Mitigating Denial of Service Attacks with Load Balancing

Denial of service (DoS) attack continues to pose a huge risk to online businesses. The attack has moved from attack at the network level – layer 3 and layer 4 to the layer 7 of the OSI model. This layer 7 attack or application layer attack is not easily detectable by firewalls and most intrusion Detection systems and other security tools but have the capability of bringing down a well-equipped web server. The wide availability and easy accessibility of the attack tools makes this type of security risk very easy to execute, very prolific and difficult to completely mitigate. There have been an increasing number of such attacks against the web server infrastructures of many organisations being recorded. The aim of this research is to look at some layer 7 application DDoS attack tools and test open source tools that offer some form of defense against these attacks. The research deployed open source load balancing software, HAProxy as a first line of defense against Denial of Service attack. The three components of the popular free open source data analysis tool, Elastic stack framework- Logstash, Elasticsearch and Kibana were used to collect logs from the web server, filter and query the logs and then display results in dashboards and graphs to help in the identification of an attack by analysing the visually displayed log data. Rules are also setup to alert the business of anomalies detected based on pre-determined benchmarks

A Brief Exposition on Brain-Computer Interface

Brain-Computer Interface is a technology that records brain signals and translates them into useful commands to operate a drone or a wheelchair. Drones are used in various applications such as aerial operations, where pilot’s presence is impossible. The BCI can also be used for patients suffering from brain diseases who lose their body control and are unable to move to satisfy their basic needs. By taking advantage of BCI and drone technology, algorithms for Mind-Controlled Unmanned Aerial System can be developed. This paper deals with the classification of BCI & UAV, methodologies of BCI, the framework of BCI, neuro-imaging methods, BCI headset options, BCI platforms, electrode types & their placement, and the result of feature extraction technique (FFT) with 72.5% accuracy

Modeling and Link Performance Analysis of Busbar Distribution Systems for Narrowband PLC

Busbar distribution system is used as a modular infrastructure to carry electrical energy in low voltage grid. Due to the widespread usage in industrial areas, the power line communication possibilities should be investigated in terms of smart grid concept. This paper addresses modeling of the busbar distribution system as a transmission line and gives some suggestions on the link performance for narrowband power line communication for the first time in literature. Firstly, S-parameters of different current level busbars were measured up to 500 kHz for all possible two-port signal paths. The utilization of the frequency-dependent model was proposed to extract transmission line characteristics to eliminate the unwanted measurement effects. Particle swarm algorithm was used to optimize the model parameters with a good agreement between measured and simulated S-parameters. Additionally, link performance of busbar distribution system as a power line communication channel at 3 kHz-148.5 kHz band was examined for frequency shift keying and phase shift keying modulations under different network configurations such as varying busbar type, the line length between transmitter and receiver, branch number, and terminating load impedance. Obtained results were presented as bit-error-rate vs. signal to noise ratio graphs

DLPFA: Deep Learning based Persistent Fault Analysis against Block Ciphers

Deep learning techniques have been widely applied to side-channel analysis (SCA) in recent years and shown better performance compared with traditional methods. However, there has been little research dealing with deep learning techniques in fault analysis to date. This article undertakes the first study to introduce deep learning techniques into fault analysis to perform key recovery. We investigate the application of multi-layer perceptron (MLP) and convolutional neural network (CNN) in persistent fault analysis (PFA) and propose deep learning-based persistent fault analysis (DLPFA). DLPFA is first applied to advanced encryption standard (AES) to verify its availability. Then, to push the study further, we extend DLPFA to PRESENT, which is a lightweight substitution–permutation network (SPN)-based block cipher. The experimental results show that DLPFA can handle random faults and provide outstanding performance with a suitable selection of hyper-parameters

