New Directions in Automated Traffic Analysis
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Threat Defense: Cyber Deception Approach and Education for Resilience in Hybrid Threats Model
S S symmetry Article Threat Defense: Cyber Deception Approach and Education for Resilience in Hybrid Threats Model William Steingartner 1,* , Darko Galinec 2 and Andrija Kozina 3 1 Faculty of Electrical Engineering and Informatics, Technical University of Košice, Letná 9, 042 00 Košice, Slovakia 2 Department of Informatics and Computing, Zagreb University of Applied Sciences, Vrbik 8, 10000 Zagreb, Croatia; [email protected] 3 Dr. Franjo Tudman¯ Croatian Defence Academy, 256b Ilica Street, 10000 Zagreb, Croatia; [email protected] * Correspondence: [email protected] Abstract: This paper aims to explore the cyber-deception-based approach and to design a novel conceptual model of hybrid threats that includes deception methods. Security programs primarily focus on prevention-based strategies aimed at stopping attackers from getting into the network. These programs attempt to use hardened perimeters and endpoint defenses by recognizing and blocking malicious activities to detect and stop attackers before they can get in. Most organizations implement such a strategy by fortifying their networks with defense-in-depth through layered prevention controls. Detection controls are usually placed to augment prevention at the perimeter, and not as consistently deployed for in-network threat detection. This architecture leaves detection gaps that are difficult to fill with existing security controls not specifically designed for that role. Rather than using prevention alone, a strategy that attackers have consistently succeeded against, defenders Citation: Steingartner, W.; are adopting a more balanced strategy that includes detection and response. Most organizations Galinec, D.; Kozina, A. Threat Defense: Cyber Deception Approach deploy an intrusion detection system (IDS) or next-generation firewall that picks up known attacks and Education for Resilience in or attempts to pattern match for identification. -
Trend Micro Incorporated Research Paper 2012
Trend Micro Incorporated Research Paper 2012 Detecting APT Activity with Network Traffic Analysis Nart Villeneuve and James Bennett Contents About This Paper .................................................................................................................................. 1 Introduction ........................................................................................................................................... 1 Detecting Remote Access Trojans ................................................................................................... 3 GhostNet......................................................................................................................................... 3 Nitro and RSA Breach .................................................................................................................4 Detecting Ongoing Campaigns .........................................................................................................5 Taidoor ............................................................................................................................................5 IXESHE ............................................................................................................................................5 Enfal aka Lurid ..............................................................................................................................6 Sykipot ........................................................................................................................................... -
Introducing Traffic Analysis
Introducing Traffic Analysis George Danezis and Richard Clayton January 26, 2007 1 Introduction In the Second World War, traffic analysis was used by the British at Bletchley Park to assess the size of Germany's air-force, and Japanese traffic analysis countermeasures contributed to the surprise of their 1941 attack on Pearl Harbour. Nowadays, Google uses the incidence of links to assess the relative importance of web pages, credit card companies examine transactions to spot fraudulent patterns of spending, and amateur plane-spotters revealed the CIA's `extraordinary rendition' programme. Diffie and Landau, in their book on wiretapping, went so far as to say that “traffic