Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 51 for “"malware detection"”.
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Towards robust malware detection
A central challenge of malware detection using machine learning methods is the presence of adversarial variants, small changes to detectable malware that allow it to evade a model (i.e. be classified as benign). We take inspiration from adversarial variant generation methods in the …
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Machine Learning Interpretability in Malware Detection
… with increasingly complex applications, such as malware detection. Recently, malware authors have become increasingly successful in bypassing traditional malware detection methods, partly due to advanced evasion techniques such as obfuscation and server-side polymorphism. Further, new programming …
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MDEA : malware detection with evolutionary adversarial learning
… have used machine learning as a tool to detect malware. These applications take in raw or processed binary data to feed neural network models to classify benign or malicious files. Even though this approach has proved effective against dynamic changes, such as encrypting, obfuscating and packing …
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Machine Learning Methods for Hardware-Based Malware Detection
L'abstract è presente nell'allegato / the abstract is in the attachment
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Feature Selection and Improving Classification Performance for Malware Detection
… on machine learning algorithms for detecting malware. However, these methods require significant amount of extracted features for correct malware classification, making that feature extraction, training, and testing take significant time; even more, it has been unexplored which are the most …
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Network-based advanced malware detection using multi-classifier machine learning
… evolved in persistence and sophistication. Malware has been the primary choice of weapon to carry out various cyberattacks. Host-based malware detection, as the primary line of defence, evolved into the \Achilles Heel". In particular, the increase of security-aware targeted attacks, …
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Design and implementation of robust systems for secure malware detection
Malicious software (malware) have significantly increased in terms of number and effectiveness during the past years. Until 2006, such software were mostly used to disrupt network infrastructures or to show coders’ skills. Nowadays, malware constitute a very important source of economical profit, …
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Fast and Accurate Machine Learning-based Malware Detection via RC4 Ciphertext Analysis
<p>Malware is dramatically increasing its viability while hiding its malicious intent and/or behavior by employing ciphers. So far, many efforts have been made to detect malware and prevent it from damaging users by monitoring network packets. However, conventional detection schemes analyzing …
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Rethinking the Weakness of Stream Ciphers and Its Application to Encrypted Malware Detection
… may be utilized to detect a specific type of malware that exploits a stream cipher with a stored key to encrypt or obfuscate its activity. Finally, using real-world example of this type of malware, it is shown that the scheme is capable of detecting packets sent by the DarkComet remote access …
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Discovery of Triggering Relations and Its Applications in Network Security and Android Malware Detection
An increasing variety of malware, including spyware, worms, and bots, threatens data confidentiality and system integrity on computing devices ranging from backend servers to mobile devices. To address these threats, exacerbated by dynamic network traffic patterns and growing volumes, network …
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A Heuristic Featured Based Quantification Framework for Efficient Malware Detection. Measuring the Malicious intent of a file using anomaly probabilistic scoring and evidence combinational theory with fuzzy hashing for malware detection in Portable Executable files
Malware is still one of the most prominent vectors through which computer networks and systems are compromised. A compromised computer system or network provides data and or processing resources to the world of cybercrime. With cybercrime projected to cost the world $6 trillion by 2021, malware is …
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A defensive strategy for detecting targeted adversarial poisoning attacks in machine learning trained malware detection models
… of technology through automation of feature detection which previously required human input. However, machine learning algorithms are susceptible to a variety of adversarial attacks, which allow an attacker to manipulate the machine learning model into performing an unwanted action, such as …
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Feature selection to enhance android malware detection using modified term frequency-inverse document frequency (MTF-IDF)
… (TF-IDF) as the main algorithm in Android malware detection. The TF-IDF algorithm is used to filter Android features filtered before detection process. However, IDF is unaware to the training class labels and gives incorrect weight value to some features. Therefore, the proposed approach …
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A Cloud-Based Intelligent and Energy Efficient Malware Detection Framework. A Framework for Cloud-Based, Energy Efficient, and Reliable Malware Detection in Real-Time Based on Training SVM, Decision Tree, and Boosting using Specified Heuristics Anomalies of Portable Executable Files
… to cyber-attacks prove the substantial growth of malware and their lethal proliferation techniques. Every successful malware attack highlights the weaknesses in the defence mechanisms responsible for securing the targeted computer or a network. The recent cyber-attacks reveal the presence of …
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A Cloud-Based Intelligent and Energy Efficient Malware Detection Framework. A Framework for Cloud-Based, Energy Efficient, and Reliable Malware Detection in Real-Time Based on Training SVM, Decision Tree, and Boosting using Specified Heuristics Anomalies of Portable Executable Files
… to cyber-attacks prove the substantial growth of malware and their lethal proliferation techniques. Every successful malware attack highlights the weaknesses in the defence mechanisms responsible for securing the targeted computer or a network. The recent cyber-attacks reveal the presence of …
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Learning-based Cyber Security Analysis and Binary Customization for Security
This thesis presents machine-learning based malware detection and post-detection rewriting techniques for mobile and web security problems. In mobile malware detection, we focus on detecting repackaged mobile malware. We design and demonstrate an Android repackaged malware detection technique based …
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Pattern recognition for computer security
Computer Security deals with the detection and mitigation of threats to computer networks, data, and computing hardware. This thesis addresses the following two computer security problems: email spam campaign and malware detection. Email spam campaigns can easily be generated using popular …
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Efficient resolution of security-sensitive values in Android using abstract interpretation
… an important component of DroidSafe, an Android malware detection system designed to prove important properties of sensitive program behaviors before the programs appear in an application marketplace. The resolved program values provide important context for other DroidSafe analyses and the …
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Towards an optimised and adaptive automation of post-incident malware investigation: a novel reinforcement learning framework
… cybersecurity, the increasing sophistication of malware necessitates dynamic and adaptive solutions for effective post-incident investigations. Existing malware detection frameworks, predominantly reliant on heuristic and signature-based techniques, exhibit significant limitations in identifying …
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Adversarial-resilience assurance for mobile security systems
… attacks. In particular, we use mobile malware detection as a representative of security systems for our investigation. To show how a malware detection approach can be enhanced by intelligent techniques such as machine learning and static program analysis, we propose AppContext, an …
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