Detection of Fraudulent Financial Statements Using the Beneish Ratio Index for Manufacturing Companies Listed on the Indonesian Stock Exchange in 2016 and 2017 Period

Fraud is an action taken intentionally and it is done for personal or other people's purposes, where the action has caused harm to certain parties or certain institutions. Misstatements contained in fraudulent financial statements are intentional misstatements to deceive users of financial statements. The source of this misstatement includes manipulation or falsification of accounting records, intentional misstatements or omissions from financial statements, and / or incorrect application of accounting principles. In Indonesia, accounting fraud also occurs at the company level, both private and government companies. On December 6, 2012, the announcement of Indonesia's score in the Corruption Perception Index (CPI) was 32 and ranked 118th out of 176 countries which measured the level of corruption (Transparency International, 2012). In 2001, a fraud scandal occurred by PT Kimia Farma Tbk. PT Kimia Farma is a state-owned company whose shares have been traded on the exchange to become public company. Based on indications by the Ministry of BUMN and Bapepam's examination, it was found that there were misstatements in the financial statements which resulted in overstatement of net income for the year ended 31 December 2001 of Rp. 32.7 billion, which represented 2.3% of sales and 24.7% from net income. The author's purpose of this study is to discuss about detecting fraud in financial statements by using 5 (five) of the 8 (eight) Beneish ratio indices, because Beneish's research states that the Days Sales in Receivables Index (DSRI) ratio index, the Gross Margin Index ( GMI), Asset Quality Index (AQI), Sales Growth Index (SGI), and Total Accrual to Total Asst Index (TATA) have significant results to detect financial report manipulation

THE INFLUENCE OF SPECIAL ITEMS TO CORE EARNINGS IN EARNINGS MANAGEMENT AT MANUFACTURING COMPANIES LISTED IN JAKARTA STOCK EXCHANGE

This paper examines the classification of items within the income statement as an earnings management tool. Evidence is consistent with managers opportunistically shifting expenses from core expenses (cost of goods sold and selling, general, and administrative expenses) to special items. This vertical movement of expenses does not change bottom-line earnings, but overstates ‘‘core’’ earnings. Keywords: earnings management; earnings components; special items

Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection

Machine learning based solutions have been successfully employed for automatic detection of malware in Android applications. However, machine learning models are known to lack robustness against inputs crafted by an adversary. So far, the adversarial examples can only deceive Android malware detectors that rely on syntactic features, and the perturbations can only be implemented by simply modifying Android manifest. While recent Android malware detectors rely more on semantic features from Dalvik bytecode rather than manifest, existing attacking/defending methods are no longer effective. In this paper, we introduce a new highly-effective attack that generates adversarial examples of Android malware and evades being detected by the current models. To this end, we propose a method of applying optimal perturbations onto Android APK using a substitute model. Based on the transferability concept, the perturbations that successfully deceive the substitute model are likely to deceive the original models as well. We develop an automated tool to generate the adversarial examples without human intervention to apply the attacks. In contrast to existing works, the adversarial examples crafted by our method can also deceive recent machine learning based detectors that rely on semantic features such as control-flow-graph. The perturbations can also be implemented directly onto APK's Dalvik bytecode rather than Android manifest to evade from recent detectors. We evaluated the proposed manipulation methods for adversarial examples by using the same datasets that Drebin and MaMadroid (5879 malware samples) used. Our results show that, the malware detection rates decreased from 96% to 1% in MaMaDroid, and from 97% to 1% in Drebin, with just a small distortion generated by our adversarial examples manipulation method.Comment: 15 pages, 11 figure

Semantic Sentiment Analysis of Twitter Data

Internet and the proliferation of smart mobile devices have changed the way information is created, shared, and spreads, e.g., microblogs such as Twitter, weblogs such as LiveJournal, social networks such as Facebook, and instant messengers such as Skype and WhatsApp are now commonly used to share thoughts and opinions about anything in the surrounding world. This has resulted in the proliferation of social media content, thus creating new opportunities to study public opinion at a scale that was never possible before. Naturally, this abundance of data has quickly attracted business and research interest from various fields including marketing, political science, and social studies, among many others, which are interested in questions like these: Do people like the new Apple Watch? Do Americans support ObamaCare? How do Scottish feel about the Brexit? Answering these questions requires studying the sentiment of opinions people express in social media, which has given rise to the fast growth of the field of sentiment analysis in social media, with Twitter being especially popular for research due to its scale, representativeness, variety of topics discussed, as well as ease of public access to its messages. Here we present an overview of work on sentiment analysis on Twitter.Comment: Microblog sentiment analysis; Twitter opinion mining; In the Encyclopedia on Social Network Analysis and Mining (ESNAM), Second edition. 201