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CloudIDEA: A Malware Defense Architecture for Cloud Data Centers [chapter]

Andreas Fischer, Thomas Kittel, Bojan Kolosnjaji, Tamas K. Lengyel, Waseem Mandarawi, Hermann de Meer, Tilo Müller, Mykola Protsenko, Hans P. Reiser, Benjamin Taubmann, Eva Weishäupl
2015 Lecture Notes in Computer Science  
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

Luhui Yang, Guangjie Liu, Yuewei Dai, Jinwei Wang, Jiangtao Zhai
2020 IEEE Access  
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

Ying Xing, Hui Shu, Hao Zhao, Dannong Li, Li Guo, Jude Hemanth
2021 Mathematical Problems in Engineering  
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

Luca Caviglione, Michal Choras, Igino Corona, Artur Janicki, Wojciech Mazurczyk, Marek Pawlicki, Katarzyna Wasielewska
2020 IEEE Access  
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]

Ignacio Arnaldo, Alfredo Cuesta-Infante, Ankit Arun, Mei Lam, Costas Bassias, Kalyan Veeramachaneni
2017 Lecture Notes in Computer Science  
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]

Fatima Hussain, Rasheed Hussain, Syed Ali Hassan, Ekram Hossain
2019 arXiv   pre-print
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

Harald Vranken, Hassan Alizadeh
2022 Electronics  
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

Wenye Liu, Chip-Hong Chang, Xueyang Wang, Chen Liu, Jason Fung, Mohammad Ebrahimabadi, Naghmeh Karimi, Xingyu Meng, Kanad Basu
2021 IEEE Journal on Emerging and Selected Topics in Circuits and Systems  
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

Nickolaos Koroniotis, Nour Moustafa, Elena Sitnikova
2019 IEEE Access  
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

Jose Selvi, Ricardo J. Rodriguez, Emilio Soria-Olivas
2021 IEEE Access  
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

Ansam Khraisat, Ammar Alazab
2021 Cybersecurity  
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

Egon Kidmose, Matija Stevanovic, Soren Brandbyge, Jens M. Pedersen
2020 IEEE Access  
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

Romil Rawat, Yagya Nath Rimal, P. William, Snehil Dahima, Sonali Gupta, K. Sakthidasan Sankaran
2022 International Journal of Information Technology and Web Engineering  
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

Maria Rigaki, Sebastian Garcia
2018 2018 IEEE Security and Privacy Workshops (SPW)  
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

Fan Liang, William G. Hatcher, Weixian Liao, Weichao Gao, Wei Yu
2019 IEEE Access  
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
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