Biblio

Modelling network traffic is gaining importance to counter modern security threats of ever increasing sophistication. It is though surprisingly difficult and costly to construct reliable classifiers on top of telemetry data due to the variety and complexity of signals that no human can manage to interpret in full. Obtaining training data with sufficiently large and variable body of labels can thus be seen as a prohibitive problem. The goal of this work is to detect infected computers by observing their HTTP(S) traffic collected from network sensors, which are typically proxy servers or network firewalls, while relying on only minimal human input in the model training phase. We propose a discriminative model that makes decisions based on a computer's all traffic observed during a predefined time window (5 minutes in our case). The model is trained on traffic samples collected over equally-sized time windows for a large number of computers, where the only labels needed are (human) verdicts about the computer as a whole (presumed infected vs. presumed clean). As part of training, the model itself learns discriminative patterns in traffic targeted to individual servers and constructs the final high-level classifier on top of them. We show the classifier to perform with very high precision, and demonstrate that the learned traffic patterns can be interpreted as Indicators of Compromise. We implement the discriminative model as a neural network with special structure reflecting two stacked multi instance problems. The main advantages of the proposed configuration include not only improved accuracy and ability to learn from gross labels, but also automatic learning of server types (together with their detectors) that are typically visited by infected computers.

Modelling network traffic is gaining importance to counter modern security threats of ever increasing sophistication. It is though surprisingly difficult and costly to construct reliable classifiers on top of telemetry data due to the variety and complexity of signals that no human can manage to interpret in full. Obtaining training data with sufficiently large and variable body of labels can thus be seen as a prohibitive problem. The goal of this work is to detect infected computers by observing their HTTP(S) traffic collected from network sensors, which are typically proxy servers or network firewalls, while relying on only minimal human input in the model training phase. We propose a discriminative model that makes decisions based on a computer's all traffic observed during a predefined time window (5 minutes in our case). The model is trained on traffic samples collected over equally-sized time windows for a large number of computers, where the only labels needed are (human) verdicts about the computer as a whole (presumed infected vs. presumed clean). As part of training, the model itself learns discriminative patterns in traffic targeted to individual servers and constructs the final high-level classifier on top of them. We show the classifier to perform with very high precision, and demonstrate that the learned traffic patterns can be interpreted as Indicators of Compromise. We implement the discriminative model as a neural network with special structure reflecting two stacked multi instance problems. The main advantages of the proposed configuration include not only improved accuracy and ability to learn from gross labels, but also automatic learning of server types (together with their detectors) that are typically visited by infected computers.

The malware detection arms race involves constant change: malware changes to evade detection and labels change as detection mechanisms react. Recognizing that malware changes over time, prior work has enforced temporally consistent samples by requiring that training binaries predate evaluation binaries. We present temporally consistent labels, requiring that training labels also predate evaluation binaries since training labels collected after evaluation binaries constitute label knowledge from the future. Using a dataset containing 1.1 million binaries from over 2.5 years, we show that enforcing temporal label consistency decreases detection from 91% to 72% at a 0.5% false positive rate compared to temporal samples alone.

The impact of temporal labeling demonstrates the potential of improved labels to increase detection results. Hence, we present a detector capable of selecting binaries for submission to an expert labeler for review. At a 0.5% false positive rate, our detector achieves a 72% true positive rate without an expert, which increases to 77% and 89% with 10 and 80 expert queries daily, respectively. Additionally, we detect 42% of malicious binaries initially undetected by all 32 antivirus vendors from VirusTotal used in our evaluation. For evaluation at scale, we simulate the human expert labeler and show that our approach is robust against expert labeling errors. Our novel contributions include a scalable malware detector integrating manual review with machine learning and the examination of temporal label consistency

Mobile malware attempts to evade detection during app analysis by mimicking security-sensitive behaviors of benign apps that provide similar functionality (e.g., sending SMS mes- sages), and suppressing their payload to reduce the chance of being observed (e.g., executing only its payload at night). Since current approaches focus their analyses on the types of security- sensitive resources being accessed (e.g., network), these evasive techniques in malware make differentiating between malicious and benign app behaviors a difficult task during app analysis. We propose that the malicious and benign behaviors within apps can be differentiated based on the contexts that trigger security- sensitive behaviors, i.e., the events and conditions that cause the security-sensitive behaviors to occur. In this work, we introduce AppContext, an approach of static program analysis that extracts the contexts of security-sensitive behaviors to assist app analysis in differentiating between malicious and benign behaviors. We implement a prototype of AppContext and evaluate AppContext on 202 malicious apps from various malware datasets, and 633 benign apps from the Google Play Store. AppContext correctly identifies 192 malicious apps with 87.7% precision and 95% recall. Our evaluation results suggest that the maliciousness of a security-sensitive behavior is more closely related to the intention of the behavior (reflected via contexts) than the type of the security-sensitive resources that the behavior accesses.

With the rise in the underground Internet economy, automated malicious programs popularly known as malwares have become a major threat to computers and information systems connected to the internet. Properties such as self healing, self hiding and ability to deceive the security devices make these software hard to detect and mitigate. Therefore, the detection and the mitigation of such malicious software is a major challenge for researchers and security personals. The conventional systems for the detection and mitigation of such threats are mostly signature based systems. Major drawback of such systems are their inability to detect malware samples for which there is no signature available in their signature database. Such malwares are known as zero day malware. Moreover, more and more malware writers uses obfuscation technology such as polymorphic and metamorphic, packing, encryption, to avoid being detected by antivirus. Therefore, the traditional signature based detection system is neither effective nor efficient for the detection of zero-day malware. Hence to improve the effectiveness and efficiency of malware detection system we are using classification method based on structural information and behavioral specifications. In this paper we have used both static and dynamic analysis approaches. In static analysis we are extracting the features of an executable file followed by classification. In dynamic analysis we are taking the traces of executable files using NtTrace within controlled atmosphere. Experimental results obtained from our algorithm indicate that our proposed algorithm is effective in extracting malicious behavior of executables. Further it can also be used to detect malware variants.

Mobile malware threats (e.g., on Android) have recently become a real concern. In this paper, we evaluate the state-of-the-art commercial mobile anti-malware products for Android and test how resistant they are against various common obfuscation techniques (even with known malware). Such an evaluation is important for not only measuring the available defense against mobile malware threats, but also proposing effective, next-generation solutions. We developed DroidChameleon, a systematic framework with various transformation techniques, and used it for our study. Our results on 10 popular commercial anti-malware applications for Android are worrisome: none of these tools is resistant against common malware transformation techniques. In addition, a majority of them can be trivially defeated by applying slight transformation over known malware with little effort for malware authors. Finally, in light of our results, we propose possible remedies for improving the current state of malware detection on mobile devices.