A Strategy for Detection and Mitigation of DoS Attacks on Software-Defined Networks

Computer networks support applications in virtually every area of application and knowledge, and as such, they have widely distributed structures and are susceptible to security attacks in general.Software-Defined Networks (SDN), in turn, are a technological solution that has several advantages by separating the control plane from the data plane in the structuring of computer networks. Given this technological difference, software-defined networks are a network implementation paradigm used to mitigate network security attacks. In summary, the use of SDN to mitigate network attacks provides greater flexibility in implementing the attack strategy. However, the separation of control and data planes creates new points of vulnerability for the security of the network operation.The denial of service attack (DoS) of the type Syn-Flooding is one of the most common possible attacks. It can cause, concerning the network, the commitment to perform services and, concerning the operation of the SDN, the commitment in the bandwidth of the communication channel between the control planes and the data plane, the saturation of the ow table in the switch, and the increasing of the processing load in the controller.In general, the investigation about new strategies aimed at safety with SDN becomes necessary to improve security strategies for network attacks and maximize the reliability of SDN operation, allowing use in different application scenarios. This work presents a defense strategy against attacks of DoS Syn-Flooding using the SDN facilities of an integrated controller with an intrusion detection system (IDS).The proposed strategy aims to mitigate Syn-Flooding DoS attacks and the vulnerability arising from the use of SDN to mitigate attacks.

Asset Management Method of Industrial IoT Systems for Cyber-Security Countermeasures

Cyber-security countermeasures are important for IIoT (industrial Internet of things) systems in which IT (information technology) and OT (operational technology) are integrated. The appropriate asset management is the key to creating strong security systems to protect from various cyber threats. However, the timely and coherent asset management methods used for conventional IT systems are difficult to be implemented for IIoT systems. This is because these systems are composed of various network protocols, various devices, and open technologies. Besides, it is necessary to guarantee reliable and real-time control and save CPU and memory usage for legacy OT devices. In this study, therefore, (1) we model various asset configurations for IIoT systems and design a data structure based on SCAP (Security Content Automation Protocol). (2) We design the functions to automatically acquire the detailed information from edge devices by “asset configuration management agent”, which ensures a low processing load. (3) We implement the proposed asset management system to real edge devices and evaluate the functions. Our contribution is to automate the asset management method that is valid for the cyber security countermeasures in the IIoT systems.

A Framework for Managing Device Association and Offloading the Transport Layer’s Security Overhead of WiFi Device to Access Points

Considering the resource constraints of Internet of Things (IoT) stations, establishing secure communication between stations and remote servers imposes a significant overhead on these stations in terms of energy cost and processing load. This overhead, in particular, is considerable in networks providing high communication rates and frequent data exchange, such as those relying on the IEEE 802.11 (WiFi) standard. This paper proposes a framework for offloading the processing overhead of secure communication protocols to WiFi access points (APs) in deployments where multiple APs exist. Within this framework, the main problem is finding the AP with sufficient computation and communication capacities to ensure secure and efficient transmissions for the stations associated with that AP. Based on the data-driven profiles obtained from empirical measurements, the proposed framework offloads most heavy security computations from the stations to the APs. We model the association problem as an optimization process with a multi-objective function. The goal is to achieve maximum network throughput via the minimum number of APs while satisfying the security requirements and the APs’ computation and communication capacities. The optimization problem is solved using genetic algorithms (GAs) with constraints extracted from a physical testbed. Experimental results demonstrate the practicality and feasibility of our comprehensive framework in terms of task and energy efficiency as well as security.

