A Multi-view Context-aware Approach to Android Malware Detection and Malicious Code Localization

Existing Android malware detection approaches use a variety of features such as security sensitive APIs, system calls, control-flow structures and information flows in conjunction with Machine Learning classifiers to achieve accurate detection. Each of these feature sets provides a unique semantic perspective (or view) of apps' behaviours with inherent strengths and limitations. Meaning, some views are more amenable to detect certain attacks but may not be suitable to characterise several other attacks. Most of the existing malware detection approaches use only one (or a selected few) of the aforementioned feature sets which prevent them from detecting a vast majority of attacks. Addressing this limitation, we propose MKLDroid, a unified framework that systematically integrates multiple views of apps for performing comprehensive malware detection and malicious code localisation. The rationale is that, while a malware app can disguise itself in some views, disguising in every view while maintaining malicious intent will be much harder. MKLDroid uses a graph kernel to capture structural and contextual information from apps' dependency graphs and identify malice code patterns in each view. Subsequently, it employs Multiple Kernel Learning (MKL) to find a weighted combination of the views which yields the best detection accuracy. Besides multi-view learning, MKLDroid's unique and salient trait is its ability to locate fine-grained malice code portions in dependency graphs (e.g., methods/classes). Through our large-scale experiments on several datasets (incl. wild apps), we demonstrate that MKLDroid outperforms three state-of-the-art techniques consistently, in terms of accuracy while maintaining comparable efficiency. In our malicious code localisation experiments on a dataset of repackaged malware, MKLDroid was able to identify all the malice classes with 94% average recall

Tiresias: Predicting Security Events Through Deep Learning

With the increased complexity of modern computer attacks, there is a need for defenders not only to detect malicious activity as it happens, but also to predict the specific steps that will be taken by an adversary when performing an attack. However this is still an open research problem, and previous research in predicting malicious events only looked at binary outcomes (e.g., whether an attack would happen or not), but not at the specific steps that an attacker would undertake. To fill this gap we present Tiresias, a system that leverages Recurrent Neural Networks (RNNs) to predict future events on a machine, based on previous observations. We test Tiresias on a dataset of 3.4 billion security events collected from a commercial intrusion prevention system, and show that our approach is effective in predicting the next event that will occur on a machine with a precision of up to 0.93. We also show that the models learned by Tiresias are reasonably stable over time, and provide a mechanism that can identify sudden drops in precision and trigger a retraining of the system. Finally, we show that the long-term memory typical of RNNs is key in performing event prediction, rendering simpler methods not up to the task

Survey of Machine Learning Techniques for Malware Analysis

Coping with malware is getting more and more challenging, given their relentless growth in complexity and volume. One of the most common approaches in literature is using machine learning techniques, to automatically learn models and patterns behind such complexity, and to develop technologies for keeping pace with the speed of development of novel malware. This survey aims at providing an overview on the way machine learning has been used so far in the context of malware analysis. We systematize surveyed papers according to their objectives (i.e., the expected output, what the analysis aims to), what information about malware they specifically use (i.e., the features), and what machine learning techniques they employ (i.e., what algorithm is used to process the input and produce the output). We also outline a number of problems concerning the datasets used in considered works, and finally introduce the novel concept of malware analysis economics, regarding the study of existing tradeoffs among key metrics, such as analysis accuracy and economical costs

Malware Detection Techniques based on Machine Learning

Artificial intelligence and machine learning have become crucial tools in the fight against cyber attacks. With the constant evolution of technology, traditional methods of protecting networks are no longer enough. This is where AI and machine learning come into play, by analyzing vast amounts of data and detecting patterns or anomalies that might indicate a potential threat. This paper aims at understanding and analyzing the implementation of Artificial Intelligence (AI) and Machine Learning (ML) systems in enhancing cyber security. By detecting patterns and anomalies in network traffic, AI algorithms can quickly identify potential threats and reduce response time, far surpassing human capabilities. This not only saves valuable time and resources for organizations but also improves overall protection against cyber-attacks. As technology continues to advance, it is crucial that we leverage AI for cybersecurity to stay ahead in the fight against malicious actors. With proper utilization of AI and ML technologies, we can ensure a safer digital future for all users.

Malware Detection and Analysis Tools

The huge amounts of data and information that need to be analyzed for possible malicious intent are one ofthe big and significant challenges that the Web faces today. Malicious software, also referred to as malware developed by attackers, is polymorphic and metamorphic in nature which can modify the code as it spreads.In addition, the diversity and volume of their variants severely undermine the effectiveness of traditional defenses that typically use signature-based techniques and are unable to detect malicious executables previously unknown. Malware family variants share typical patterns of behavior that indicate their origin and purpose. The behavioral trends observed either statically or dynamically can be manipulated by usingmachine learning techniques to identify and classify unknown malware into their established families. Thissurvey paper gives an overview of the malware detection and analysis techniques and tools