Sentiment analysis on predicting presidential election: Twitter used case

© Springer Nature Switzerland AG 2020. Twitter is a popular tool for social interaction over the Internet. It allows users to share/post opinions, social media events, and interact with other political and ordinary people. According to Statista web site 2019 statistical report, it estimated that the number of users on Twitter had grown dramatically over the past couple of years to research 300 million users. Twitter has become the largest source of news and postings for key presidents and political figures. Referring to the Trackalytics 2019 report, the recent president of the USA had posted 4,000 tweets per year, which indicates an average of 11–12 tweets per day. Our research proposes a technique that extracts and analyzes tweets from blogs and predicts election results based on tweets analysis. It assessed the people’s opinion and studied the impact that might predict the final results for the Turkey 2018 presidential election candidates. The final results were compared with the actual election results and had a high accuracy prediction percentage based on the collected 22,000 tweets

1st International Workshop on Search and Mining Terrorist Online Content and Advances in Data Science for Cyber Security and Risk on the Web

The deliberate misuse of technical infrastructure (including the Web and social media) for cyber deviant and cybercriminal behaviour, ranging from the spreading of extremist and terrorism-related material to online fraud and cyber security attacks, is on the rise. This workshop aims to better understand such phenomena and develop methods for tackling them in an effective and efficient manner. The workshop brings together interdisciplinary researchers and experts in Web search, security informatics, social media analysis, machine learning, and digital forensics, with particular interests in cyber security. The workshop programme includes refereed papers, invited talks and a panel discussion for better understanding the current landscape, as well as the future of data mining for detecting cyber deviance

Impact and key challenges of insider threats on organizations and critical businesses

The insider threat has consistently been identified as a key threat to organizations and governments. Understanding the nature of insider threats and the related threat landscape can help in forming mitigation strategies, including non-technical means. In this paper, we survey and highlight challenges associated with the identification and detection of insider threats in both public and private sector organizations, especially those part of a nation’s critical infrastructure. We explore the utility of the cyber kill chain to understand insider threats, as well as understanding the underpinning human behavior and psychological factors. The existing defense techniques are discussed and critically analyzed, and improvements are suggested, in line with the current state-of-the-art cyber security requirements. Finally, open problems related to the insider threat are identified and future research directions are discussed

Detecting hate speech on twitter using a convolution-GRU based deep neural network

In recent years, the increasing propagation of hate speech on social media and the urgent need for effective counter-measures have drawn significant investment from governments, companies, as well as empirical research. Despite a large number of emerging scientific studies to address the problem, existing methods are limited in several ways, such as the lack of comparative evaluations which makes it difficult to assess the contribution of individual works. This paper introduces a new method based on a deep neural network combining convolutional and long short term memory networks, and conducts an extensive evaluation of the method against several baselines and state of the art on the largest collection of publicly available datasets to date. We show that our proposed method outperforms state of the art on 6 out of 7 datasets by between 0.2 and 13.8 points in F1. We also carry out further analysis using automatic feature selection to understand the impact of the conventional manual feature engineering process that distinguishes most methods in this field. Our findings challenge the existing perception of the importance of feature engineering, as we show that: the automatic feature selection algorithm drastically reduces the original feature space by over 90% and selects predominantly generic features from datasets; nevertheless, machine learning algorithms perform better using automatically selected features than the original features

A study of cyber hate on Twitter with implications for social media governance strategies

This paper explores ways in which the harmful effects of cyber hate may be mitigated through mechanisms for enhancing the self-governance of new digital spaces. We report findings from a mixed methods study of responses to cyber hate posts, which aimed to: (i) understand how people interact in this context by undertaking qualitative interaction analysis and developing a statistical model to explain the volume of responses to cyber hate posted to Twitter, and (ii) explore use of machine learning techniques to assist in identifying cyber hate counter-speech

Optimized predictive control for AGC cyber resiliency

Automatic Generation Control (AGC) is used in smart grid systems to maintain the grid's frequency to a nominal value. Cyber-attacks such as time delay and false data injection on the tie-line power flow, frequency measurements, and Area Control Error (ACE) control signals can cause frequency excursion that can trigger load shedding, generators' damage, and blackouts. Therefore, resilience and detection of attacks are of paramount importance in terms of the reliable operation of the grid. In contrast with the previous works that overlook ACE resiliency, this paper proposes an approach for cyber-attack detection and resiliency in the overall AGC process. We propose a state estimation algorithm approach for the AGC system by utilizing prior information based on Gaussian process regression, a non-parametric, Bayesian approach to regression. We evaluate our approach using the PowerWorld simulator based on the three-area New England IEEE 39-bus model. Moreover, we utilize the modified version of the New England ISO load data for the three-area power system to create a more realistic dataset. Our results clearly show that our resilient control system approach can mitigate the system using predictive control and detect the attack with a 100 percent detection rate in a shorter period using prior auxiliary information

PharmaCrypt : blockchain for critical pharmaceutical industry to counterfeit drugs

This research analyzes the impact of counterfeit drugs on the health-care supply chain industry and evaluates the solutions currently in place to reduce the number of counterfeits coming to the market. Feedback information obtained from industry professionals is used to build requirements for PharmaCrypt, a new blockchain-driven tool

Universal trapping scaling on the unstable manifold for a collisionless electrostatic mode

An amplitude equation for an unstable mode in a collisionless plasma is derived from the dynamics on the two-dimensional unstable manifold of the equilibrium. The mode amplitude $\rho(t)$ decouples from the phase due to the spatial homogeneity of the equilibrium, and the resulting one-dimensional dynamics is analyzed using an expansion in $\rho$. As the linear growth rate $\gamma$ vanishes, the expansion coefficients diverge; a rescaling $\rho(t)\equiv\gamma^2\,r(\gamma t)$ of the mode amplitude absorbs these singularities and reveals that the mode electric field exhibits trapping scaling $|E_1|\sim\gamma^2$ as $\gamma\rightarrow0$. The dynamics for $r(\tau)$ depends only on the phase $e^{i\xi}$ where $d\epsilon_{{k}} /dz=|{\epsilon_{{k}}}|e^{-i\xi/2}$ is the derivative of the dielectric as $\gamma\rightarrow0$.Comment: 11 pages (Latex/RevTex), 2 figures available in hard copy from the Author ([email protected]); paper accepted by Physical Review Letter

Dynamic real-time risk analytics of uncontrollable states in complex internet of things systems: cyber risk at the edge

AbstractThe Internet of Things (IoT) triggers new types of cyber risks. Therefore, the integration of new IoT devices and services requires a self-assessment of IoT cyber security posture. By security posture this article refers to the cybersecurity strength of an organisation to predict, prevent and respond to cyberthreats. At present, there is a gap in the state of the art, because there are no self-assessment methods for quantifying IoT cyber risk posture. To address this gap, an empirical analysis is performed of 12 cyber risk assessment approaches. The results and the main findings from the analysis is presented as the current and a target risk state for IoT systems, followed by conclusions and recommendations on a transformation roadmap, describing how IoT systems can achieve the target state with a new goal-oriented dependency model. By target state, we refer to the cyber security target that matches the generic security requirements of an organisation. The research paper studies and adapts four alternatives for IoT risk assessment and identifies the goal-oriented dependency modelling as a dominant approach among the risk assessment models studied. The new goal-oriented dependency model in this article enables the assessment of uncontrollable risk states in complex IoT systems and can be used for a quantitative self-assessment of IoT cyber risk posture.</jats:p