analysis, not cryptanalysis, is the backbone of communications intelligence" [1]. However, until recently the topic has been neglected by Computer Science academics. A rich literature discusses how to secure the confidentiality, integrity and availability of communication content, but very little work has considered the information leaked from communications ‘traffic data' and how these compromises might be minimised. Traffic data records the time and duration of a communication, and traffic analysis examines this data to determine the detailed shape of the communication streams, the identities of the parties communicating, and what can be established about their locations. The data may even be sketchy or incomplete { simply knowing what `typical' communication patterns look like can be used to infer information about a particular observed communication. Civilian infrastructures, on which state and economic actors are increasingly reliant, are ever more vulnerable to traffic analysis: wireless and GSM telephony are replacing traditional systems, routing is transparent and protocols are overlaid over others { giving plenty of opportunity to observe, and take advantage of the traffic data. -
Mass Surveillance
Mass Surveillance Mass Surveillance What are the risks for the citizens and the opportunities for the European Information Society? What are the possible mitigation strategies? Part 1 - Risks and opportunities raised by the current generation of network services and applications Study IP/G/STOA/FWC-2013-1/LOT 9/C5/SC1 January 2015 PE 527.409 STOA - Science and Technology Options Assessment The STOA project “Mass Surveillance Part 1 – Risks, Opportunities and Mitigation Strategies” was carried out by TECNALIA Research and Investigation in Spain. AUTHORS Arkaitz Gamino Garcia Concepción Cortes Velasco Eider Iturbe Zamalloa Erkuden Rios Velasco Iñaki Eguía Elejabarrieta Javier Herrera Lotero Jason Mansell (Linguistic Review) José Javier Larrañeta Ibañez Stefan Schuster (Editor) The authors acknowledge and would like to thank the following experts for their contributions to this report: Prof. Nigel Smart, University of Bristol; Matteo E. Bonfanti PhD, Research Fellow in International Law and Security, Scuola Superiore Sant’Anna Pisa; Prof. Fred Piper, University of London; Caspar Bowden, independent privacy researcher; Maria Pilar Torres Bruna, Head of Cybersecurity, Everis Aerospace, Defense and Security; Prof. Kenny Paterson, University of London; Agustín Martin and Luis Hernández Encinas, Tenured Scientists, Department of Information Processing and Cryptography (Cryptology and Information Security Group), CSIC; Alessandro Zanasi, Zanasi & Partners; Fernando Acero, Expert on Open Source Software; Luigi Coppolino,Università degli Studi di Napoli; Marcello Antonucci, EZNESS srl; Rachel Oldroyd, Managing Editor of The Bureau of Investigative Journalism; Peter Kruse, Founder of CSIS Security Group A/S; Ryan Gallagher, investigative Reporter of The Intercept; Capitán Alberto Redondo, Guardia Civil; Prof. Bart Preneel, KU Leuven; Raoul Chiesa, Security Brokers SCpA, CyberDefcon Ltd.; Prof. -
Benchmarking and Optimization of Gradient Boosting Decision Tree Algorithms
Benchmarking and Optimization of Gradient Boosting Decision Tree Algorithms Andreea Anghel, Nikolaos Papandreou, Thomas Parnell Alessandro de Palma, Haralampos Pozidis IBM Research – Zurich, Rüschlikon, Switzerland {aan,npo,tpa,les,hap}@zurich.ibm.com Abstract Gradient boosting decision trees (GBDTs) have seen widespread adoption in academia, industry and competitive data science due to their state-of-the-art perfor- mance in many machine learning tasks. One relative downside to these models is the large number of hyper-parameters that they expose to the end-user. To max- imize the predictive power of GBDT models, one must either manually tune the hyper-parameters, or utilize automated techniques such as those based on Bayesian optimization. Both of these approaches are time-consuming since they involve repeatably training the model for different sets of hyper-parameters. A number of software GBDT packages have started to offer GPU acceleration which can help to alleviate this problem. In this paper, we consider three such packages: XG- Boost, LightGBM and Catboost. Firstly, we evaluate the performance of the GPU acceleration provided by these packages using large-scale datasets with varying shapes, sparsities and learning tasks. Then, we compare the packages in the con- text of hyper-parameter optimization, both in terms of how quickly each package converges to a good validation score, and in terms of generalization performance. 1 Introduction Many powerful techniques in machine learning construct a strong learner from a number of weak learners. Bagging combines the predictions of the weak learners, each using a different bootstrap sample of the training data set [1]. Boosting, an alternative approach, iteratively trains a sequence of weak learners, whereby the training examples for the next learner are weighted according to the success of the previously-constructed learners. -