Design and Implementation of a LoRa Communication System Supporting Edge Computing on the Smart Multi-Platform IoT Gateway

LoRa technology was developed over 10 years ago, with many communication protocols optimized for LoRaWAN. However, in the protocols, all data from the end devices are sent directly or forwarded through a gateway to the LoRaWAN server and processed centrally there. Accordingly, the gateway only acts as a forwarder. This mechanism increases the processing load on the server, increases latency, and is not suitable for applications with a large number of end devices or that require real-time applications. In this paper, we design and develop a new LoRa communication protocol that supports edge computing at the gateway. At the same time, the authors design and manufacture a Smart Multiplatform IoT Gateway (SMGW) and LoRa nodes that allow the implementation and evaluation of the proposed protocol in practice. The test results on a system of 50 LoRa nodes and the SMGW show that the proposed protocol works well when evaluating its performance in terms of reliability, latency, and power consumption. This proposed system is suitable for applications that require edge computing and is easily extendable to other IoT applications.

Programmable Data Planes meets In-Network Computing: A Review of the State of the Art and Prospective Directions

Improving network traffic in networks is one of the concerns between networking researchers and network operators since the architecture of modern networks still faces challenges to process large data traffic without the cost of consuming a significant amount of resources not related to computing specifically. On the other hand, network programmability has enabled the development of new applications and network services, from software-defined networking to domain-specific languages created to program network devices and specify their behavior. The development of programmable hardware and hardware accelerators like FPGAs, GPUs, and CPUs help this new paradigm go one step further. Use the artifact of programmability of these devices to solve problems, such as improve the processing of data traffic is the key of in-network computing. It offers the opportunity to execute programs typically running on end-hosts within programmable network devices already incorporated on the network, thus being capable of provides a reduction on the in-network processing load and requires no extra cost, since operations can be concluded using a fewer amount of devices of the network and no extra device are needed. In this paper, we survey in-network computing, as well as we suggest classifying related works to in-network computing according to the hardware accelerator used. Also, we discuss challenges and research directions.

More than words: word predictability, prosody, gesture and mouth movements in natural language comprehension

The ecology of human language is face-to-face interaction, comprising cues such as prosody, co-speech gestures and mouth movements. Yet, the multimodal context is usually stripped away in experiments as dominant paradigms focus on linguistic processing only. In two studies we presented video-clips of an actress producing naturalistic passages to participants while recording their electroencephalogram. We quantified multimodal cues (prosody, gestures, mouth movements) and measured their effect on a well-established electroencephalographic marker of processing load in comprehension (N400). We found that brain responses to words were affected by informativeness of co-occurring multimodal cues, indicating that comprehension relies on linguistic and non-linguistic cues. Moreover, they were affected by interactions between the multimodal cues, indicating that the impact of each cue dynamically changes based on the informativeness of other cues. Thus, results show that multimodal cues are integral to comprehension, hence, our theories must move beyond the limited focus on speech and linguistic processing.

Non-Contact Measurement of Motion Sickness Using Pupillary Rhythms from an Infrared Camera

Both physiological and neurological mechanisms are reflected in pupillary rhythms via neural pathways between the brain and pupil nerves. This study aims to interpret the phenomenon of motion sickness such as fatigue, anxiety, nausea and disorientation using these mechanisms and to develop an advanced non-contact measurement method from an infrared webcam. Twenty-four volunteers (12 females) experienced virtual reality content through both two-dimensional and head-mounted device interpretations. An irregular pattern of the pupillary rhythms, demonstrated by an increasing mean and standard deviation of pupil diameter and decreasing pupillary rhythm coherence ratio, was revealed after the participants experienced motion sickness. The motion sickness was induced while watching the head-mounted device as compared to the two-dimensional virtual reality, with the motion sickness strongly related to the visual information processing load. In addition, the proposed method was verified using a new experimental dataset for 23 participants (11 females), with a classification performance of 89.6% (n = 48) and 80.4% (n = 46) for training and test sets using a support vector machine with a radial basis function kernel, respectively. The proposed method was proven to be capable of quantitatively measuring and monitoring motion sickness in real-time in a simple, economical and contactless manner using an infrared camera.

