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Found 55 papers, 13 papers with code
Can LLMs Understand Computer Networks? Towards a Virtual System Administrator
Private LLMs achieve noteworthy results in small and medium networks, while challenges persist in comprehending complex network topologies, particularly for open-source models.
Federated Learning Under Attack: Exposing Vulnerabilities through Data Poisoning Attacks in Computer Networks
In LF, we randomly flipped the labels of benign data and trained the model on the manipulated data.
"All of Me": Mining Users' Attributes from their Public Spotify Playlists
In this work, we investigate the relationship between Spotify users' attributes and their public playlists.
Privacy-Preserving in Blockchain-based Federated Learning Systems
Federated Learning (FL) has recently arisen as a revolutionary approach to collaborative training Machine Learning models.
Topology-Based Reconstruction Prevention for Decentralised Learning
Specifically, we show that reconstruction requires a number of adversaries linear in the length of the network's shortest cycle.
Dr. Jekyll and Mr. Hyde: Two Faces of LLMs
This work shows that by using adversarial personas, one can overcome safety mechanisms set out by ChatGPT and Bard.
Invisible Threats: Backdoor Attack in OCR Systems
Optical Character Recognition (OCR) is a widely used tool to extract text from scanned documents.
AGIR: Automating Cyber Threat Intelligence Reporting with Natural Language Generation
In this paper, we introduce AGIR (Automatic Generation of Intelligence Reports), a transformative Natural Language Generation tool specifically designed to address the pressing challenges in the realm of CTI reporting.
Your Battery Is a Blast! Safeguarding Against Counterfeit Batteries with Authentication
By using our proposed methodologies, manufacturers can ensure that devices only use legitimate batteries, guaranteeing the operational state of any system and safety measures for the users.
Label Inference Attacks against Node-level Vertical Federated GNNs
In this work, we are the first (to the best of our knowledge) to investigate label inference attacks on VFL using a zero-background knowledge strategy.
Your Attack Is Too DUMB: Formalizing Attacker Scenarios for Adversarial Transferability
An alarming side-effect of evasion attacks is their ability to transfer among different models: this property is called transferability.
Turning Privacy-preserving Mechanisms against Federated Learning
Recently, researchers have successfully employed Graph Neural Networks (GNNs) to build enhanced recommender systems due to their capability to learn patterns from the interaction between involved entities.
Boosting Big Brother: Attacking Search Engines with Encodings
Search engines are vulnerable to attacks against indexing and searching via text encoding manipulation.
Spritz-PS: Validation of Synthetic Face Images Using a Large Dataset of Printed Documents
To highlight the problems involved with the evaluation of the dataset's IRIS images, we conducted a large number of analyses employing Siamese Neural Networks to assess the similarities between genuine and synthetic human IRISes, such as ResNet50, Xception, VGG16, and MobileNet-v2.
Social Honeypot for Humans: Luring People through Self-managed Instagram Pages
However, by choosing an appropriate content topic, this attractive mechanism could be extended to any OSN users, rather than only luring malicious actors.
STIXnet: A Novel and Modular Solution for Extracting All STIX Objects in CTI Reports
The automatic extraction of information from Cyber Threat Intelligence (CTI) reports is crucial in risk management.
Cryptocurrency wallets: assessment and security
Digital wallet as a software program or a digital device allows users to conduct various transactions.
SoK: A Systematic Evaluation of Backdoor Trigger Characteristics in Image Classification
Nevertheless, it is vulnerable to backdoor attacks that modify the training set to embed a secret functionality in the trained model.
Temporal Dynamics of Coordinated Online Behavior: Stability, Archetypes, and Influence
Large-scale online campaigns, malicious or otherwise, require a significant degree of coordination among participants, which sparked interest in the study of coordinated online behavior.
Follow Us and Become Famous! Insights and Guidelines From Instagram Engagement Mechanisms
Thanks to our interpretable approaches, we conclude by outlining guidelines for creating successful posts.
Going In Style: Audio Backdoors Through Stylistic Transformations
This work explores stylistic triggers for backdoor attacks in the audio domain: dynamic transformations of malicious samples through guitar effects.
On the Vulnerability of Data Points under Multiple Membership Inference Attacks and Target Models
Membership Inference Attacks (MIAs) infer whether a data point is in the training data of a machine learning model.
Multi-SpacePhish: Extending the Evasion-space of Adversarial Attacks against Phishing Website Detectors using Machine Learning
By considering the application of ML for Phishing Website Detection (PWD), we formalize the "evasion-space" in which an adversarial perturbation can be introduced to fool a ML-PWD -- demonstrating that even perturbations in the "feature-space" are useful.
Attribute Inference Attacks in Online Multiplayer Video Games: a Case Study on Dota2
This paper is the first to investigate such a problem.
Employing Deep Ensemble Learning for Improving the Security of Computer Networks against Adversarial Attacks
In this study, we present a novel architecture based on an ensemble classifier that combines the enhanced security of 1-Class classification (known as 1C) with the high performance of conventional 2-Class classification (known as 2C) in the absence of attacks. Our architecture is referred to as the 1. 5-Class (SPRITZ-1. 5C) classifier and constructed using a final dense classifier, one 2C classifier (i. e., CNNs), and two parallel 1C classifiers (i. e., auto-encoders).
Resisting Deep Learning Models Against Adversarial Attack Transferability via Feature Randomization
Our novel approach consists of changing the training strategy in the target network classifier and selecting random feature samples.
Label-Only Membership Inference Attack against Node-Level Graph Neural Networks
Even in those scenarios, our label-only MIA achieves a better attack performance in most cases.
An Adversarial Attack Analysis on Malicious Advertisement URL Detection Framework
In this study, we extract a novel set of lexical and web-scrapped features and employ machine learning technique to set up system for fraudulent advertisement URLs detection.
