Predicting Cyber Events by Leveraging Hacker Sentiment

Recent high-profile cyber attacks exemplify why organizations need better cyber defenses. Cyber threats are hard to accurately predict because attackers usually try to mask their traces. However, they often discuss exploits and techniques on hacking forums. The community behavior of the hackers may provide insights into groups' collective malicious activity. We propose a novel approach to predict cyber events using sentiment analysis. We test our approach using cyber attack data from 2 major business organizations. We consider 3 types of events: malicious software installation, malicious destination visits, and malicious emails that surpassed the target organizations' defenses. We construct predictive signals by applying sentiment analysis on hacker forum posts to better understand hacker behavior. We analyze over 400K posts generated between January 2016 and January 2018 on over 100 hacking forums both on surface and Dark Web. We find that some forums have significantly more predictive power than others. Sentiment-based models that leverage specific forums can outperform state-of-the-art deep learning and time-series models on forecasting cyber attacks weeks ahead of the events

#Election2020: The First Public Twitter Dataset on the 2020 US Presidential Election

The integrity of democratic political discourse is at the core to guarantee free and fair elections. With social media often dictating the tones and trends of politics-related discussion, it is of paramount important to be able to study online chatter, especially in the run up to important voting events, like in the case of the upcoming November 3, 2020 U.S. Presidential Election. Limited access to social media data is often the first barrier to impede, hinder, or slow down progress, and ultimately our understanding of online political discourse. To mitigate this issue and try to empower the Computational Social Science research community, we decided to publicly release a massive-scale, longitudinal dataset of U.S. politics- and election-related tweets. This multilingual dataset that we have been collecting for over one year encompasses hundreds of millions of tweets and tracks all salient U.S. politics trends, actors, and events between 2019 and 2020. It predates and spans the whole period of Republican and Democratic primaries, with real-time tracking of all presidential contenders of both sides of the isle. After that, it focuses on presidential and vice-presidential candidates. Our dataset release is curated, documented and will be constantly updated on a weekly-basis, until the November 3, 2020 election and beyond. We hope that the academic community, computational journalists, and research practitioners alike will all take advantage of our dataset to study relevant scientific and social issues, including problems like misinformation, information manipulation, interference, and distortion of online political discourse that have been prevalent in the context of recent election events in the United States and worldwide. Our dataset is available at: https://github.com/echen102/us-pres-elections-2020Comment: Our dataset is available at: https://github.com/echen102/us-pres-elections-202

Perils and Challenges of Social Media and Election Manipulation Analysis: The 2018 US Midterms

One of the hallmarks of a free and fair society is the ability to conduct a peaceful and seamless transfer of power from one leader to another. Democratically, this is measured in a citizen population's trust in the electoral system of choosing a representative government. In view of the well documented issues of the 2016 US Presidential election, we conducted an in-depth analysis of the 2018 US Midterm elections looking specifically for voter fraud or suppression. The Midterm election occurs in the middle of a 4 year presidential term. For the 2018 midterms, 35 senators and all the 435 seats in the House of Representatives were up for re-election, thus, every congressional district and practically every state had a federal election. In order to collect election related tweets, we analyzed Twitter during the month prior to, and the two weeks following, the November 6, 2018 election day. In a targeted analysis to detect statistical anomalies or election interference, we identified several biases that can lead to wrong conclusions. Specifically, we looked for divergence between actual voting outcomes and instances of the #ivoted hashtag on the election day. This analysis highlighted three states of concern: New York, California, and Texas. We repeated our analysis discarding malicious accounts, such as social bots. Upon further inspection and against a backdrop of collected general election-related tweets, we identified some confounding factors, such as population bias, or bot and political ideology inference, that can lead to false conclusions. We conclude by providing an in-depth discussion of the perils and challenges of using social media data to explore questions about election manipulation

Social Bots for Online Public Health Interventions

According to the Center for Disease Control and Prevention, in the United States hundreds of thousands initiate smoking each year, and millions live with smoking-related dis- eases. Many tobacco users discuss their habits and preferences on social media. This work conceptualizes a framework for targeted health interventions to inform tobacco users about the consequences of tobacco use. We designed a Twitter bot named Notobot (short for No-Tobacco Bot) that leverages machine learning to identify users posting pro-tobacco tweets and select individualized interventions to address their interest in tobacco use. We searched the Twitter feed for tobacco-related keywords and phrases, and trained a convolutional neural network using over 4,000 tweets dichotomously manually labeled as either pro- tobacco or not pro-tobacco. This model achieves a 90% recall rate on the training set and 74% on test data. Users posting pro- tobacco tweets are matched with former smokers with similar interests who posted anti-tobacco tweets. Algorithmic matching, based on the power of peer influence, allows for the systematic delivery of personalized interventions based on real anti-tobacco tweets from former smokers. Experimental evaluation suggests that our system would perform well if deployed. This research offers opportunities for public health researchers to increase health awareness at scale. Future work entails deploying the fully operational Notobot system in a controlled experiment within a public health campaign

