DDoS Attack Detection and Classification using Machine Learning Models with Real-Time Dataset Created

Distributed Denial of Service (DDoS) attack is one of the common attack that is predominant in the cyber world. DDoS attack poses a serious threat to the internet users and affects the availability of services to legitimate users. DDOS attack is characterized by the blocking a particular service by paralyzing the victim’s resources so that they cannot be used to legitimate purpose leading to server breakdown. DDoS uses networked devices into remotely controlled bots and generates attack. The proposed system detects the DDoS attack and malware with high detection accuracy using machine learning algorithms. The real time traffic is generated using virtual instances running in a private cloud. The DDoS attack is detected by considering the various SNMP parameters and classifying using machine learning technique like bagging, boosting and ensemble models. Also, the various types of malware on the networked devices are prevent from being used as a bot for DDOS attack generation.

Access and Use of Networked Electronic Devices for Educational Information Management by Undergraduates in South Africa

Electronic networked devices have broken down fences of communication and information access from anywhere in the world, and its capacity to store, manage, as well as transmit vast amounts of information to anybody anywhere in the world often makes it to be referred to as the “Information Highway.” But digital divide has constraint access and use of these devices especially in developing countries like South Africa where economic-digital divide is very common. Four electronic devices (desktop, laptop computer, mobile phones, and tablets) that are mainly used for schoolwork and educational information management were sampled. The objectives of the study were to find out the electronic devices owned/accessible to undergraduate students for educational information management, identify the location where undergraduate students mainly access the internet, appraise the means and cost of accessing the internet by undergraduate student, and ascertain how undergraduate students utilise networked devices to manage educational information. Survey research design was adopted for the study and quantitative data was collected with the use of a questionnaire from 390 respondents. Results from the study indicated that mobile phone (362 or 92.8%) and laptop computers (305 or 78.2%) are the most owned electronic devices. Only 3 or 0.8% do not own any of the electronics listed in the survey while 2 (0.5%) do not have access to any electronic device listed for the survey. Most of the respondents (307 or 78.7%) access the internet from their mobile phones with access to the internet being free wi-fi on campus 349 (89.9%). Ninety percent of the respondents make use of their mobile phone in managing educational information while only 19% manage educational information on their laptops when outside the university campuses. Digital divide still plays a major role in the hindering undergraduate students in the management of educational information using networked devices.

Visual Access and Excess

This chapter conceptualizes several aspects of digital remediations in an era of restless digital navigation. The instantaneous creation and sharing of content altering the relationship between bodies, spaces, and devices is demonstrated here. The first section of the chapter addresses various aspects of digital remediations such as screen-mediated navigations and the multifarious role of networked devices, social consequences of the intimacy between body and device, and instantaneous modes of sharing and their implications. The second segment reflects on dimensions of the post-photographic condition such as unlimited storage, ease and excess of auto-voyeuristic tendencies, publicizing the private self, death of the photographic ‘lack’, ‘fixity’, ‘nostalgia’, the witlessness/weightlessness of the visual excess, the ease of visual manipulation and the democratization of the medium, and the death of the photographer. It also briefly discusses the control and manipulation of the self-generated data.

Pornifying the Network

Watching pornography online is a deeply personal, if not secretive act, yet the ease with which a near-infinite supply of adult content is shored up by networks of shared experiences. In fact, the persistent assumption that consuming adult content is a ‘closed’ experience has largely stunted efforts to reconceptualize online pornography as a “network experience.” As Wendy Chun asks, “Why are networked devices described as ‘personal,’ when they are so chatty and promiscuous?” This article, therefore, attempts to ‘pornify the network’ by tracing the movement, flows, and processual emergence of networks that have been crucial to the formation and continued proliferation of online pornography. Two case studies are used to illustrate the persistence of this framework: the first theorizes ‘edging’ in early online pornography, while the second puts into question the politics of the world’s largest porn website deploying user data for titillating effect. Theorizing a pornified network ultimately reroutes persistent technological imaginaries of the network through affect, sensation, and the entanglements of desire.

‘Hey Alexa, what did I forget?’: Networked devices, Internet search and the delegation of human memory

This article examines how we increasingly delegate the task of memorisation to networked devices and associated applications, such as Google search. Human memory is supplemented by the proliferation of voice assistants embedded in mobile, wearable and situated devices that provide ready access to common knowledge as well as reminders for procedural tasks. Previous research in the field of transactive memory, investigating how search engines and networked information discourage memorisation, underpins the examination of these emergent technologies. However, the article extends the argument further by examining not just access to information but when it is interpolated into everyday activity and how this is facilitated by voice interfaces. At stake is deciding which aspects of our networked technology should be developed in order to support rather than supplant human memory in conscious decision-making.

An Improved Artificial Bee Colony Algorithm for the Flexible Integrated Scheduling Problem Using Networked Devices Collaboration

This paper studies the flexible integrated scheduling problem, which is an extension of job-shop scheduling considering the assembly and the machining stages at the same time, with networked devices (FISND). The completion time of the entire product may be impacted by the uncertainty of the process constraint structure and flexible equipment, so we take machining structure evaluation (MSE) into account. We proposed an improved artificial bee colony algorithm considering MSE (ABC[Formula: see text]) with two new strategies: one is a dynamic perturbation step strategy and the other is double-chain similarity and migration time factor strategy to evaluate the product processing structure, and then we computed the selection probability of the followers by it. Finally, the experimental results show that ABC[Formula: see text] has a better performance and faster convergence than the algorithm ABC, hyABC, GA. It can not only solve the flexible integrated scheduling problem with networked devices, but also yield better solutions to the typical flexible integrated scheduling problem than the flexible equipment integrated scheduling algorithm based on device-driven.