Word processing difficulty and executive control interactively shape comprehension monitoring in a second language: an eye-tracking study

Successful reading comprehension – especially in a second language (L2) – relies on the ability to monitor one’s comprehension, that is, to notice comprehension breaks and make repairs. Comprehension monitoring may be limited given effortful word processing, but may also be supported through active control. The current study addresses to what extent increased word processing difficulty reduces adolescents’ ability to monitor their comprehension when reading in their L2, and whether readers can compensate limitations given sufficient executive control. To this end, we conducted an eye-tracking experiment where 34 adolescent L2 learners read short expository texts that contained two within-subject manipulations. First, comprehension monitoring was tested through textual inconsistencies, such as when the topic changed from speaking Spanish to speaking Russian vis-à-vis consistent controls. Second, word processing difficulty was altered by inserting either shorter and higher-frequency words such as want, or longer and lower-frequency words such as prefer. We additionally measured each participants’ executive control. We found evidence of successful moment-to-moment monitoring in the L2, as visible in adolescents’ increased rereading of inconsistent as opposed to consistent information. We also found that adolescents adapted their monitoring differently to word processing difficulty, depending on their executive control: while adolescents with weaker control abilities reduced their monitoring given higher word processing difficulty, adolescents with stronger control abilities monitored their comprehension more (instead of less) on difficult texts. These findings provide insights into how comprehension monitoring in the L2 arises in the interplay of limitations due to lower-level processing load and compensation thanks to active control.

Signal-piloted processing and machine learning based efficient power quality disturbances recognition

Significant losses can occur for various smart grid stake holders due to the Power Quality Disturbances (PQDs). Therefore, it is necessary to correctly recognize and timely mitigate the PQDs. In this context, an emerging trend is the development of machine learning assisted PQDs management. Based on the conventional processing theory, the existing PQDs identification is time-invariant. It can result in a huge amount of unnecessary information being collected, processed, and transmitted. Consequently, needless processing activities, power consumption and latency can occur. In this paper, a novel combination of signal-piloted acquisition, adaptive-rate segmentation and time-domain features extraction with machine learning tools is suggested. The signal-piloted acquisition and processing brings real-time compression. Therefore, a remarkable reduction can be secured in the data storage, processing and transmission requirement towards the post classifier. Additionally, a reduced computational cost and latency of classifier is promised. The classification is accomplished by using robust machine learning algorithms. A comparison is made among the k-Nearest Neighbor, Naïve Bayes, Artificial Neural Network and Support Vector Machine. Multiple metrics are used to test the success of classification. It permits to avoid any biasness of findings. The applicability of the suggested approach is studied for automated recognition of the power signal’s major voltage and transient disturbances. Results show that the system attains a 6.75-fold reduction in the collected information and the processing load and secures the 98.05% accuracy of classification.

Attentional brain rhythms during prolonged cognitive activity

As routine and lower demand cognitive tasks are taken over by automated assistive systems, human operators are increasingly required to sustain cognitive demand over long periods of time. This has been reported to have long term adverse effects on cardiovascular and mental health. However, it remains unclear whether prolonged cognitive activity results in a monotonic decrease in the efficiency of the recruited brain processes, or whether the brain is able to sustain functions over time spans of one hour and more. Here, we show that during working sessions of one hour or more, contrary to the prediction of a monotonic decline, behavioral performance in both humans and non-human primates consistently fluctuates between periods of optimal and suboptimal performance at a very slow rhythm of circa 5 cycles per hour. These fluctuations are observed in both high attentional (in non-human primates) and low attentional (in humans) demand conditions. They coincide with fluctuations in pupil diameter, indicating underlying changes in arousal and information-processing load. Accordingly, we show that these rhythmic behavioral fluctuations correlate, at the neurophysiological level, with fluctuations in the informational attention orientation and perception processing capacity of prefrontal neuronal populations. We further identify specific markers of these fluctuations in LFP power, LFP coherence and spike-field coherence, pointing towards long-range rhythmic modulatory inputs to the prefrontal cortex rather than a local prefrontal origin. These results shed light on the resilience of brain mechanisms to sustained effort and have direct implications on how to optimize high cognitive demand working and learning environments.