Analysing the Nigerian Teacher’s Readiness for Technology Integration

Performance Evaluation of the IEEE 802.16 ARQ Mechanism

The IEEE 802.16 technology defines the ARQ mechanism that enables a connection to resend data at the MAC level if an error is detected. In this paper, we analyze the key features and parameters of the ARQ mechanism. In particular, we consider a choice for the ARQ feedback type, a scheduling of the ARQ feedbacks and retransmissions, the ARQ block rearrangement, ARQ transmission window and ARQ block size. We run a number of simulation scenarios to study these parameters and how they impact a performance of application protocols. The simulation results reveal that the ARQ mechanism plays an important role in transmitting data over wireless channels in the IEEE 802.16 networks.

On Detection of Network-Based Co-residence Verification Attacks in SDN-Driven Clouds

Modern cloud environments allow users to consume computational and storage resources in the form of virtual machines. Even though machines running on the same cloud server are logically isolated from each other, a malicious customer can create various side channels to obtain sensitive information from co-located machines. In this study, we concentrate on timely detection of intentional co-residence attempts in cloud environments that utilize software-defined networking. SDN enables global visibility of the network state which allows the cloud provider to monitor and extract necessary information from each flow in every virtual network in online mode. We analyze the extracted statistics on d…

Online anomaly detection using dimensionality reduction techniques for HTTP log analysis

Modern web services face an increasing number of new threats. Logs are collected from almost all web servers, and for this reason analyzing them is beneficial when trying to prevent intrusions. Intrusive behavior often differs from the normal web traffic. This paper proposes a framework to find abnormal behavior from these logs. We compare random projection, principal component analysis and diffusion map for anomaly detection. In addition, the framework has online capabilities. The first two methods have intuitive extensions while diffusion map uses the Nyström extension. This fast out-of-sample extension enables real-time analysis of web server traffic. The framework is demonstrated using …

Data Stream Clustering for Application-Layer DDoS Detection in Encrypted Traffic

Application-layer distributed denial-of-service attacks have become a serious threat to modern high-speed computer networks and systems. Unlike network-layer attacks, application-layer attacks can be performed using legitimate requests from legitimately connected network machines that make these attacks undetectable by signature-based intrusion detection systems. Moreover, the attacks may utilize protocols that encrypt the data of network connections in the application layer, making it even harder to detect an attacker’s activity without decrypting users’ network traffic, and therefore violating their privacy. In this paper, we present a method that allows us to detect various application-l…

An Intrusion Detection System for Fog Computing and IoT based Logistic Systems using a Smart Data Approach

The Internet of Things (IoT) is widely used in advanced logistic systems. Safety and security of such systems are utmost important to guarantee the quality of their services. However, such systems are vulnerable to cyber-attacks. Development of lightweight anomaly based intrusion detection systems (IDS) is one of the key measures to tackle this problem. In this paper, we present a new distributed and lightweight IDS based on an Artificial Immune System (AIS). The IDS is distributed in a three-layered IoT structure including the cloud, fog and edge layers. In the cloud layer, the IDS clusters primary network traffic and trains its detectors. In the fog layer, we take advantage of a smart dat…

Cell degradation detection based on an inter-cell approach

Fault management is a crucial part of cellular network management systems. The status of the base stations is usually monitored by well-defined key performance indicators (KPIs). The approaches for cell degradation detection are based on either intra-cell or inter-cell analysis of the KPIs. In intra-cell analysis, KPI profiles are built based on their local history data whereas in inter-cell analysis, KPIs of one cell are compared with the corresponding KPIs of the other cells. In this work, we argue in favor of the inter-cell approach and apply a degradation detection method that is able to detect a sleeping cell that could be difficult to observe using traditional intra-cell methods. We d…

