Mask Off: Analytic-based Malware Detection By Transfer Learning and Model Personalization

The vulnerability of smartphones to cyberattacks has been a severe concern to users arising from the integrity of installed applications (\textit{apps}). Although applications are to provide legitimate and diversified on-the-go services, harmful and dangerous ones have also uncovered the feasible way to penetrate smartphones for malicious behaviors. Thorough application analysis is key to revealing malicious intent and providing more insights into the application behavior for security risk assessments. Such in-depth analysis motivates employing deep neural networks (DNNs) for a set of features and patterns extracted from applications to facilitate detecting potentially dangerous applications independently. This paper presents an Analytic-based deep neural network, Android Malware detection (ADAM), that employs a fine-grained set of features to train feature-specific DNNs to have consensus on the application labels when their ground truth is unknown. In addition, ADAM leverages the transfer learning technique to obtain its adjustability to new applications across smartphones for recycling the pre-trained model(s) and making them more adaptable by model personalization and federated learning techniques. This adjustability is also assisted by federated learning guards, which protect ADAM against poisoning attacks through model analysis. ADAM relies on a diverse dataset containing more than 153000 applications with over 41000 extracted features for DNNs training. The ADAM's feature-specific DNNs, on average, achieved more than 98% accuracy, resulting in an outstanding performance against data manipulation attacks

Experience Report on the Challenges and Opportunities in Securing Smartphones Against Zero-Click Attacks

Zero-click attacks require no user interaction and typically exploit zero-day (i.e., unpatched) vulnerabilities in instant chat applications (such as WhatsApp and iMessage) to gain root access to the victim's smartphone and exfiltrate sensitive data. In this paper, we report our experiences in attempting to secure smartphones against zero-click attacks. We approached the problem by first enumerating several properties we believed were necessary to prevent zero-click attacks against smartphones. Then, we created a security design that satisfies all the identified properties, and attempted to build it using off-the-shelf components. Our key idea was to shift the attack surface from the user's smartphone to a sandboxed virtual smartphone ecosystem where each chat application runs in isolation. Our performance and usability evaluations of the system we built highlighted several shortcomings and the fundamental challenges in securing modern smartphones against zero-click attacks. In this experience report, we discuss the lessons we learned, and share insights on the missing components necessary to achieve foolproof security against zero-click attacks for modern mobile devices

A study of security issues of mobile apps in the android platform using machine learning approaches

Mobile app poses both traditional and new potential threats to system security and user privacy. There are malicious apps that may do harm to the system, and there are mis-behaviors of apps, which are reasonable and legal when not abused, yet may lead to real threats otherwise. Moreover, due to the nature of mobile apps, a running app in mobile devices may be only part of the software, and the server side behavior is usually not covered by analysis. Therefore, direct analysis on the app itself may be incomplete and additional sources of information are needed. In this dissertation, we discuss how we can apply machine learning techniques in multiple tasks for security issues in regard of mobile apps in the Android platform. These include malicious apps detection and security risk estimation of apps. Both direct sources of information from the developer of apps and indirect sources of information from user comments are utilized in these tasks. We also propose comparison of these different sources in the task of security risk estimation to point out the necessity of usage of indirect sources in mobile app security tasks

MALGRA: Machine Learning and N-Gram Malware Feature Extraction and Detection System

Detection and mitigation of modern malware are critical for the normal operation of an organisation. Traditional defence mechanisms are becoming increasingly ineffective due to the techniques used by attackers such as code obfuscation, metamorphism, and polymorphism, which strengthen the resilience of malware. In this context, the development of adaptive, more effective malware detection methods has been identified as an urgent requirement for protecting the IT infrastructure against such threats, and for ensuring security. In this paper, we investigate an alternative method for malware detection that is based on N-grams and machine learning. We use a dynamic analysis technique to extract an Indicator of Compromise (IOC) for malicious files, which are represented using N-grams. The paper also proposes TF-IDF as a novel alternative used to identify the most significant N-grams features for training a machine learning algorithm. Finally, the paper evaluates the proposed technique using various supervised machine-learning algorithms. The results show that Logistic Regression, with a score of 98.4%, provides the best classification accuracy when compared to the other classifiers used.</jats:p