Optical Space Division Multiplexing in Short Reach Multi-Mode Fiber Systems

The application of space division multiplexing to fiber-optic communications is a promising approach to further increase the channel capacity of optical waveguides. In this work, short reach and low-cost optical space division multiplexing systems with intensity modulation and direct detection (IM/DD) are in the focus of interest. Herein, different modes are utilized to generate spatial diversity in a multi-mode fiber. In such IM/DD systems, the process of square-law detection is inherently non-linear. In order to obtain an understanding of the channel characteristics, a system model is developed, which is able to show under which conditions the system can be considered linear in baseband. It is shown that linearity applies in scenarios with low mode cross-talk. This enables the use of linear multiple-input multiple-output (MIMO) signal processing strategies for equalization purposes. In conditions with high mode cross-talk, significant interference occurs, and the transmitted information cannot be extracted at the receiver. Furthermore, a method to determine the power coupling coefficients between mode groups is presented that does not require the excitation of individual modes, and hence it can be realized with inexpensive components. In addition, different optical components are analyzed with respect for their suitability in MIMO setups with IM/DD. The conventional approach with single-mode fiber to multi-mode fiber offset launches and optical couplers as well as a configuration that utilizes multi-segment detection are feasible options for a (2x2) setup. It is further shown that conventional photonic lanterns are not suited for MIMO with IM/DD due to their low mode orthogonality during the multiplexing process. In order to enable higher order MIMO configurations, devices for mode multiplexing and demultiplexing need to be developed, which exhibit a high mode orthogonality on one hand and are low-cost on the other hand

Passive RFID-Based Inventory of Traffic Signs on Roads and Urban Environments

This paper presents a system with location functionalities for the inventory of traffic signs based on passive RFID technology. The proposed system simplifies the current video-based techniques, whose requirements regarding visibility are difficult to meet in some scenarios, such as dense urban areas. In addition, the system can be easily extended to consider any other street facilities, such as dumpsters or traffic lights. Furthermore, the system can perform the inventory process at night and at a vehicle’s usual speed, thus avoiding interfering with the normal traffic flow of the road. Moreover, the proposed system exploits the benefits of the passive RFID technologies over active RFID, which are typically employed on inventory and vehicular routing applications. Since the performance of passive RFID is not obvious for the required distance ranges on these in-motion scenarios, this paper, as its main contribution, addresses the problem in two different ways, on the one hand theoretically, presenting a radio wave propagation model at theoretical and simulation level for these scenarios; and on the other hand experimentally, comparing passive and active RFID alternatives regarding costs, power consumption, distance ranges, collision problems, and ease of reconfiguration. Finally, the performance of the proposed on-board system is experimentally validated, testing its capabilities for inventory purposesMinisterio de Economía y Competitividad TEC2016-80396-C2-2-

Don’t Learn What You Already Know

Over the past few years, deep-learning-based attacks have emerged as a de facto standard, thanks to their ability to break implementations of cryptographic primitives without pre-processing, even against widely used counter-measures such as hiding and masking. However, the recent works of Bronchain and Standaert at Tches 2020 questioned the soundness of such tools if used in an uninformed setting to evaluate implementations protected with higher-order masking. On the opposite, worst-case evaluations may be seen as possibly far from what a real-world adversary could do, thereby leading to too conservative security bounds. In this paper, we propose a new threat model that we name scheme-aware benefiting from a trade-off between uninformed and worst-case models. Our scheme-aware model is closer to a real-world adversary, in the sense that it does not need to have access to the random nonces used by masking during the profiling phase like in a worst-case model, while it does not need to learn the masking scheme as implicitly done by an uninformed adversary. We show how to combine the power of deep learning with the prior knowledge of scheme-aware modeling. As a result, we show on simulations and experiments on public datasets how it sometimes allows to reduce by an order of magnitude the profiling complexity, i.e., the number of profiling traces needed to satisfyingly train a model, compared to a fully uninformed adversary

Emotion AI-Driven Sentiment Analysis: A Survey, Future Research Directions, and Open Issues

The essential use of natural language processing is to analyze the sentiment of the author via the context. This sentiment analysis (SA) is said to determine the exactness of the underlying emotion in the context. It has been used in several subject areas such as stock market prediction, social media data on product reviews, psychology, judiciary, forecasting, disease prediction, agriculture, etc. Many researchers have worked on these areas and have produced significant results. These outcomes are beneficial in their respective fields, as they help to understand the overall summary in a short time. Furthermore, SA helps in understanding actual feedback shared across di erent platforms such as Amazon, TripAdvisor, etc. The main objective of this thorough survey was to analyze some of the essential studies done so far and to provide an overview of SA models in the area of emotion AI-driven SA. In addition, this paper o ers a review of ontology-based SA and lexicon-based SA along with machine learning models that are used to analyze the sentiment of the given context. Furthermore, this work also discusses di erent neural network-based approaches for analyzing sentiment. Finally, these di erent approaches were also analyzed with sample data collected from Twitter. Among the four approaches considered in each domain, the aspect-based ontology method produced 83% accuracy among the ontology-based SAs, the term frequency approach produced 85% accuracy in the lexicon-based analysis, and the support vector machine-based approach achieved 90% accuracy among the other machine learning-based approaches.Ministerio de Educación (MOE) en Taiwán N/