Towards proactive context-aware self-healing for 5G networks

In this paper, we suggest a new research direction and a future vision for Self-Healing (SH) in Self-Organizing Networks (SONs). The problem we wish to solve is that traditional SH solutions may not be sufficient for the future needs of cellular network management because of their reactive nature, i.e., they start recovering after detecting already occurred faults instead of preparing for possible future faults in a pre-emptive manner. The detection delays are especially problematic with regard to the zero latency requirements of 5G networks. To address this problem, existing SONs need to be upgraded from reactive to proactive response. One of the dimensions in SH research is to employ more…

Weighted Fuzzy Clustering for Online Detection of Application DDoS Attacks in Encrypted Network Traffic

Distributed denial-of-service (DDoS) attacks are one of the most serious threats to today’s high-speed networks. These attacks can quickly incapacitate a targeted business, costing victims millions of dollars in lost revenue and productivity. In this paper, we present a novel method which allows us to timely detect application-layer DDoS attacks that utilize encrypted protocols by applying an anomaly-based approach to statistics extracted from network packets. The method involves construction of a model of normal user behavior with the help of weighted fuzzy clustering. The construction algorithm is self-adaptive and allows one to update the model every time when a new portion of network tr…

Proxy Mobile IPv6-Based Seamless Handover

A prospective next generation wireless network is expected to integrate harmoniously into an IP-based core network. It is widely anticipated that IP-layer handover is a feasible solution to global mobility. However, the performance of IP-layer handover based on basic Mobile IP (MIP) cannot support real time services very well due to long handover delay. The Internet Engineering Task Force (IETF) Network-based Localized Mobility Management (NETLMM) working group developed a network-based localized mobility management protocol called Proxy Mobile IPv6 (PMIPv6) to reduce the handoff latency of MIPv6. Moreover, PMIPv6 provides the IP with the mobility to support User Equipments (UEs) without it…

On optimal deployment of low power nodes for high frequency next generation wireless systems

Recent development of wireless communication systems and standards is characterized by constant increase of allocated spectrum resources. Since lower frequency ranges cannot provide sufficient amount of bandwidth, new bands are allocated at higher frequencies, for which operators resort to deploy more base stations to ensure the same coverage and to utilize more efficiently higher frequencies spectrum. Striving for deployment flexibility, mobile operators can consider deploying low power nodes that could be either small cells connected via the wired backhaul or relays that utilize the same spectrum and the wireless access technology. However, even though low power nodes provide a greater fl…

Cross-Subject Emotion Recognition Using Fused Entropy Features of EEG.

Emotion recognition based on electroencephalography (EEG) has attracted high interest in fields such as health care, user experience evaluation, and human–computer interaction (HCI), as it plays an important role in human daily life. Although various approaches have been proposed to detect emotion states in previous studies, there is still a need to further study the dynamic changes of EEG in different emotions to detect emotion states accurately. Entropy-based features have been proved to be effective in mining the complexity information in EEG in many areas. However, different entropy features vary in revealing the implicit information of EEG. To improve system reliability, in this paper,…

Ensuring the QoS requirements in 802.16 scheduling

IEEE 802.16 standard defines the wireless broadband access network technology called WiMAX. WiMAX introduces several interesting advantages, and one of them is the support for QoS at the MAC level. For these purposes, the base station must allocate slots based on some algorithm. We propose a simple, yet efficient, solution for the WiMAX base station that is capable of allocating slots based on the QoS requirements, bandwidth request sizes, and the WiMAX network parameters. To test the proposed solution, we have implemented the WiMAX MAC layer in the NS-2 simulator. Several simulation scenarios are presented that demonstrate how the scheduling solution allocates resources in various cases. S…

Compression Methods for Microclimate Data Based on Linear Approximation of Sensor Data

Edge computing is currently one of the main research topics in the field of Internet of Things. Edge computing requires lightweight and computationally simple algorithms for sensor data analytics. Sensing edge devices are often battery powered and have a wireless connection. In designing edge devices the energy efficiency needs to be taken into account. Pre-processing the data locally in the edge device reduces the amount of data and thus decreases the energy consumption of wireless data transmission. Sensor data compression algorithms presented in this paper are mainly based on data linearity. Microclimate data is near linear in short time window and thus simple linear approximation based …

