Papers Computer Security
“Computer Security” 태그가 달린 논문 70편 · 필터 해제
Dos and Don'ts of Machine Learning in Computer Security
With the growing processing power of computing systems and the increasing availability of massive datasets, machine learning algorithms have led to major breakthroughs in many different areas. This development has influe…
BIG-bench Machine LearningComputer SecurityMalware DetectionDetecting malicious PDF using CNN
Malicious PDF files represent one of the biggest threats to computer security. To detect them, significant research has been done using handwritten signatures or machine learning based on manual feature extraction. Those…
ClusteringComputer SecurityGenerating Adversarial Examples with an Optimized Quality
Deep learning models are widely used in a range of application areas, such as computer vision, computer security, etc. However, deep learning models are vulnerable to Adversarial Examples (AEs),carefully crafted samples …
Adversarial AttackComputer SecurityIReEn: Reverse-Engineering of Black-Box Functions via Iterative Neural Program Synthesis
In this work, we investigate the problem of revealing the functionality of a black-box agent. Notably, we are interested in the interpretable and formal description of the behavior of such an agent. Ideally, this descrip…
Computer SecurityProgram SynthesisExploring Optimal Deep Learning Models for Image-based Malware Variant Classification
Analyzing a huge amount of malware is a major burden for security analysts. Since emerging malware is often a variant of existing malware, automatically classifying malware into known families greatly reduces a part of t…
ClassificationComputer SecurityDeep LearningGeneral Classification+3The Offense-Defense Balance of Scientific Knowledge: Does Publishing AI Research Reduce Misuse?
There is growing concern over the potential misuse of artificial intelligence (AI) research. Publishing scientific research can facilitate misuse of the technology, but the research can also contribute to protections aga…
Computer SecurityCoda: An End-to-End Neural Program Decompiler
Reverse engineering of binary executables is a critical problem in the computer security domain. On the one hand, malicious parties may recover interpretable source codes from the software products to gain commercial adv…
Computer SecurityMalware DetectionA Neural-based Program Decompiler
Reverse engineering of binary executables is a critical problem in the computer security domain. On the one hand, malicious parties may recover interpretable source codes from the software products to gain commercial adv…
Computer SecurityMalware DetectionEvaluating Explanation Methods for Deep Learning in Security
Deep learning is increasingly used as a building block of security systems. Unfortunately, neural networks are hard to interpret and typically opaque to the practitioner. The machine learning community has started to add…
Computer SecurityDeep LearningMalware DetectionOpen-Ended Question AnsweringDefending Against Neural Fake News
Recent progress in natural language generation has raised dual-use concerns. While applications like summarization and translation are positive, the underlying technology also might enable adversaries to generate neural …
Computer SecurityFake News DetectionText GenerationArtificial Consciousness and Security
This paper describes a possible way to improve computer security by implementing a program which implements the following three features related to a weak notion of artificial consciousness: (partial) self-monitoring, ab…
Computer SecurityMaximal Divergence Sequential Autoencoder for Binary Software Vulnerability Detection
Due to the sharp increase in the severity of the threat imposed by software vulnerabilities, the detection of vulnerabilities in binary code has become an important concern in the software industry, such as the embedded …
Computer SecurityVulnerability DetectionEffectiveness of Tree-based Ensembles for Anomaly Discovery: Insights, Batch and Streaming Active Learning
In many real-world AD applications including computer security and fraud prevention, the anomaly detector must be configurable by the human analyst to minimize the effort on false positives. One important way to configur…
Active LearningAnomaly DetectionComputer SecurityDrift DetectionPoisoning Behavioral Malware Clustering
Clustering algorithms have become a popular tool in computer security to analyze the behavior of malware variants, identify novel malware families, and generate signatures for antivirus systems. However, the suitability …
ClusteringComputer SecurityMalware ClusteringActive Anomaly Detection via Ensembles
In critical applications of anomaly detection including computer security and fraud prevention, the anomaly detector must be configurable by the analyst to minimize the effort on false positives. One important way to con…
Active LearningAnomaly DetectionComputer SecurityDrift DetectionRobust Neural Malware Detection Models for Emulation Sequence Learning
Malicious software, or malware, presents a continuously evolving challenge in computer security. These embedded snippets of code in the form of malicious files or hidden within legitimate files cause a major risk to syst…
Computer SecurityMalware ClassificationMalware DetectionNon-deterministic Behavior of Ranking-based Metrics when Evaluating Embeddings
Embedding data into vector spaces is a very popular strategy of pattern recognition methods. When distances between embeddings are quantized, performance metrics become ambiguous. In this paper, we present an analysis of…
Computer SecurityDetecting Denial-of-Service Attacks from Social Media Text: Applying NLP to Computer Security
This paper describes a novel application of NLP models to detect denial of service attacks using only social media as evidence. Individual networks are often slow in reporting attacks, so a detection system from public d…
Computer SecurityGenerative Models for Spear Phishing Posts on Social Media
Historically, machine learning in computer security has prioritized defense: think intrusion detection systems, malware classification, and botnet traffic identification. Offense can benefit from data just as well. Socia…
ClusteringComputer SecurityIntrusion DetectionMalware ClassificationReservoir of Diverse Adaptive Learners and Stacking Fast Hoeffding Drift Detection Methods for Evolving Data Streams
The last decade has seen a surge of interest in adaptive learning algorithms for data stream classification, with applications ranging from predicting ozone level peaks, learning stock market indicators, to detecting com…
Computer SecurityDrift Detection