Speech Activity and Speaker Change Point Detection for Online Streams

Disertační práce je věnována dvěma si blízkým řečovým úlohám a následně jejich použití v online prostředí. Konkrétně se jedná o úlohy detekce řeči a detekce změny mluvčího. Ty jsou často nedílnou součástí systémů pro zpracování řeči (např. pro diarizaci mluvčích nebo rozpoznávání řeči), kde slouží pro předzpracování akustického signálu. Obě úlohy jsou v literatuře velmi aktivním tématem, ale většina existujících prací je směřována primárně na offline využití. Nicméně právě online nasazení je nezbytné pro některé řečové aplikace, které musí fungovat v reálném čase (např. monitorovací systémy).Úvodní část disertační práce je tvořena třemi kapitolami. V té první jsou vysvětleny základní pojmy a následně je nastíněno využití obou úloh. Druhá kapitola je věnována současnému poznání a je doplněna o přehled existujících nástrojů. Poslední kapitola se skládá z motivace a z praktického použití zmíněných úloh v monitorovacích systémech. V závěru úvodní části jsou stanoveny cíle práce.Následující dvě kapitoly jsou věnovány teoretickým základům obou úloh. Představují vybrané přístupy, které jsou buď relevantní pro disertační práci (porovnání výsledků), nebo jsou zaměřené na použití v online prostředí.V další kapitole je předložen finální přístup pro detekci řeči. Postupný návrh tohoto přístupu, společně s experimentálním vyhodnocením, je zde detailně rozebrán. Přístup dosahuje nejlepších výsledků na korpusu QUT-NOISE-TIMIT v podmínkách s nízkým a středním zašuměním. Přístup je také začleněn do monitorovacího systému, kde doplňuje svojí funkcionalitou rozpoznávač řeči.Následující kapitola detailně představuje finální přístup pro detekci změny mluvčího. Ten byl navržen v rámci několika po sobě jdoucích experimentů, které tato kapitola také přibližuje. Výsledky získané na databázi COST278 se blíží výsledkům, kterých dosáhl referenční offline systém, ale předložený přístup jich docílil v online módu a to s nízkou latencí.Výstupy disertační práce jsou shrnuty v závěrečné kapitole.The main focus of this thesis lies on two closely interrelated tasks, speech activity detection and speaker change point detection, and their applications in online processing. These tasks commonly play a crucial role of speech preprocessors utilized in speech-processing applications, such as automatic speech recognition or speaker diarization. While their use in offline systems is extensively covered in literature, the number of published works focusing on online use is limited.This is unfortunate, as many speech-processing applications (e.g., monitoring systems) are required to be run in real time.The thesis begins with a three-chapter opening part, where the first introductory chapter explains the basic concepts and outlines the practical use of both tasks. It is followed by a chapter, which reviews the current state of the art and lists the existing toolkits. That part is concluded by a chapter explaining the motivation behind this work and the practical use in monitoring systems; ultimately, this chapter sets the main goals of this thesis.The next two chapters cover the theoretical background of both tasks. They present selected approaches relevant to this work (e.g., used for result comparisons) or focused on online processing.The following chapter proposes the final speech activity detection approach for online use. Within this chapter, a detailed description of the development of this approach is available as well as its thorough experimental evaluation. This approach yields state-of-the-art results under low- and medium-noise conditions on the standardized QUT-NOISE-TIMIT corpus. It is also integrated into a monitoring system, where it supplements a speech recognition system.The final speaker change point detection approach is proposed in the following chapter. It was designed in a series of consecutive experiments, which are extensively detailed in this chapter. An experimental evaluation of this approach on the COST278 database shows the performance of approaching the offline reference system while operating in online mode with low latency.Finally, the last chapter summarizes all the results of this thesis