The Origination and Evolution of Radio Traffic Analysis: the World War I Era
UNCLASSI Fl ED The Origination and Evolution of Radio Traffic Analysis: The World War I Era (b )(3)-P. L. 86-36 Not unlike the telegraph and ita influence on the American Civil War, the invention of radio had a profound affect on World War I military operations and in all conflicts since 1901. Signals intelligence, a new form. of intelligence produced from. the intercept of radio traffic, developed on a parallel course with radio during the early years of the twentieth century. Although signals intelligence was identified as a method to produce useful and critical information during war, it did not mature as a significant tool until after the ,.War to End All Wars." Radio traffic analysis, a branch of signals intelligence, was not even recognized as a separate technique until long after the First World War ended. Nevertheless, traffic analysis, or TIA, existed as a function in that era and made significant contributions to military operations and to the development ofsignals intelligence. For the American signals intelligence service, radio traffic analysis originated as a technique in the codebreaking section and with the clerks in the goniometric or Direction Finding (DF) service of the American Expeditionary Force. The early cryptanalysts developed TIA techniques to identify the originator and receiver of radio messages and to determine the more important encoded or enciphered messages to attack. TIA also evolved in the DF service with the clerks who discovered ways to produce intelligence from analysis of the externals of messages and from the location ofthe radio transmitters. The increasingly more complex communications systems which defied cryptanalytic attack provided the impetus for these developments. -
Ten Strategies of a World-Class Cybersecurity Operations Center Conveys MITRE’S Expertise on Accumulated Expertise on Enterprise-Grade Computer Network Defense
Bleed rule--remove from file Bleed rule--remove from file MITRE’s accumulated Ten Strategies of a World-Class Cybersecurity Operations Center conveys MITRE’s expertise on accumulated expertise on enterprise-grade computer network defense. It covers ten key qualities enterprise- grade of leading Cybersecurity Operations Centers (CSOCs), ranging from their structure and organization, computer MITRE network to processes that best enable effective and efficient operations, to approaches that extract maximum defense Ten Strategies of a World-Class value from CSOC technology investments. This book offers perspective and context for key decision Cybersecurity Operations Center points in structuring a CSOC and shows how to: • Find the right size and structure for the CSOC team Cybersecurity Operations Center a World-Class of Strategies Ten The MITRE Corporation is • Achieve effective placement within a larger organization that a not-for-profit organization enables CSOC operations that operates federally funded • Attract, retain, and grow the right staff and skills research and development • Prepare the CSOC team, technologies, and processes for agile, centers (FFRDCs). FFRDCs threat-based response are unique organizations that • Architect for large-scale data collection and analysis with a assist the U.S. government with limited budget scientific research and analysis, • Prioritize sensor placement and data feed choices across development and acquisition, enteprise systems, enclaves, networks, and perimeters and systems engineering and integration. We’re proud to have If you manage, work in, or are standing up a CSOC, this book is for you. served the public interest for It is also available on MITRE’s website, www.mitre.org. more than 50 years. -
A Hybrid Machine Learning/Deep Learning COVID-19 Severity Predictive Model from CT Images and Clinical Data