Studying JavaScript Security Through Static Analysis

Mit dem stetigen Wachstum des Internets wächst auch das Interesse von Angreifern. Ursprünglich sollte das Internet Menschen verbinden; gleichzeitig benutzen aber Angreifer diese Vernetzung, um Schadprogramme wirksam zu verbreiten. Insbesondere JavaScript ist zu einem beliebten Angriffsvektor geworden, da es Angreifer ermöglicht Bugs und weitere Sicherheitslücken auszunutzen, und somit die Sicherheit und Privatsphäre der Internetnutzern zu gefährden. In dieser Dissertation fokussieren wir uns auf die Erkennung solcher Bedrohungen, indem wir JavaScript Code statisch und effizient analysieren. Zunächst beschreiben wir unsere zwei Detektoren, welche Methoden des maschinellen Lernens mit statischen Features aus Syntax, Kontroll- und Datenflüssen kombinieren zur Erkennung bösartiger JavaScript Dateien. Wir evaluieren daraufhin die Verlässlichkeit solcher statischen Systeme, indem wir bösartige JavaScript Dokumente umschreiben, damit sie die syntaktische Struktur von bestehenden gutartigen Skripten reproduzieren. Zuletzt studieren wir die Sicherheit von Browser Extensions. Zu diesem Zweck modellieren wir Extensions mit einem Graph, welcher Kontroll-, Daten-, und Nachrichtenflüsse mit Pointer Analysen kombiniert, wodurch wir externe Flüsse aus und zu kritischen Extension-Funktionen erkennen können. Insgesamt wiesen wir 184 verwundbare Chrome Extensions nach, welche die Angreifer ausnutzen könnten, um beispielsweise beliebigen Code im Browser eines Opfers auszuführen.As the Internet keeps on growing, so does the interest of malicious actors. While the Internet has become widespread and popular to interconnect billions of people, this interconnectivity also simplifies the spread of malicious software. Specifically, JavaScript has become a popular attack vector, as it enables to stealthily exploit bugs and further vulnerabilities to compromise the security and privacy of Internet users. In this thesis, we approach these issues by proposing several systems to statically analyze real-world JavaScript code at scale. First, we focus on the detection of malicious JavaScript samples. To this end, we propose two learning-based pipelines, which leverage syntactic, control and data-flow based features to distinguish benign from malicious inputs. Subsequently, we evaluate the robustness of such static malicious JavaScript detectors in an adversarial setting. For this purpose, we introduce a generic camouflage attack, which consists in rewriting malicious samples to reproduce existing benign syntactic structures. Finally, we consider vulnerable browser extensions. In particular, we abstract an extension source code at a semantic level, including control, data, and message flows, and pointer analysis, to detect suspicious data flows from and toward an extension privileged context. Overall, we report on 184 Chrome extensions that attackers could exploit to, e.g., execute arbitrary code in a victim's browser

Deep Learning for Network Traffic Monitoring and Analysis (NTMA): A Survey

Modern communication systems and networks, e.g., Internet of Things (IoT) and cellular networks, generate a massive and heterogeneous amount of traffic data. In such networks, the traditional network management techniques for monitoring and data analytics face some challenges and issues, e.g., accuracy, and effective processing of big data in a real-time fashion. Moreover, the pattern of network traffic, especially in cellular networks, shows very complex behavior because of various factors, such as device mobility and network heterogeneity. Deep learning has been efficiently employed to facilitate analytics and knowledge discovery in big data systems to recognize hidden and complex patterns. Motivated by these successes, researchers in the field of networking apply deep learning models for Network Traffic Monitoring and Analysis (NTMA) applications, e.g., traffic classification and prediction. This paper provides a comprehensive review on applications of deep learning in NTMA. We first provide fundamental background relevant to our review. Then, we give an insight into the confluence of deep learning and NTMA, and review deep learning techniques proposed for NTMA applications. Finally, we discuss key challenges, open issues, and future research directions for using deep learning in NTMA applications.publishedVersio

Klasifikasi cyberbullying berbasis text mining menggunakan Bayes Optimal Classifier

Pengaruh perkembangan teknologi yang semakin mudah digunakan, membuat banyak efek negatif yang bermunculan. Salah satunya adalah tindakan cyberbullying yang dilakukan diberbagai media online, seperti media sosial dan online game. Tidak jarang pemain online game melakukan tindakan tersebut, sehinggga dapat meresahkan individu maupun negara. Untuk menindak lanjuti permasalahan tersebut diterapkan sebuah sistem klasifikasi teks untuk mengetahui teks cyberbullying. Sehingga pada penelitian ini dilakukan beberapa proses seperti pengambilan data, pengolahan data dan analisis perhitungan performa dari metode klasifikasi Bayes Optimal Classifier (BOC) dan metode Bernoulli Naïve Bayes (BNB). Hasil penelitian dengan menggunakan tiga macam skenario proporsi data yakni pada sekenario pertama data sebesar 40% data latih dan 60% data uji, sekenario kedua sebesar 50% data latih dan 50% data uji, dan sekenario ketiga sebesar 60% data latih dan 40% data uji, didapatkan hasil akurasi metode BOC terbesar adalah 81,11% pada skenario ketiga dengan proporsi data sebesar 60% data latih dan 40% data uji. Sedangkan hasil pada skenario yang sama, nilai akurasi dari metode BNB sebesar 73,33%. Dari hasil analisis yang diperoleh, diketahui bahwa metode BOC mendapat nilai akurasi lebih tinggi daripada metode BN