Assessment of Deep Learning Methodology for Self-Organizing 5G Networks

In this paper, we present an auto-encoder-based machine learning framework for self organizing networks (SON). Traditional machine learning approaches, for example, K Nearest Neighbor, lack the ability to be precisely predictive. Therefore, they can not be extended for sequential data in the true sense because they require a batch of data to be trained on. In this work, we explore artificial neural network-based approaches like the autoencoders (AE) and propose a framework. The proposed framework provides an advantage over traditional machine learning approaches in terms of accuracy and the capability to be extended with other methods. The paper provides an assessment of the application of …

Internet of Things for Sustainable Smart Education: An Overview

In the realm of fourth-generation industrialization, there will be great demand for a skilled workforceTo produce a skilled workforce, we need sustainable education with quality and equity. Conventional ways of delivering and managing education would not fulfil the demands of the fourth industrial revolution (4IR). Disruptive technologies, such as Internet of Things (IoT), have great potential in revolutionizing the current educational setup. Therefore, this research work aims to present an overview of the capabilities of IoT applications in educational settings. Our research article digs into recent research carried out referring to IoT applications in education and provides a detailed ins…

Comparison and analysis of the revenue-based adaptive queuing models

This paper presents several adaptive resource sharing models that use a revenue criterion to allocate bandwidth in an optimal way. The models ensure QoS requirements of data flows and, at the same time, maximize the total revenue by adjusting parameters of the underlying schedulers. Besides, the adaptive models eliminate the need to find the optimal static weight values because they are calculated dynamically. The simulation consists of several cases that analyse the models and the way they provide the required QoS guarantees. The simulation reveals that the installation of the adaptive model increases the total revenue and ensures the QoS requirements for all service classes. The paper als…

Leveraging National Auditing Criteria to Implement Cybersecurity Safeguards for the Automotive Emergency Response Vehicles : A case study from Finland

A modern Emergency Response Vehicle (ERV) is a combination of emergency services and functional mobile office on the wheels. The mobile office is aiming to leverage the benefits of fixed office while moving on the wheels. Researchers have observed that emergency response personnel including Law Enforcement Authorities (LEAs), Police and border guards, could be on the duty while having possibility to use same services compared to fixed office. On the one hand, demand of mobile office has significantly improved the emergency response services. On the other hand, emergency vehicle designers should rethink the demand of users. This resulted into modern standard emergency response vehicle with t…

Factors affecting Nigerian teacher educators’ technology integration : Considering characteristics, knowledge constructs, ICT practices and beliefs

To provide a diverse comprehension of teachers' TPACK (Technological, Pedagogical, and Content Knowledge) and how TPACK is reflected in practice, this study examined teacher educators' (TEs') conceptions of technology integration. Specifically, the main objective of the study was to investigate the factors influencing Nigerian teacher educators' technology integration using a self-completion survey administered to Nigerian teacher educators from three schools in the southern region of Nigeria. We utilized the partial least squares structural equation modeling (PLS-SEM) approach for the data analysis. Two frameworks—TPACK and Second Information Technology in Education Study (SITES)— guided t…

Reputation-Based Blockchain for Spatial Crowdsourcing in Vehicular Networks

The sharing of high-quality traffic information plays a crucial role in enhancing the driving experience and safety performance for vehicular networks, especially in the development of electric vehicles (EVs). The crowdsourcing-based real-time navigation of charging piles is characterized by low delay and high accuracy. However, due to the lack of an effective incentive mechanism and the resource-consuming bottleneck of sharing real-time road conditions, methods to recruit or motivate more EVs to provide high-quality information gathering has attracted considerable interest. In this paper, we first introduce a blockchain platform, where EVs act as the blockchain nodes, and a reputation-base…

Optimal Relays Deployment for 802.16j Networks

In this paper, we consider optimal relay station deployment for the IEEE 802.16j networks. IEEE 802.16j is an emerging wireless broadband networking standard that integrates infrastructure base stations with multihop relay technology. The proposed relay deployment mechanism allows us to maximize network capacity for every user or to maximize total network capacity, and, therefore, to reach greater network capacity values while employing smaller number of relay stations. With the proposed approach, the necessary number of relays for a region can be found.