A hybrid machine learning/deep learning COVID-19 severity predictive model from CT images and clinical data Matteo Chieregato1,*, Fabio Frangiamore2, Mauro Morassi3, Claudia Baresi4, Stefania Nici1, Chiara Bassetti1, Claudio Bna` 2, and Marco Galelli1 1Fondazione Poliambulanza Istituto Ospedaliero, Unit of Medical Physics, Brescia, 25124, Italy 2Tattile s.r.l., Mairano (BS), 25030 Italy 3Fondazione Poliambulanza Istituto Ospedaliero, Department of Diagnostic Imaging, Unit of Radiology, Brescia, 25124, Italy 4Fondazione Poliambulanza Istituto Ospedaliero, Information and Communications Technology, Unit of Lean Managing, Brescia, 25124, Italy *[email protected] ABSTRACT COVID-19 clinical presentation and prognosis are highly variable, ranging from asymptomatic and paucisymptomatic cases to acute respiratory distress syndrome and multi-organ involvement. We developed a hybrid machine learning/deep learning model to classify patients in two outcome categories, non-ICU and ICU (intensive care admission or death), using 558 patients admitted in a northern Italy hospital in February/May of 2020. A fully 3D patient-level CNN classifier on baseline CT images is used as feature extractor. Features extracted, alongside with laboratory and clinical data, are fed for selection in a Boruta algorithm with SHAP game theoretical values. A classifier is built on the reduced feature space using CatBoost gradient boosting algorithm and reaching a probabilistic AUC of 0.949 on holdout test set. The model aims to provide clinical decision support to medical doctors, with the probability score of belonging to an outcome class and with case-based SHAP interpretation of features importance. Introduction To date (May 2021), more than one hundred millions of individuals have been reported as affected by COVID-19. -
Detecting Malicious Iot Network Activity Using Online Traffic Analysis
1 IoT-KEEPER: Detecting Malicious IoT Network Activity using Online Traffic Analysis at the Edge Ibbad Hafeez*, Markku Antikainenz, Aaron Yi Dingy, Sasu Tarkoma* *University of Helsinki, Finland, zAalto University, Finland, yDelft University of Technology, Netherlands Abstract—IoT devices are notoriously vulnerable even to trivial able to compromise IoT devices installed deep inside SOHO attacks and can be easily compromised. In addition, resource networks, to launch extremely large scale attacks [10], [11] as constraints and heterogeneity of IoT devices make it impractical these devices have no security in place except for the network to secure IoT installations using traditional endpoint and network security solutions. To address this problem, we present IOT- address translation (NAT), which is done on the gateway. KEEPER, a lightweight system which secures the communication To address the sorry state of IoT security, our goal in of IoT. IOT-KEEPER uses our proposed anomaly detection this paper is to develop a system capable of securing the technique to perform traffic analysis at edge gateways. It uses a communication of IoT in edge networks. Such a system should combination of fuzzy C-means clustering and fuzzy interpolation be able to detect and isolate malicious IoT devices, with scheme to analyze network traffic and detect malicious net- high sensitivity and minimal false alarms. This system should work activity. Once malicious activity is detected, IOT-KEEPER automatically enforces network access restrictions against IoT also be lightweight enough to operate efficiently using limited device generating this activity, and prevents it from attacking resources available at network gateways, typically used to set other devices or services. -
Going Dark: Impact to Intelligence and Law Enforcement and Threat Mitigation
GOING DARK: IMPACT TO INTELLIGENCE AND LAW ENFORCEMENT AND THREAT MITIGATION Bonnie Mitchell Krystle Kaul G. S. McNamara Michelle Tucker Jacqueline Hicks Colin Bliss Rhonda Ober Danell Castro Amber Wells Catalina Reguerin Cindy Green-Ortiz Ken Stavinoha ACKNOWLEDGEMENTS We would like to first thank the Office of the Director of National Intelligence (ODNI) for its generous funding and support for our study and learning journey to the DEFCON hacking conference. We are also very grateful to the Department of Homeland Security (DHS) for its support during the duration of the program. We could not have completed this study without the unwavering support and dedication of