Detection of advanced persistent threat using machine-learning correlation analysis

As one of the most serious types of cyber attack, Advanced Persistent Threats (APT) have caused major concerns on a global scale. APT refers to a persistent, multi-stage attack with the intention to compromise the system and gain information from the targeted system, which has the potential to cause significant damage and substantial financial loss. The accurate detection and prediction of APT is an ongoing challenge. This work proposes a novel machine learning-based system entitled MLAPT, which can accurately and rapidly detect and predict APT attacks in a systematic way. The MLAPT runs through three main phases: (1) Threat detection, in which eight methods have been developed to detect different techniques used during the various APT steps. The implementation and validation of these methods with real traffic is a significant contribution to the current body of research; (2) Alert correlation, in which a correlation framework is designed to link the outputs of the detection methods, aims to identify alerts that could be related and belong to a single APT scenario; and (3) Attack prediction, in which a machine learning-based prediction module is proposed based on the correlation framework output, to be used by the network security team to determine the probability of the early alerts to develop a complete APT attack. MLAPT is experimentally evaluated and the presented sy

Past is Present: Settler Colonialism in Palestine

The editors introduce this special issue of settler colonial studies focusing on settler colonialism in Palestine

Enhancing heart disease prediction using a self-attention-based transformer model

Cardiovascular diseases (CVDs) continue to be the leading cause of more than 17 million mortalities worldwide. The early detection of heart failure with high accuracy is crucial for clinical trials and therapy. Patients will be categorized into various types of heart disease based on characteristics like blood pressure, cholesterol levels, heart rate, and other characteristics. With the use of an automatic system, we can provide early diagnoses for those who are prone to heart failure by analyzing their characteristics. In this work, we deploy a novel self-attention-based transformer model, that combines self-attention mechanisms and transformer networks to predict CVD risk. The self-attention layers capture contextual information and generate representations that effectively model complex patterns in the data. Self-attention mechanisms provide interpretability by giving each component of the input sequence a certain amount of attention weight. This includes adjusting the input and output layers, incorporating more layers, and modifying the attention processes to collect relevant information. This also makes it possible for physicians to comprehend which features of the data contributed to the model's predictions. The proposed model is tested on the Cleveland dataset, a benchmark dataset of the University of California Irvine (UCI) machine learning (ML) repository. Comparing the proposed model to several baseline approaches, we achieved the highest accuracy of 96.51%. Furthermore, the outcomes of our experiments demonstrate that the prediction rate of our model is higher than that of other cutting-edge approaches used for heart disease prediction

An Enhanced Nonlinear Companding Scheme for Reducing PAPR of OFDM Systems

A new companding scheme for reducing the peak-to-average power ratio (PAPR) of orthogonal frequency-division multiplexing (OFDM) systems is proposed in this study. It proceeds from speech signal processing similar to the earliest μ -law companding (MC) model. The proposed scheme compands (compresses and expands) the amplitudes of OFDM signals to a maximum of 1 V. Besides significantly reducing the PAPR, the proposed technique is also able to function as a limiter, thus reducing the system complexity and limiting the amplitudes of OFDM symbols to a unity maximum voltage, which does not exist in other companding PAPR techniques. Over frequency-selective fading channels with frequency domain equalization and using minimum mean square error (MMSE) to minimize the noise overhead, the proposed technique outperforms four other companding schemes over light and severe fading conditions. Finally, we demonstrate that PAPR reduction using companding can dispense with corresponding decompanding scheme at the receiver as it amplifies the distortion noise, thereby reducing the bit error ratio performance and increasing the receiver complexity. We investigate the out-of-band interference of the proposed scheme and also show that it outperforms the other existing techniques by up to 5 dB

Evaluation of Bond Strength Between Carbon Fiber Reinforced Polymer (CFRP) Composites with Modified Epoxy Resins and Concrete

Rehabilitation and strengthening of concrete structures are becoming more significant in civil engineering applications. The use of externally bonded Fiber Reinforced Polymers (FRP) is one of the methods to strengthen and rehabilitate reinforced concrete members, providing noticeable improvement to their capacity in resisting load. Carbon Fiber Reinforced Polymer (CFRP) is used along with epoxy resins to evaluate the bond strength of two commercially available epoxies (EPON 828 and EPON 862) between CFRP and concrete. In addition, three new combinations that resulted from mixing the two epoxies were examined. The mechanical properties of epoxy resins are significantly weaker than this of the CFRP making the epoxy characteristics the determining factor in the quality of the bond strength. Three-point flexural test was conducted to examine the bond strength between the CFRP composites and concrete. Further, differential scanning calorimetry was conducted to examine the glass transition temperature of the resultant epoxies. The results showed that the optimum composition was a mixture of 70% of epoxy 828 and 30% of epoxy 862. Therefore, achieving better bond strength and high glass transition temperature, resulting in CFRP composite with higher fire resistance

Low-Power Wide Area Network Technologies for Internet-of-Things: A Comparative Review

