2,999 research outputs found
Robust Recommender System: A Survey and Future Directions
With the rapid growth of information, recommender systems have become
integral for providing personalized suggestions and overcoming information
overload. However, their practical deployment often encounters "dirty" data,
where noise or malicious information can lead to abnormal recommendations.
Research on improving recommender systems' robustness against such dirty data
has thus gained significant attention. This survey provides a comprehensive
review of recent work on recommender systems' robustness. We first present a
taxonomy to organize current techniques for withstanding malicious attacks and
natural noise. We then explore state-of-the-art methods in each category,
including fraudster detection, adversarial training, certifiable robust
training against malicious attacks, and regularization, purification,
self-supervised learning against natural noise. Additionally, we summarize
evaluation metrics and common datasets used to assess robustness. We discuss
robustness across varying recommendation scenarios and its interplay with other
properties like accuracy, interpretability, privacy, and fairness. Finally, we
delve into open issues and future research directions in this emerging field.
Our goal is to equip readers with a holistic understanding of robust
recommender systems and spotlight pathways for future research and development
Machine Learning Aided Static Malware Analysis: A Survey and Tutorial
Malware analysis and detection techniques have been evolving during the last
decade as a reflection to development of different malware techniques to evade
network-based and host-based security protections. The fast growth in variety
and number of malware species made it very difficult for forensics
investigators to provide an on time response. Therefore, Machine Learning (ML)
aided malware analysis became a necessity to automate different aspects of
static and dynamic malware investigation. We believe that machine learning
aided static analysis can be used as a methodological approach in technical
Cyber Threats Intelligence (CTI) rather than resource-consuming dynamic malware
analysis that has been thoroughly studied before. In this paper, we address
this research gap by conducting an in-depth survey of different machine
learning methods for classification of static characteristics of 32-bit
malicious Portable Executable (PE32) Windows files and develop taxonomy for
better understanding of these techniques. Afterwards, we offer a tutorial on
how different machine learning techniques can be utilized in extraction and
analysis of a variety of static characteristic of PE binaries and evaluate
accuracy and practical generalization of these techniques. Finally, the results
of experimental study of all the method using common data was given to
demonstrate the accuracy and complexity. This paper may serve as a stepping
stone for future researchers in cross-disciplinary field of machine learning
aided malware forensics.Comment: 37 Page
SPARTA: A Graphical User Interface for Malicious Mobile Code Fingerprint-ing.
This thesis introduces and describes SPARTA (for Stochastic Profiling Application for the Rendering of Trees and Automata), a graphical user interface used as a front end to a collection of tools written in C that collectively convert a log of registry system calls performed by an application into binary descriptions of PSTs (for Probabilistic Suffix Trees) and PSAs (for Probabilistic Suffix Automata), which are models used to represent application behavior on Windows-based systems. SPARTA works by rendering these binary descriptions into graphical form, showcasing a variety of features intended to make the user interaction with PSTs and PSAs informative and insightful.
The ultimate goal of SPARTA is to aid in the process of profiling applications based on the system calls they make, using characteristics from PSTs and PSAs that are more easily noticeable in their graphical form to define “normal” behavior for Windows applications. With knowledge of normal behavior, these very same models can be used to measure deviations that might ultimately result in the destructive actions of malicious mobile code, enabling the user to halt or quarantine them before they take place
GRASE: Granulometry Analysis with Semi Eager Classifier to Detect Malware
Technological advancement in communication leading to 5G, motivates everyone to get connected to the internet including ‘Devices’, a technology named Web of Things (WoT). The community benefits from this large-scale network which allows monitoring and controlling of physical devices. But many times, it costs the security as MALicious softWARE (MalWare) developers try to invade the network, as for them, these devices are like a ‘backdoor’ providing them easy ‘entry’. To stop invaders from entering the network, identifying malware and its variants is of great significance for cyberspace. Traditional methods of malware detection like static and dynamic ones, detect the malware but lack against new techniques used by malware developers like obfuscation, polymorphism and encryption. A machine learning approach to detect malware, where the classifier is trained with handcrafted features, is not potent against these techniques and asks for efforts to put in for the feature engineering. The paper proposes a malware classification using a visualization methodology wherein the disassembled malware code is transformed into grey images. It presents the efficacy of Granulometry texture analysis technique for improving malware classification. Furthermore, a Semi Eager (SemiE) classifier, which is a combination of eager learning and lazy learning technique, is used to get robust classification of malware families. The outcome of the experiment is promising since the proposed technique requires less training time to learn the semantics of higher-level malicious behaviours. Identifying the malware (testing phase) is also done faster. A benchmark database like malimg and Microsoft Malware Classification challenge (BIG-2015) has been utilized to analyse the performance of the system. An overall average classification accuracy of 99.03 and 99.11% is achieved, respectively
Detection of Lying Electrical Vehicles in Charging Coordination Application Using Deep Learning
The simultaneous charging of many electric vehicles (EVs) stresses the
distribution system and may cause grid instability in severe cases. The best
way to avoid this problem is by charging coordination. The idea is that the EVs
should report data (such as state-of-charge (SoC) of the battery) to run a
mechanism to prioritize the charging requests and select the EVs that should
charge during this time slot and defer other requests to future time slots.
However, EVs may lie and send false data to receive high charging priority
illegally. In this paper, we first study this attack to evaluate the gains of
the lying EVs and how their behavior impacts the honest EVs and the performance
of charging coordination mechanism. Our evaluations indicate that lying EVs
have a greater chance to get charged comparing to honest EVs and they degrade
the performance of the charging coordination mechanism. Then, an anomaly based
detector that is using deep neural networks (DNN) is devised to identify the
lying EVs. To do that, we first create an honest dataset for charging
coordination application using real driving traces and information revealed by
EV manufacturers, and then we also propose a number of attacks to create
malicious data. We trained and evaluated two models, which are the multi-layer
perceptron (MLP) and the gated recurrent unit (GRU) using this dataset and the
GRU detector gives better results. Our evaluations indicate that our detector
can detect lying EVs with high accuracy and low false positive rate
- …