Ms. Bonnie Mitchell, ODNI Deputy National Intelligence Manager for the Western Hemisphere and the Homeland, our devoted Team Champion who steered us throughout this study and helped turn an idea into a product. We would like to acknowledge and thank each member of our public-private sector working group for their tireless efforts from around the U.S., which includes Krystle Kaul, G. S. McNamara, Michelle Tucker, Jacqueline Hicks, Colin Bliss, Rhonda Ober, Danell Castro, Amber Wells, Catalina Reguerin, Cindy Green- Ortiz and Ken Stavinoha. We are very thankful for all the unique insight we received from interviewees who contributed to this report by educating our group on the many aspects of ‘going dark,’ and we take full responsibility for any and all errors of fact or interpretation implied or explicit in this paper. Our interviewees include the Village sponsors at DEF CON, private sector industry experts and government officials. We are thankful for the interesting and diverse perspectives particularly from senior government officials and private sector experts. -
Development of Surveillance Technology and Risk of Abuse of Economic Information
∋(9(/230(172)6859(,//∃1&( 7(&+12/2∗<∃1∋5,6.2)∃%86( 2)(&2120,&,1)250∃7,21 9ΡΟ 7ΚΗςΗΡΙΚΗΥΛΘΦΡΠΠΞΘΛΦΛΡΘς ,ΘΗΟΟΛϑΗΘΦΗ&20,17ΡΙΞΡΠ∆ΗΓΣΥΡΦΗςςΛΘϑΙΡΥΛΘΗΟΟΛϑΗΘΦΗΣΞΥΣΡςΗς ΡΙΛΘΗΥΦΗΣΗΓΕΥΡΓΕΘΓΠΞΟΛΟΘϑΞϑΗΟΗςΗΓΡΥΦΡΠΠΡΘΦΥΥΛΗΥ ς∴ςΗΠςΘΓΛςΣΣΟΛΦΕΛΟΛ∴Ρ&20,17ΥϑΗΛΘϑΘΓςΗΟΗΦΛΡΘ ΛΘΦΟΞΓΛΘϑςΣΗΗΦΚΥΗΦΡϑΘΛΛΡΘ :ΡΥΝΛΘϑΓΡΦΞΠΗΘΙΡΥΚΗ672∃3ΘΗΟ /Ξ[ΗΠΕΡΞΥϑ2ΦΡΕΗΥ 3(9ΡΟ &ΟΡϑΞΛΘϑΓ 7ΛΟΗ 3∆Υ7ΚΗςΗΡΙΚΗΥΛΘΦΡΠΠΞΘΛΦΛΡΘς ,ΘΗΟΟΛϑΗΘΦΗ&20,17ΡΙΞΡΠ∆ΗΓΣΥΡΦΗςςΛΘϑΙΡΥ ΛΘΗΟΟΛϑΗΘΦΗΣΞΥΣΡςΗςΡΙΛΘΗΥΦΗΣΗΓΕΥΡΓΕΘΓΞΟΛ ΟΘϑΞϑΗΟΗςΗΓΡΥΦΡΠΠΡΘΦΥΥΛΗΥς∴ςΗΠςΘΓΛς ΣΣΟΛΦΕΛΟΛ∴Ρ&20,17ΥϑΗΛΘϑΘΓςΗΟΗΦΛΡΘ ΛΘΦΟΞΓΛΘϑςΣΗΗΦΚΥΗΦΡϑΘΛΛΡΘ :ΡΥΝΣΟΘ5ΗΙ (3,9%672∃ 3ΞΕΟΛςΚΗΥ (ΞΥΡΣΗΘ3ΥΟΛΠΗΘ ∋ΛΥΗΦΡΥΗ∗ΗΘΗΥΟΙΡΥ5ΗςΗΥΦΚ ∋ΛΥΗΦΡΥΗ∃ 7ΚΗ672∃3ΥΡϑΥ∆ΠΠΗ ∃ΞΚΡΥ ∋ΞΘΦΘ&ΠΣΕΗΟΟ,379/ΩΓ(ΓΛΘΕΞΥϑΚ (ΓΛΡΥ 0Υ∋ΛΦΝ+2/∋6:257+ +ΗΓΡΙ672∃8ΘΛ ∋Η 2ΦΡΕΗΥ 3(ΘΞΠΕΗΥ 3(9ΡΟ 7ΚΛςΓΡΦΞΠΗΘΛςΖΡΥΝΛΘϑ∋ΡΦΞΠΗΘΙΡΥΚΗ672∃3ΘΗΟ,ΛςΘΡΘΡΙΙΛΦΛΟΣΞΕΟΛΦΛΡΘΡΙ672∃ 7ΚΛςΓΡΦΞΠΗΘΓΡΗςΘΡΘΗΦΗςςΥΛΟ∴ΥΗΣΥΗςΗΘΚΗΨΛΗΖςΡΙΚΗ(ΞΥΡΣΗΘ3ΥΟΛΠΗΘ I nterception Capabilities 2000 Report to the Director General for Research of the European Parliament (Scientific and Technical Options Assessment programme office) on the development of surveillance technology and risk of abuse of economic information. This study considers the state of the art in Communications intelligence (Comint) of automated processing for intelligence purposes of intercepted broadband multi-language leased or common carrier systems, and its applicability to Comint targeting and selection, including speech recognition. I nterception Capabilities 2000 Cont ent s SUMMARY ............................................................................................................................................................................................. -
ISBN # 1-60132-514-2; American Council on Science & Education / CSCE 2021
ISBN # 1-60132-514-2; American Council on Science & Education / CSCE 2021 CSCI 2021 BOOK of ABSTRACTS The 2021 World Congress in Computer Science, Computer Engineering, and Applied Computing CSCE 2021 https://www.american-cse.org/csce2021/ July 26-29, 2021 Luxor Hotel (MGM Property), 3900 Las Vegas Blvd. South, Las Vegas, 89109, USA Table of Contents Keynote Addresses .................................................................................................................... 2 Int'l Conf. on Applied Cognitive Computing (ACC) ...................................................................... 3 Int'l Conf. on Bioinformatics & Computational Biology (BIOCOMP) ............................................ 6 Int'l Conf. on Biomedical Engineering & Sciences (BIOENG) ................................................... 12 Int'l Conf. on Scientific Computing (CSC) .................................................................................. 14 SESSION: Military & Defense Modeling and Simulation ............................................................ 27 Int'l Conf. on e-Learning, e-Business, EIS & e-Government (EEE) ............................................ 28 SESSION: Agile IT Service Practices for the cloud ................................................................... 34 Int'l Conf. on Embedded Systems, CPS & Applications (ESCS) ................................................ 37 Int'l Conf. on Foundations of Computer Science (FCS) ............................................................. 39 Int'l Conf. on Frontiers