A Cyber Kill Chain Approach for Detecting Advanced Persistent Threats

The number of cybersecurity incidents is on the rise despite significant investment in security measures.The existing conventional security approaches have demonstrated limited success against some of the more complex cyber-attacks.This is primarily due to the sophistication of the attacks and the availability of powerful tools.Interconnected devices such as the Internet of Things(IoT)are also increasing attack exposures due to the increase in vulnerabilities.Over the last few years,we have seen a trend moving towards embracing edge technologies to harness the power of IoT devices and 5G networks.Edge technology brings processing power closer to the network and brings many advantages,including reduced latency,while it can also introduce vulnerabilities that could be exploited.Smart cities are also dependent on technologies where everything is interconnected.This interconnectivity makes them highly vulnerable to cyber-attacks,especially by the Advanced Persistent Threat(APT),as these vulnerabilities are amplified by the need to integrate new technologies with legacy systems.Cybercriminals behind APT attacks have recently been targeting the IoT ecosystems,prevalent in many of these cities.In this paper,we used a publicly available dataset on Advanced Persistent Threats(APT)and developed a data-driven approach for detecting APT stages using the Cyber Kill Chain.APTs are highly sophisticated and targeted forms of attacks that can evade intrusion detection systems,resulting in one of the greatest current challenges facing security professionals.In this experiment,we used multiple machine learning classifiers,such as Naïve Bayes,Bayes Net,KNN,Random Forest and Support Vector Machine(SVM).We used Weka performance metrics to show the numeric results.The best performance result of 91.1%was obtained with the Naïve Bayes classifier.We hope our proposed solution will help security professionals to deal with APTs in a timely and effective manner.

Digital Forensics for Skulls Classification in Physical Anthropology Collection Management

The size,shape,and physical characteristics of the human skull are distinct when considering individual humans.In physical anthropology,the accurate management of skull collections is crucial for storing and maintaining collections in a cost-effective manner.For example,labeling skulls inaccurately or attaching printed labels to skulls can affect the authenticity of collections.Given the multiple issues associated with the manual identification of skulls,we propose an automatic human skull classification approach that uses a support vector machine and different feature extraction methods such as gray-level co-occurrence matrix features,Gabor features,fractal features,discrete wavelet transforms,and combinations of features.Each underlying facial bone exhibits unique characteristics essential to the face’s physical structure that could be exploited for identification.Therefore,we developed an automatic recognition method to classify human skulls for consistent identification compared with traditional classification approaches.Using our proposed approach,we were able to achieve an accuracy of 92.3–99.5%in the classification of human skulls with mandibles and an accuracy of 91.4–99.9%in the classification of human skills without mandibles.Our study represents a step forward in the construction of an effective automatic human skull identification system with a classification process that achieves satisfactory performance for a limited dataset of skull images.

Dynamic channel estimation-aware routing protocol in mobile cognitive radio networks for smart IIoT applications

Cognitive Radio Networks(CRNs)have become a successful platform in recent years for a diverse range of future systems,in particularly,industrial internet of things(IIoT)applications.In order to provide an efficient connection among IIoT devices,CRNs enhance spectrum utilization by using licensed spectrum.However,the routing protocol in these networks is considered one of the main problems due to node mobility and time-variant channel selection.Specifically,the channel selection for routing protocol is indispensable in CRNs to provide an adequate adaptation to the Primary User(PU)activity and create a robust routing path.This study aims to construct a robust routing path by minimizing PU interference and routing delay to maximize throughput within the IIoT domain.Thus,a generic routing framework from a cross-layer perspective is investigated that intends to share the information resources by exploiting a recently proposed method,namely,Channel Availability Probability.Moreover,a novel cross-layer-oriented routing protocol is proposed by using a time-variant channel estimation technique.This protocol combines lower layer(Physical layer and Data Link layer)sensing that is derived from the channel estimation model.Also,it periodically updates and stores the routing table for optimal route decision-making.Moreover,in order to achieve higher throughput and lower delay,a new routing metric is presented.To evaluate the performance of the proposed protocol,network simulations have been conducted and also compared to the widely used routing protocols,as a benchmark.The simulation results of different routing scenarios demonstrate that our proposed solution outperforms the existing protocols in terms of the standard network performance metrics involving packet delivery ratio(with an improved margin of around 5–20%approximately)under varying numbers of PUs and cognitive users in Mobile Cognitive Radio Networks(MCRNs).Moreover,the cross-layer routing protocol successfully achieves high routing performance in finding a robust route,selecting the high channel stability,and reducing the probability of PU interference for continued communication.

New bounds on the mutual information for discrete constellations and application to wireless channel estimation

The lack of closed-form expressions of the mutual information for discrete constellations has limited its uses for analyzing reliable communication over wireless fading channels.In order to address this issue,this paper proposes analytically-tractable lower bounds on the mutual information based on Arithmetic-Mean-Geometric-Mean(AMGM)inequality.The new bounds can apply to a wide range of discrete constellations and reveal some insights into the rate behavior at moderate to high Signal-to-Noise Ratio(SNR)values.The usability of the bounds is further demonstrated to approximate the optimum pilot overhead in stationary fading channels.