A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
Filters
CloudIDEA: A Malware Defense Architecture for Cloud Data Centers
[chapter]
2015
Lecture Notes in Computer Science
Preserved Fulltext
Due to the proliferation of cloud computing, cloud-based systems are becoming an increasingly attractive target for malware. ...
In an Infrastructure-as-a-Service (IaaS) cloud, malware located in a customer's virtual machine (VM) affects not only this customer, but may also attack the cloud infrastructure and other co-hosted customers ...
To our best knowledge, however, there is no solution that combines stealthy intrusion detection, comprehensive evidence collection and indepth automated malware analysis in a joint architecture for cloud-based ...
doi:10.1007/978-3-319-26148-5_40
fatcat:mk7ojldplzdvtm5vvid6bmnxj4
Detecting Stealthy Domain Generation Algorithms Using Heterogeneous Deep Neural Network Framework
2020
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
HDNN employs a proposed improved parallel CNN (IPCNN) architecture with multisizes of convolution kernel for extracting multi-scale local features from a domain name. ...
In recent years, stealthy domain generation algorithms (SDGA) have been proposed and revealed significantly stronger stealthiness comparing to the traditional character-based DGA. ...
This framework can extract effective
character-level local features and global features,
which can be used for more accurate detection and
classification. ...
doi:10.1109/access.2020.2988877
fatcat:lyxrcqalwneprdiejbypru7w7y
Survey on Botnet Detection Techniques: Classification, Methods, and Evaluation
2021
Mathematical Problems in Engineering
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Finally, the challenges and future trends in the field of botnet detection are summarized. ...
It studies the mechanism characteristics of botnet architecture, life cycle, and command and control channel and provides a classification of botnet detection techniques. ...
Acknowledgments is paper was supported by the National Key Research and Development Project (2016YFB08011601). e authors would like to acknowledge the support. ...
doi:10.1155/2021/6640499
fatcat:hkafnnj2cnbzjdbuk6iel3b5cm
Tight Arms Race: Overview of Current Malware Threats and Trends in Their Detection
2020
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
That is why, in this paper we first perform a detailed meta-review of the existing surveys related to malware and its detection techniques, showing an arms race between these two sides of a barricade. ...
At present, the complexity, proliferation, and variety of malware pose a real challenge for the existing countermeasures and require their constant improvements. ...
The authors proposed a new method that used space filling curve mapping (SFCM) to visualize malware, extracted image features by a CNN, and classified the images using a SVM. ...
doi:10.1109/access.2020.3048319
fatcat:tatdk6pzczgp3aylvbxoxabuta
Learning Representations for Log Data in Cybersecurity
[chapter]
2017
Lecture Notes in Computer Science
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
The presented framework uses a divide-and-conquer strategy combining behavioral analytics, time series modeling and representation learning algorithms to model large volumes of data. ...
We demonstrate the approach with a novel dataset extracted from 3 billion log lines generated at an enterprise network boundaries with reported command and control communications. ...
To build the dataset, the authors combined traces extracted from the ISOT botnet detection dataset [27] , the ISCX 2012 IDS dataset [18] , and traffic generated by the Malware Capture Facility Project ...
doi:10.1007/978-3-319-60080-2_19
fatcat:mp52smh2z5alvdlwkppzcns2by
Machine Learning in IoT Security: Current Solutions and Future Challenges
[article]
2019
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
At last, based on the detailed investigation of the existing solutions in the literature, we discuss the future research directions for ML- and DL-based IoT security. ...
Therefore, Machine Learning (ML) and Deep Learning (DL) techniques, which are able to provide embedded intelligence in the IoT devices and networks, are leveraged to cope with different security problems ...
[77] used ensemble supervised learning technique with random forest classifier to detect android-based malware. ...
arXiv:1904.05735v1
fatcat:k5v6zad7lfhdrjngjmxgroafz4
Detection of DGA-Generated Domain Names with TF-IDF
2022
Electronics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
We next propose the use of TF-IDF to measure frequencies of the most relevant n-grams in domain names, and use these as features in learning algorithms. ...
We perform experiments with various machine-learning and deep-learning models using TF-IDF features, of which a deep MLP model yields the best results. ...
Acknowledgments: We kindly thank IT and Facility Services at Open Universiteit and SURF for providing the compute servers for performing our experiments. ...
doi:10.3390/electronics11030414
fatcat:ykcmtt6v2fdz5lhvntgbdwfdta
Two Sides of the Same Coin: Boons and Banes of Machine Learning in Hardware Security
2021
IEEE Journal on Emerging and Selected Topics in Circuits and Systems
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
ML schemes have been extensively used to enhance the security and trust of embedded systems like hardware Trojans and malware detection. ...
On the other hand, ML-based approaches have also been adopted by adversaries to assist side-channel attacks, reverse engineer integrated circuits and break hardware security primitives like Physically ...
