Data-Driven and Deep Learning Methodology for Deceptive Advertising and Phone Scams Detection

The advance of smartphones and cellular networks boosts the need of mobile advertising and targeted marketing. However, it also triggers the unseen security threats. We found that the phone scams with fake calling numbers of very short lifetime are increasingly popular and have been used to trick the users. The harm is worldwide. On the other hand, deceptive advertising (deceptive ads), the fake ads that tricks users to install unnecessary apps via either alluring or daunting texts and pictures, is an emerging threat that seriously harms the reputation of the advertiser. To counter against these two new threats, the conventional blacklist (or whitelist) approach and the machine learning approach with predefined features have been proven useless. Nevertheless, due to the success of deep learning in developing the highly intelligent program, our system can efficiently and effectively detect phone scams and deceptive ads by taking advantage of our unified framework on deep neural network (DNN) and convolutional neural network (CNN). The proposed system has been deployed for operational use and the experimental results proved the effectiveness of our proposed system. Furthermore, we keep our research results and release experiment material on http://DeceptiveAds.TWMAN.ORG and http://PhoneScams.TWMAN.ORG if there is any update.Comment: 6 pages, TAAI 2017 versio

Applications of Machine Learning to Threat Intelligence, Intrusion Detection and Malware

Artificial Intelligence (AI) and Machine Learning (ML) are emerging technologies with applications to many fields. This paper is a survey of use cases of ML for threat intelligence, intrusion detection, and malware analysis and detection. Threat intelligence, especially attack attribution, can benefit from the use of ML classification. False positives from rule-based intrusion detection systems can be reduced with the use of ML models. Malware analysis and classification can be made easier by developing ML frameworks to distill similarities between the malicious programs. Adversarial machine learning will also be discussed, because while ML can be used to solve problems or reduce analyst workload, it also introduces new attack surfaces

Doctor of Philosophy

dissertationIn computer science, functional software testing is a method of ensuring that software gives expected output on specific inputs. Software testing is conducted to ensure desired levels of quality in light of uncertainty resulting from the complexity of software. Most of today's software is written by people and software development is a creative activity. However, due to the complexity of computer systems and software development processes, this activity leads to a mismatch between the expected software functionality and the implemented one. If not addressed in a timely and proper manner, this mismatch can cause serious consequences to users of the software, such as security and privacy breaches, financial loss, and adversarial human health issues. Because of manual effort, software testing is costly. Software testing that is performed without human intervention is automatic software testing and it is one way of addressing the issue. In this work, we build upon and extend several techniques for automatic software testing. The techniques do not require any guidance from the user. Goals that are achieved with the techniques are checking for yet unknown errors, automatically testing object-oriented software, and detecting malicious software. To meet these goals, we explored several techniques and related challenges: automatic test case generation, runtime verification, dynamic symbolic execution, and the type and size of test inputs for efficient detection of malicious software via machine learning. Our work targets software written in the Java programming language, though the techniques are general and applicable to other languages. We performed an extensive evaluation on freely available Java software projects, a flight collision avoidance system, and thousands of applications for the Android operating system. Evaluation results show to what extent dynamic symbolic execution is applicable in testing object-oriented software, they show correctness of the flight system on millions of automatically customized and generated test cases, and they show that simple and relatively small inputs in random testing can lead to effective malicious software detection

Malware classification using self organising feature maps and machine activity data

In this article we use machine activity metrics to automatically distinguish between malicious and trusted portable executable software samples. The motivation stems from the growth of cyber attacks using techniques that have been employed to surreptitiously deploy Advanced Persistent Threats (APTs). APTs are becoming more sophisticated and able to obfuscate much of their identifiable features through encryption, custom code bases and in-memory execution. Our hypothesis is that we can produce a high degree of accuracy in distinguishing malicious from trusted samples using Machine Learning with features derived from the inescapable footprint left behind on a computer system during execution. This includes CPU, RAM, Swap use and network traffic at a count level of bytes and packets. These features are continuous and allow us to be more flexible with the classification of samples than discrete features such as API calls (which can also be obfuscated) that form the main feature of the extant literature. We use these continuous data and develop a novel classification method using Self Organizing Feature Maps to reduce over fitting during training through the ability to create unsupervised clusters of similar ‘behaviour’ that are subsequently used as features for classification, rather than using the raw data. We compare our method to a set of machine classification methods that have been applied in previous research and demonstrate an increase of between 7.24% and 25.68% in classification accuracy using our method and an unseen dataset over the range of other machine classification methods that have been applied in previous research