ISAdetect

Static and dynamic binary analysis techniques are actively used to reverse engineer software's behavior and to detect its vulnerabilities, even when only the binary code is available for analysis. To avoid analysis errors due to misreading op-codes for a wrong CPU architecture, these analysis tools must precisely identify the Instruction Set Architecture (ISA) of the object code under analysis. The variety of CPU architectures that modern security and reverse engineering tools must support is ever increasing due to massive proliferation of IoT devices and the diversity of firmware and malware targeting those devices. Recent studies concluded that falsely identifying the binary code's ISA ca…

A Novel Method for Detecting APT Attacks by Using OODA Loop and Black Swan Theory

Advanced Persistent Threat(APT) attacks are a major concern for the modern societal digital infrastructures due to their highly sophisticated nature. The purpose of these attacks varies from long period espionage in high level environment to causing maximal destruction for targeted cyber environment. Attackers are skilful and well funded by governments in many cases. Due to sophisticated methods it is highly important to study proper countermeasures to detect these attacks as early as possible. Current detection methods under-performs causing situations where an attack can continue months or even years in a targeted environment. We propose a novel method for analysing APT attacks through OO…

Probabilistic Transition-Based Approach for Detecting Application-Layer DDoS Attacks in Encrypted Software-Defined Networks

With the emergence of cloud computing, many attacks, including Distributed Denial-of-Service (DDoS) attacks, have changed their direction towards cloud environment. In particular, DDoS attacks have changed in scale, methods, and targets and become more complex by using advantages provided by cloud computing. Modern cloud computing environments can benefit from moving towards Software-Defined Networking (SDN) technology, which allows network engineers and administrators to respond quickly to the changing business requirements. In this paper, we propose an approach for detecting application-layer DDoS attacks in cloud environment with SDN. The algorithm is applied to statistics extracted from…

Driver Distraction Detection Using Bidirectional Long Short-Term Network Based on Multiscale Entropy of EEG

Driver distraction diverting drivers' attention to unrelated tasks and decreasing the ability to control vehicles, has aroused widespread concern about driving safety. Previous studies have found that driving performance decreases after distraction and have used vehicle behavioral features to detect distraction. But how brain activity changes while distraction remains unknown. Electroencephalography (EEG), a reliable indicator of brain activities has been widely employed in many fields. However, challenges still exist in mining the distraction information of EEG in realistic driving scenarios with uncertain information. In this paper, we propose a novel framework based on Multi-scale entrop…

Automatic Taxonomy Induction based on Word-embedding of Neural Nets

Taxonomy is a knowledge management tool that presents useful information in a well-ordered structure prevents overloading of information on its access and making the information access qualitative. This article is concerned with automatically extracting asymmetrical hierarchical relations from a large corpus and subsequent taxonomy construction by domain independent and semi-supervised system. The methodology relies on the term’s distributional semantics. The algorithm utilizes the word-embedding generated from the vector space model. The model is trained over a large corpus to generate word-embedding of each word in a corpus. Then, the system finds and extracts the hypernyms by using the g…

DNS Tunneling Detection Techniques – Classification, and Theoretical Comparison in Case of a Real APT Campaign

Domain Name System (DNS) plays an important role as a translation protocol in everyday use of the Internet. The purpose of DNS is to translate domain names into IP addresses and vice versa. However, its simple architecture can easily be misused for malicious activities. One huge security threat concerning DNS is tunneling, which helps attackers bypass the security systems unnoticed. A DNS tunnel can be used for three purposes: as a command and control channel, for data exfiltration or even for tunneling another protocol through it. In this paper, we surveyed different techniques for DNS tunneling detection. We classified those first based on the type of data and then within the categories b…