By passing the features through the hidden layers of the neural network, relevant features will be extracted and used to train the classifier and enhance the classification accuracy. ...
doi:10.1109/jetcas.2021.3084400
fatcat:c4wdkghpo5fwbhvkekaysnahzm
Forensics and Deep Learning Mechanisms for Botnets in Internet of Things: A Survey of Challenges and Solutions
2019
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
With a wide variety of applications, such as home automation, smart grids/cities, and critical infrastructure management, the IoT systems make compelling targets for cyber-attacks. ...
Since IoT enabled botnets are scalable, technologically diverse and make use of current high-speed networks, developing forensic mechanisms capable of investigating the IoT Botnet activities has become ...
Their task was to create a user-friendly interface, where data was uploaded to the system, pre-processed with Weka and after the feature-extraction process, NaÃŕve Bayes and SVM classifiers were built. ...
doi:10.1109/access.2019.2916717
fatcat:a3w7kzvdlvh3hcjqri3uoyqsbu
Towards Optimal LSTM Neural Networks for Detecting Algorithmically Generated Domain Names
2021
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Malware developers can modify the way DGA-based domain names are generated, so the features extracted from the previous feature engineering process are no longer useful. ...
First, an Improved Parallel CNN (IPCNN) architecture that uses multiple CNNs with different kernel sizes combined together to extract local features at different scales. ...
doi:10.1109/access.2021.3111307
fatcat:j5mkihonpva7zbvr7wjro3jzcu
A critical review of intrusion detection systems in the internet of things: techniques, deployment strategy, validation strategy, attacks, public datasets and challenges
2021
Cybersecurity
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
To this end, Numerous IoT intrusion detection Systems (IDS) have been proposed in the literature to tackle attacks on the IoT ecosystem, which can be broadly classified based on detection technique, validation ...
IDS and detection capabilities to detect IoT attacks. ...
Informatics and Applied Optimization (CIAO) for their support. ...
doi:10.1186/s42400-021-00077-7
fatcat:32nrdpgvkjg4ljjxc44rewc55y
Featureless discovery of correlated and false intrusion alerts
2020
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
His research interests include network security, traffic anomaly detection, and malware detection based on network traffic analysis. ...
Malware and cyber-attacks cause substantial damage to corporations. A common countermeasure is Intrusion Detection Systems (IDSs). ...
SECTION SUMMARY To summarise, we have presented a general approach for extracting useful information from IDS alerts, to automate correlation and filtering without any feature engineering or expert knowledge ...
doi:10.1109/access.2020.3001374
fatcat:2g4mzfnflfd37e3jezvubqw6tm
Malware Threat Affecting Financial Organization Analysis Using Machine Learning Approach
2022
International Journal of Information Technology and Web Engineering
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Using network packet (computation) identifiers, the Random Forest classifier detects emotet-based flows with 99.9726 percent precision and a 92.3 percent true positive rating. ...
Without analyzing network packet payload computing (PPC), IP address labels, port number traces, or protocol knowledge, we have used Machine Learning (ML) modeling to detect Emotet malware infections and ...
for detecting Analysis of Malware Families 2017) malware-generated domain names using RNN (recurrent neural networks). ...
doi:10.4018/ijitwe.304051
fatcat:c3xn53dyovd2zh3hnk44yrvxey
Bringing a GAN to a Knife-Fight: Adapting Malware Communication to Avoid Detection
2018
2018 IEEE Security and Privacy Workshops (SPW)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
A real-life scenario was successfully implemented using the Stratosphere behavioral IPS in a router, while the malware and the GAN were deployed in the local network of our laboratory, and the C2 server ...
In this way, it was possible to avoid the detection of new-generation Intrusion Prevention Systems that use machine learning and behavioral characteristics. ...
It does not perform Deep Packet Inspection (DPI) or any other signature based detection.
B. GAN Both the generator and the discriminator are Recurrent Neural Networks (RNNs). ...
doi:10.1109/spw.2018.00019
dblp:conf/sp/RigakiG18
fatcat:ggt7letsu5fgpmrfc7ocn52wbq
Machine Learning for Security and the Internet of Things: the Good, the Bad, and the Ugly
2019
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
In detail, we consider the numerous benefits (good use) that machine learning has brought, both in general, and specifically for security and CPS/IoT, such as the improvement of intrusion detection mechanisms ...
The advancement of the Internet of Things (IoT) has allowed for unprecedented data collection, automation, and remote sensing and actuation, transforming autonomous systems and bringing smart command and ...
Furthermore, the approach rebuilds the data features by using the distances, and formats the data features as a k-Nearest Neighbor (k-NN) classifier. ...
doi:10.1109/access.2019.2948912
fatcat:wxd6imn62fgufdmfh3gtaijeru
« Previous
Showing results 1 — 15 out of 60 results