momentum uncertainty relation for this Lorentz-invariant wave packet deviates from the ordinary Heisenberg uncertainty principle, and reduces to it in the non-relativistic limit.

A Secure Protocol against Selfish and Pollution Attacker Misbehavior in Clustered WSNs

Wireless sensor networks (WSNs) have been widely used for applications in numerous fields. One of the main challenges is the limited energy resources when designing secure routing in such networks. Hierarchical organization of nodes in the network can make efficient use of their resources. In this case, a subset of nodes, the cluster heads (CHs), is entrusted with transmitting messages from cluster nodes to the base station (BS). However, the existence of selfish or pollution attacker nodes in the network causes data transmission failure and damages the network availability and integrity. Mainly, when critical nodes like CH nodes misbehave by refusing to forward data to the BS, by modifying data in transit or by injecting polluted data, the whole network becomes defective. This paper presents a secure protocol against selfish and pollution attacker misbehavior in clustered WSNs, known as (SSP). It aims to thwart both selfish and pollution attacker misbehaviors, the former being a form of a Denial of Service (DoS) attack. In addition, it maintains a level of confidentiality against eavesdroppers. Based on a random linear network coding (NC) technique, the protocol uses pre-loaded matrices within sensor nodes to conceive a larger number of new packets from a set of initial data packets, thus creating data redundancy. Then, it transmits them through separate paths to the BS. Furthermore, it detects misbehaving nodes among CHs and executes a punishment mechanism using a control counter. The security analysis and simulation results demonstrate that the proposed solution is not only capable of preventing and detecting DoS attacks as well as pollution attacks, but can also maintain scalable and stable routing for large networks. The protocol means 100% of messages are successfully recovered and received at the BS when the percentage of lost packets is around 20%. Moreover, when the number of misbehaving nodes executing pollution attacks reaches a certain threshold, SSP scores a reception rate of correctly reconstructed messages equal to 100%. If the SSP protocol is not applied, the rate of reception of correctly reconstructed messages is reduced by 90% at the same case.

Fixed Urgent Window Pass for a Wireless Network with User Preferences

This research considers the router allocation problem in situations with sensitive, highest-priority, and severe demand constraints. Examples include bespoke communication, masquerade communication, war, disasters, and various crises (political, social, etc.). Specifically, by introducing a network component called the scheduler. The scheduler is established to provide the intelligencers the highest priority to forwarding their packets based on their preferences at any time. Intelligencers define their preferences to control the data release time, due time, and static window transmission time-slot. Each window represents a reserved time-slot for the intelligencer. A window is an interval when the intelligencer can send data without any contention and can block any other user trying to gain access to the router during this interval. The reserved interval is called a “static urgent window pass”. For each block of the received data, the scheduler solves a scheduling problem with the windows reserved for the router based on the given constraints, which is an NP-hard problem. This work proposes eight heuristics based on dispatching with constraints, iterative randomization method, and subset-sum method to solve the studied problem. These heuristics are utilized by the two proposed algorithms to forward data from the sender to the receiver based on the specified constraints. The first algorithm prioritizes the set of packets to be sent and is called the “priority packet-based algorithm”. The second algorithm is called the “cumulative packet-based algorithm” and is based on the non-priority set of the remaining packets. At any time other than the specified window, the scheduler rearranges and reschedules all the remaining (non-scheduled) packets. Experimental results showed the performance of the proposed heuristics in terms of gap and time. In the performed experiments, the scheduler was capable to pass the router role and gain the control to send the required data based on the specified constraints.

A Survey of Mobility Management as a Service in Real-Time Inter/Intra Slice Control

In-network softwarization, Network Slicing provides scalability and flexibility through various services such as Quality of Service (QoS) and Quality of Experience (QoE) to cover the network demands. For the QoS, a set of policies must be considered in real-time, accompanied by a group of functions and services to guarantee the end-user needs based on network demand. On the other hand, for the QoE, the service’s performance needs to be improved to bring an efficient service to cover the demands of the end-user. The 3G Partnership Project (3GPP) defined the slice as a component of resources used to process a set of packets. These resources need to be flexible, which means the resources can be scaled up or down based on the demand. This survey discusses softwarization and virtualization techniques, considering how to implement the slices for future networks. Specifically, we discuss current advances concerning the functionality and architecture of the 5G network. Therefore, the paper critically evaluates recent research and systems related to mobility management as a service in real-time inter/intra slice control by considering the strengths and limitations of these contributions to identify the research gaps and possible research directions for emerging research and development opportunities. Moreover, we extend our review by considering the slice types and their numbers based on the 3GPP Technical Specification (3GPP TS). The study presented in this paper identifies open issues and research directions that reveal that mobility management at a service level with inter/intra slice management techniques has strong potential in future networks and requires further investigation from the research community to exploit its benefits fully.

SnapCatch: Automatic Detection of Covert Timing Channels Using Image Processing and Machine Learning

With the rapid growth of data exfiltration carried out by cyber attacks, Covert Timing Channels (CTC) have become an imminent network security risk that continues to grow in both sophistication and utilization. These types of channels utilize inter-arrival times to steal sensitive data from the targeted networks. CTC detection relies increasingly on machine learning techniques, which utilize statistical-based metrics to separate malicious (covert) traffic flows from the legitimate (overt) ones. However, given the efforts of cyber attacks to evade detection and the growing column of CTC, covert channels detection needs to improve in both performance and precision to detect and prevent CTCs and mitigate the reduction of the quality of service caused by the detection process. In this article, we present an innovative image-based solution for fully automated CTC detection and localization. Our approach is based on the observation that the covert channels generate traffic that can be converted to colored images. Leveraging this observation, our solution is designed to automatically detect and locate the malicious part (i.e., set of packets) within a traffic flow. By locating the covert parts within traffic flows, our approach reduces the drop of the quality of service caused by blocking the entire traffic flows in which covert channels are detected. We first convert traffic flows into colored images, and then we extract image-based features for detection covert traffic. We train a classifier using these features on a large data set of covert and overt traffic. This approach demonstrates a remarkable performance achieving a detection accuracy of 95.83% for cautious CTCs and a covert traffic accuracy of 97.83% for 8 bit covert messages, which is way beyond what the popular statistical-based solutions can achieve.