Energy Saving by Chopping off Peak Demand Using Day Light

An artificial intelligent technique has been implemented in this research using real time data’s to calculate how much energy can be chopped from peak load demand. The results are based on real time data that are taken from power delivering centers. These data’s do reflect the present condition of power and a solution to those critical conditions during the peak period. These are done in such a way such that helps in judicious scheduling of load. The time based load scheduling has been done so as to understand the basic criteria for solving power crisis during morning peak and early evening peak. The sunray availability and percentage of load that will use day light saving (DLS) technique has been taken into account in this work. The results shows that about 0.5% to 1% of load can be shedded off from the peak load period which otherwise is reduction of power. Thus it otherwise also means that an equivalent amount of energy is saved which amounts to a large saving of national money. This result is obtained on monthly and even daily basis. Thus this paper justifies DLS gives a new renewable technique to save energy.

APOPTOSIS-INDUCING POTENTIAL OF LAWSONIA ALBA LAM. LEAVES ON HEPATOCELLULAR CARCINOMA (HEP-G2) CELLS ALONG WITH ITS ANTI-OXIDANT PROPERTY

Objective: The current study investigated the anti-cancer potential of methanolic and ethyl acetate fraction of Lawsonia alba L. (Lythraceae) leaves extract on Hep-G2 and RAW 264.7 cells along with in vitro anti-oxidant property of the ethyl acetate fraction.Methods: The cytotoxic activity of methanolic extract and its fractions had been studied by MTT assay on Hep-G2 and RAW 264.7 cells. Morphological study of Hep-G2 cells was performed by light, fluorescence and confocal microscope. 1% agarose gel electrophoresis, detection of apoptosis and cell cycle arrest by flow cytometric analysis had been performed to determine the proportion and stages of cellular apoptosis of Hep-G2 cells. In vitro anti-oxidant study of various fractions of MLA were performed by DPPH and Hydroxyl radical scavenging assay.Results: Cytotoxicity study of MLA and ELA had been confirmed by MTT assay and the IC50 value were calculated to be 75.85μg/ml and 32.81μg/ml on Hep-G2 cell line respectively. Morphological study showed the arrays of nuclear changes including chromatin condensation and apoptotic body formation indicating that treatment with ELA, causes apoptotic changes in the hepatoma cells compared to the untreated control. Agarose gel electrophoresis study showed fragmented DNA in the form of a ladder. Flow cytometric analysis showed an appreciable number of cells in early apoptosis stage. The cells were arrested, mostly in G0/G1 phase of the cell cycle. Antioxidant property of ELA fraction was confirmed by free radical scavenging activity.Conclusion: Ethyl acetate fraction of Lawsonia alba L. leaves possess potent apoptotic activity against Hep-G2 cell line along with notable anti-oxidant activity

LAWSONIA ALBA LEAVES INDUCE APOPTOSIS AND CELL CYCLE ARREST IN B16F10 MELANOMA CELLS

Objective: The present study was designed to investigate the anti-melanoma activity of the ethyl acetate fraction of Lawsonia alba lam leaves (ELA) against B16F10 cells.Methods: Cytotoxicity of ELA on B16F10 cells was determined by MTT assay and supported with the morphology of apoptotic and necrotic cells under phase-contrast microscope, fluorescence microscopy with AO/EtBr, confocal microscopy with PI, Agarose gel electrophoresis and Annexin V-FITC/PI staining, mitochondrial membrane potential and cell cycle arrest by FACS was also performed on B16F10 cells.Results: Cytotoxic effect of ELA on B16F10 melanoma cell was confirmed by MTT assay with an IC50 value of 14.10μg/ml. Morphological study showed arrays of both the nuclear changes including chromatin condensation and apoptotic body formation indicating that the treatment with ELA and 5-Fluorouracil (standard) causes apoptotic changes in the melanoma cells compared to the untreated control. Agarose gel electrophoresis study showed fragmented DNA in the form of ladder. The depolarization of mitochondrial membrane potential was confirmed. Flow cytometric analysis showed appreciable number of cells in early apoptotic stage. The cells were arrested mostly in G0/G1 phase of cell cycle.Conclusion: Ethyl acetate fraction of Lawsonia alba L. leaves possesses potent apoptotic activity against B16F10 cells

Unveiling Coordinated Groups Behind White Helmets Disinformation

Propaganda, disinformation, manipulation, and polarization are the modern illnesses of a society increasingly dependent on social media as a source of news. In this paper, we explore the disinformation campaign, sponsored by Russia and allies, against the Syria Civil Defense (a.k.a. the White Helmets). We unveil coordinated groups using automatic retweets and content duplication to promote narratives and/or accounts. The results also reveal distinct promoting strategies, ranging from the small groups sharing the exact same text repeatedly, to complex "news website factories" where dozens of accounts synchronously spread the same news from multiple sites.Comment: To be presented at WWW 2020 Workshop on Computational Methods in Online Misbehavior and forthcoming in the Companion Proceedings of the Web Conference 202