The rapid growth of Internet-of-Things (IoT) in the current decade has led to the the development of a multitude of new access technologies targeted at low-power, wide area networks (LP-WANs). However, this has also created another challenge pertaining to technology selection. This paper reviews the performance of LP-WAN technologies for IoT, including design choices and their implications. We consider Sigfox, LoRaWAN, WavIoT, random phase multiple access (RPMA), narrow band IoT (NB-IoT) as well as LTE-M and assess their performance in terms of signal propagation, coverage and energy conservation. The comparative analyses presented in this paper are based on available data sheets and simulation results. A sensitivity analysis is also conducted to evaluate network performance in response to variations in system design parameters. Results show that each of RPMA, NB-IoT and LTE-M incurs at least 9 dB additional path loss relative to Sigfox and LoRaWAN. This study further reveals that with a 10% improvement in receiver sensitivity, NB-IoT 882 MHz and LoRaWAN can increase coverage by up to 398% and 142% respectively, without adverse effects on the energy requirements. Finally, extreme weather conditions can significantly reduce the active network life of LP-WANs. In particular, the results indicate that operating an IoT device in a temperature of -20∘C can shorten its life by about half; 53% (WavIoT, LoRaWAN, Sigfox, NB-IoT, RPMA) and 48% in LTE-M compared with environmental temperature of 40C

Optimization of Impulsive Noise Mitigation Scheme for PAPR Reduced OFDM Signals Over Powerline Channels

The IEEE 1901 powerline standard can be deployed using orthogonal frequency division multiplexing (OFDM) since it is robust over impulsive channels. However, the powerline channel picks up impulsive interference that the conventional OFDM driver cannot combat. Since the probability density function (PDF) of OFDM amplitudes follow the Rayleigh distribution, it becomes difficult to correctly predict the existence of impulsive noise (IN) in powerline systems. In this study, we use companding transforms to convert the PDF of the conventional OFDM system to a uniform distribution which avails the identification and mitigation of IN. Results show significant improvement in the output signal-to-noise ratio (SNR) when nonlinear optimization search is applied. We also show that the conventional PDF leads to false IN detection which diminishes the output SNR when nonlinear memoryless mitigation scheme such as clipping or blanking is applied. Thus, companding OFDM signals before transmission helps to correctly predict the optimal blanking or clipping threshold which in turn improves the output SNR performance

Root-Based Nonlinear Companding Technique for Reducing PAPR of Precoded OFDM Signals

Orthogonal frequency division multiplexing (OFDM) signals are characteristically independent and identically distributed Gaussian random variables that follow Rayleigh distribution. The signals also exhibit high peak-toaverage power ratio (PAPR) problem due to the infinitesimal amplitude component distributed above the mean of the Rayleigh distribution plot. Since the amplitudes are nonlinearly and non-monotonically increasing, applying roots to the amplitude distribution is shown in this work to change the probability density function (PDF) and thus reduces the PAPR. We exemplify these by imposing this constraint on standard μ-law companding (MC) technique in reducing PAPR of OFDM signals which is known to expand the amplitudes of low power signals only without impacting the higher amplitude signals. This limits the PAPR reduction performance of the MC scheme. Since companding involves simultaneously compressing/expanding high/low amplitude OFDM signals respectively, in this study, we refer to the new method as a root-based MC (RMC) scheme that simultaneously expands and compresses OFDM signal amplitudes unlike MC. In addition, we express a second transform independent of the MC model. The results of the two proposed schemes outperform four other widely used companding techniques (MC, log-based modified (LMC), hyperbolic arc-sine companding (HASC) and exponential companding (EC)). Besides these, we precode the OFDM signals using discrete Hartley Transform (DHT) in order to further reduce the PAPR limits achieved by RMC by distorting the phase. While preserving the BER, DHT-precoded RMC outperforms all four other companding schemes (MC, EC, HASC, LMC) in terms of PAPR

Guest Editorial: Smart, optimal, and explainable orchestration of network slices in 5G and beyond networks

Network slicing is a much discussed topic in fifth generation (5G) and beyond (B5G) networks. The network slice feature differentiates 5G and B5G networks from the earlier generations since it replaces the conventional concept of quality of service (QoS) with end-to-end multi-service provisioning and multi-tenancy. A diverse set of resources for computing, networking, storage, and power need to be smartly assigned in network slices. Traditional optimization/resource scheduling techniques are typically one-dimensional and may not scale well in large-scale 5G/B5G networks. Therefore, there is a pressing need to smartly address the orchestration and management of network slices. Since beyond 5G networks will heavily use embedded intelligence, how to leverage AI-based techniques, such as machine learning, deep learning, and reinforcement learning, to address and solve the various complex network slicing problems is emerging as a challenging problem. The Guest Editors worked hard to reach out to researchers from academia and industry to address these points in this Special Issue in search of a genuinely intelligent B5G network rollout that could be both smart and practical

Solitons in magneto-optic waveguides with Kudryashov’s law nonlinear refractive index for coupled system of generalized nonlinear Schrödinger’s equation using modified extended mapping method

In this work, we investigate the optical solitons and other waves through magneto-optic waveguides with Kudryashov’s law of nonlinear refractive index in the presence of chromatic dispersion and Hamiltonian-type perturbation factors using the modified extended mapping approach. Many classifications of solutions are established like bright solitons, dark solitons, singular solitons, singular periodic wave solutions, exponential wave solutions, rational wave, solutions, Weierstrass elliptic doubly periodic solutions, and Jacobi elliptic function solutions. Some of the extracted solutions are described graphically to provide their physical understanding of the acquired solutions