Random inverse packet information and its acquisition

Abstract Packet sets and inverse packet sets are two kinds of novel mathematical tools to analyze dynamic information systems. With advances in inverse packet sets, random inverse packet information is proposed by introducing random characteristics into inverse packet sets. Hence, random inverse packet information has dynamic and random characteristics, and is an extended form of inverse packet sets. Furthermore, random feature, dynamic feature, and identification relation about the random inverse packet information are discussed. Finally, based on the above theory, an instance is used to illustrate the applications of intelligent acquisition of investment information.

An Optical Image Transmission System for Deep Sea Creature Sampling Missions Using Autonomous Underwater Vehicle

The exploration of oceans using autonomous underwater vehicles (AUVs) is necessary for activities, such as the sustainable management of fishery resources, extraction of seafloor minerals and energy resources, and inspection of underwater infrastructure. As the next step in ocean exploration, AUVs are expected to employ end-effectors to make physical contact with seafloor creatures and materials. We propose a scenario for realizing a sampling mission using an AUV that is equipped to sample marine life. In this scenario, the sampling AUV observes the seafloor while concurrently transmitting the observed images to a surface vessel for inspection by the AUV operators. If the received images show an object of interest, the object is selected as a candidate of sampling target by the operators, who send a sampling command to the AUV. After receiving the command, the AUV returns to the target area and attempts to sample it. In this paper, we propose a system for transmitting images of the seafloor as part of the sampling-mission scenario. The proposed image transmission system includes a process for enhancing images of the deep seafloor, a process for selecting interesting images, and processes for compressing and reconstructing images. The image enhancement process resolves imaging problems resulting from light attenuation, such as color attenuation and uneven illumination. The process for selecting interesting images selects those that contain interesting objects, such as marine life. The selection process prevents the transmission of meaningless images that contain only flat sand on the seafloor. The proposed image compression method, which is based on color depth compression, reduces the amount of data. The combined process of selecting an interesting image and compressing it reduces various problems in acoustic communication, such as low information density and data loss. Instead of an overall image, part of an overall image is transmitted by a set of data packet, and each received data packet is reconstructed onboard the vessel. Because of image compression, the colors of a reconstructed image differ from those of an enhanced image. However, the reconstructed image contains similar colors, and the structural similarity index was found to be 91.4% by evaluating images that were subjected to a 4-b color compression. The proposed image transmission system was tested in the Sea of Okhotsk, and these tests were performed four times in different sea areas (minimum depth 380 m, maximum depth 590 m). The results show that the size of the data for a single image was reduced by a factor of 18 using the proposed image compression process, with each image taking 3.7 s to be transmitted via an acoustic modem (20 kb/s). Of the automatically selected images, 63% contained marine life, and the total transmission success rate was 22%.

Temporal modes in quantum optics: then and now

We review the concepts of temporal modes (TMs) in quantum optics, highlighting Roy Glauber’s crucial and historic contributions to their development, and their growing importance in quantum information science. TMs are orthogonal sets of wave packets that can be used to represent a multimode light field. They are temporal counterparts to transverse spatial modes of light and play analogous roles—decomposing multimode light into the most natural basis for isolating statistically independent degrees of freedom. We discuss how TMs were developed to describe compactly various processes: superfluorescence, stimulated Raman scattering, spontaneous parametric down conversion, and spontaneous four-wave mixing. TMs can be manipulated, converted, demultiplexed, and detected using nonlinear optical processes such as three-wave mixing and quantum optical memories. As such, they play an increasingly important role in constructing quantum information networks.

Attendance automatic recognition and learning behavior of web-based course attendance based on machine learning algorithms

At present, the automatic attendance mode of distance education is not conducive to the confirmation and analysis of information after class. In order to study the effective automatic recognition algorithm of remote education classroom, this study takes the educational classroom of intelligent innovation and entrepreneurship of Internet + as an example for analysis. Moreover, this paper adopts facial features as the basis of recognition, establishes corresponding positioning points, and constructs precise positioning methods for real-time feature capture. At the same time, the ASM algorithm is used to extract facial features, and the algorithm is improved to improve the extraction effect. In addition, this paper proposes Gabor-wavelet packet set and Gabor beamlet set for auxiliary recognition, which improves the recognition rate. Finally, this paper designs experiments to analyze the performance of the algorithm of this study. The results show that the proposed algorithm has certain practical effects and can provide theoretical reference for subsequent related research.

Data-Driven Extraction of Vehicle States From CAN Bus Traffic for Cyberprotection and Safety

This article develops a data -driven, semisupervised approach to learn physical relationships of controller area network (CAN) signals from only a limited set of CAN packets. These mappings are then used to develop a hidden Markov model (HMM) of the driver's actions upon which transaction analysis is performed to optimize the real-time identification of the states. The proposed approach builds an image from the CAN data, then trains a convolutional neural network (CNN) to give emission probabilities to predicts drivers's actions.