Real or Virtual: A Video Conferencing Background Manipulation-Detection System
Additionally, it enables users to employ a virtual background to conceal their own environment due to privacy concerns or to reduce distractions, particularly in professional settings.
The Cross-evaluation of Machine Learning-based Network Intrusion Detection Systems
By using XeNIDS on six well-known datasets, we demonstrate the concealed potential, but also the risks, of cross-evaluations of ML-NIDS.
Dynamic Backdoors with Global Average Pooling
It was recently shown that countermeasures in image classification, like Neural Cleanse and ABS, could be bypassed with dynamic triggers that are effective regardless of their pattern and location.
Detecting High-Quality GAN-Generated Face Images using Neural Networks
On the other hand, the recent development of GAN models may create high-quality face images without evidence of spatial artifacts.
Captcha Attack: Turning Captchas Against Humanity
Content moderators constantly monitor these online platforms to prevent the spreading of inappropriate content (e. g., hate speech, nudity images).
Hand Me Your PIN! Inferring ATM PINs of Users Typing with a Covered Hand
We consider the setting where the attacker can access an ATM PIN pad of the same brand/model as the target one.
Demystifying the Transferability of Adversarial Attacks in Computer Networks
In this paper, we provide the first comprehensive study which assesses the robustness of CNN-based models for computer networks against adversarial transferability.
The Spread of Propaganda by Coordinated Communities on Social Media
Large-scale manipulations on social media have two important characteristics: (i) use of propaganda to influence others, and (ii) adoption of coordinated behavior to spread it and to amplify its impact.
Can You Hear It? Backdoor Attacks via Ultrasonic Triggers
This work explores backdoor attacks for automatic speech recognition systems where we inject inaudible triggers.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Do Not Deceive Your Employer with a Virtual Background: A Video Conferencing Manipulation-Detection System
On the other hand, users maybe want to fool people in the meeting by considering the virtual background to conceal where they are.
Fall of Giants: How popular text-based MLaaS fall against a simple evasion attack
The increased demand for machine learning applications made companies offer Machine-Learning-as-a-Service (MLaaS).
UAVs Path Deviation Attacks: Survey and Research Challenges
No specific attacks and defenses have been found in literature for GNSS+ or for UAVs moving in group without a pre-ordered arrangement.
Cryptography and Security
A Survey on Industrial Control System Testbeds and Datasets for Security Research
In dealing with this security requirement, the research community focuses on developing new security mechanisms such as Intrusion Detection Systems (IDSs), facilitated by leveraging modern machine learning techniques.
Intrusion Detection
Cryptography and Security
A Machine Learning-based Approach to Detect Threats in Bio-Cyber DNA Storage Systems
The similarities between these biological media and classical ones can also be a drawback, as malicious parties might replicate traditional attacks on the former archival system, using biological instruments and techniques.
Assessing the Use of Insecure ICS Protocols via IXP Network Traffic Analysis
Therefore, Shodan do not allow to understand the actual use of insecure industrial protocols on the Internet and the current security practices in ICS communications.
Cryptography and Security Networking and Internet Architecture
Similarity-based Android Malware Detection Using Hamming Distance of Static Binary Features
We test our experiments in a different type of features: API, intent, and permission features on these three datasets.
On Defending Against Label Flipping Attacks on Malware Detection Systems
Our evaluation shows that using random forest feature selection and varying ratios of features can result in an improvement of up to 19\% accuracy when compared with the state-of-the-art method in the literature.
Can Machine Learning Model with Static Features be Fooled: an Adversarial Machine Learning Approach
We also test our methods using various classifier algorithms and compare them with the state-of-the-art data poisoning method using the Jacobian matrix.
PILOT: Password and PIN Information Leakage from Obfuscated Typing Videos
This paper studies leakage of user passwords and PINs based on observations of typing feedback on screens or from projectors in the form of masked characters that indicate keystrokes.
Cryptography and Security K.6.5
FeatureAnalytics: An approach to derive relevant attributes for analyzing Android Malware
Ever increasing number of Android malware, has always been a concern for cybersecurity professionals.
All You Need is "Love": Evading Hate-speech Detection
With the spread of social networks and their unfortunate use for hate speech, automatic detection of the latter has become a pressing problem.
Peek-a-Boo: I see your smart home activities, even encrypted!
It is realized utilizing state-of-the-art machine-learning approaches for detecting and identifying particular types of IoT devices, their actions, states, and ongoing user activities in a cascading style by only observing passively the wireless traffic from smart home devices.
Cryptography and Security
Internet of Things Security and Forensics: Challenges and Opportunities
The Internet of Things (IoT) envisions pervasive, connected, and smart nodes interacting autonomously while offering all sorts of services.
Cryptography and Security
Forensics Analysis of Android Mobile VoIP Apps
Voice over Internet Protocol (VoIP) applications (apps) provide convenient and low cost means for users to communicate and share information with each other in real-time.
Cryptography and Security
Breaking Fitness Records without Moving: Reverse Engineering and Spoofing Fitbit
In this paper, we provide an in-depth security analysis of the operation of fitness trackers commercialized by Fitbit, the wearables market leader.
Cryptography and Security
Android Code Protection via Obfuscation Techniques: Past, Present and Future Directions
We believe that, there is a need to investigate efficiency of the defense techniques used for code protection.
Cryptography and Security
ODIN: Obfuscation-based privacy preserving consensus algorithm for Decentralized Information fusion in smart device Networks
ODIN is a privacy-preserving extension of the popular consensus gossip algorithm, that prevents distributed agents have direct access to the data while they iteratively reach consensus; agents cannot access even the final consensus value, but can only retrieve partial information, e. g., a binary decision.
Cryptography and Security
