Preventing Poisoning Attacks on Threat Intelligence Systems

April 22nd, 2018

Preventing Poisoning Attacks on Threat Intelligence Systems

Nitika Khurana, Graduate Student, UMBC

11:00-12:00 Monday, 23 April 2018, ITE346, UMBC

As AI systems become more ubiquitous, securing them becomes an emerging challenge. Over the years, with the surge in online social media use and the data available for analysis, AI systems have been built to extract, represent and use this information. The credibility of this information extracted from open sources, however, can often be questionable. Malicious or incorrect information can cause a loss of money, reputation, and resources; and in certain situations, pose a threat to human life. In this paper, we determine the credibility of Reddit posts by estimating their reputation score to ensure the validity of information ingested by AI systems. We also maintain the provenance of the output generated to ensure information and source reliability and identify the background data that caused an attack. We demonstrate our approach in the cybersecurity domain, where security analysts utilize these systems to determine possible threats by analyzing the data scattered on social media websites, forums, blogs, etc.


UMBC at SemEval-2018 Task 8: Understanding Text about Malware

April 21st, 2018

UMBC at SemEval-2018 Task 8: Understanding Text about Malware

Ankur Padia, Arpita Roy, Taneeya Satyapanich, Francis Ferraro, Shimei Pan, Anupam Joshi and Tim Finin, UMBC at SemEval-2018 Task 8: Understanding Text about Malware, Int. Workshop on Semantic Evaluation (collocated with NAACL-HLT), New Orleans, LA, June 2018.

We describe the systems developed by the UMBC team for 2018 SemEval Task 8, SecureNLP (Semantic Extraction from CybersecUrity REports using Natural Language Processing). We participated in three of the sub-tasks: (1) classifying sentences as being relevant or irrelevant to malware, (2) predicting token labels for sentences, and (4) predicting attribute labels from the Malware Attribute Enumeration and Characterization vocabulary for defining malware characteristics. We achieved F1 scores of 50.34/18.0 (dev/test), 22.23 (test-data), and 31.98 (test-data) for Task1, Task2 and Task2 respectively. We also make our cybersecurity embeddings publicly available at https://bit.ly/cybr2vec.


UMBC/ICMA Survey of Local Government Cybersecurity Practices

April 14th, 2018

 

UMBC/ICMA Survey of Local Government Cybersecurity Practices

In 2016, the International City/County Management Association (ICMA), in partnership with the University of Maryland, Baltimore County (UMBC), conducted a survey to better understand local government cybersecurity practices. The results of this survey provide insights into the cybersecurity issues faced by U.S. local governments, including what their capacities are, what kind of barriers they face, and what type of support they have to implement cybersecurity programs.

The survey was sent on paper via postal mail to the chief information officers of 3,423 U.S. local governments with populations of 25,000 or greater. An online submission option was also made available to survey recipients. Responses were received from 411 of the governments surveyed, yielding a response rate of 12%.

A summary of the results written by ICMA staff is available here.


AI for Cybersecurity: Intrusion Detection Using Neural Networks

March 25th, 2018

AI for Cybersecurity: Intrusion Detection Using Neural Networks

Sowmya Ramapatruni, UMBC

11:00-12:00 Monday 26 March, 2018, ITE346, UMBC

The constant growth in the use of computer networks raised concerns about security and privacy. Intrusion attacks on computer networks is a very common attack on internet today. Intrusion detection systems have been considered essential in keeping network security and therefore have been commonly adopted by network administrators. A possible disadvantage is the fact that such systems are usually based on signature systems, which make them strongly dependent on updated database and consequently inefficient against novel attacks (unknown attacks). In this study we analyze the use of machine learning in the development of intrusion detection system.

The focus of this presentation is to analyze the various machine learning algorithms that can be used to perform classification of network attacks. We will also analyze the common techniques used to build and fine tune artificial neural networks for network attack classification and address the drawbacks in these systems. We will also analyze the data sets and the information that is critical for the classification. The understanding of network packet data is essential for the feature engineering, which is an essential precursor activity for any machine learning systems. Finally, we study the drawbacks of existing machine learning systems and walk through the further study possible in this area.


MS defense: Internal Penetration Test of a Simulated Automotive Ethernet Environment, 11/21

November 18th, 2017

M.S. Thesis Defense

Internal Penetration Test of a Simulated Automotive Ethernet Environment

Kenneth Owen Truex

11:15 Tuesday, 21 November 2017, ITE325, UMBC

The capabilities of modern day automobiles have far exceeded what Robert Bosch GmbH could have imagined when it proposed the Controller Area Network (CAN) bus back in 1986. Over time, drivers wanted more functionality, comfort, and safety in their automobiles — creating a burden for automotive manufacturers. With these driver demands came many innovations to the in-vehicle network core protocol. Modern automobiles that have a video based infotainment system or any type of camera assisted functionality such as an Advanced Driver Assistance System (ADAS) use ethernet as their network backbone. This is because the original CAN specification only allowed for up to 8 bytes of data per message on a bus rated at 1 Mbps. This is far less than the requirements of more advanced video-based automotive systems. The ethernet protocol allows for 1500 bytes of data per packet on a network rated for up to 100 Mbps. This led the automotive industry to adopt ethernet as the core protocol, overcoming most of the limitations posed by the CAN protocol. By adopting ethernet as the protocol for automotive networks, certain attack vectors are now available for black hat hackers to exploit in order to put the vehicle in an unsafe condition. I will create a simulated automotive ethernet environment using the CANoe network simulation platform by Vector GmbH. Then, a penetration test will be conducted on the simulated environment in order to discover attacks that pose a threat to automotive ethernet networks. These attacks will strictly follow a comprehensive threat model in order to narrowly focus the attack surface. If exploited successfully, these attacks will cover all three sides of the Confidentiality, Integrity, Availability (CIA) triad.

I will then propose a new and innovative mitigation strategy that can be implemented on current industry standard ECUs and run successfully under strict time and resource limitations. This new strategy can help to limit the attack surface that exists on modern day automobiles and help to protect the vehicle and its occupants from malicious adversaries.

Committee: Drs. Anupam Joshi (chair), Richard Forno, Charles Nicholas, Nilanjan Banerjee


Arya Renjan: Multi-observable Session Reputation Scoring System

October 22nd, 2017

Multi-observable Session Reputation Scoring System

Arya Renjan

11:00-12:00 Monday, 23 October 2017, ITE 346

With increasing adoption of Cloud Computing, cyber attacks have become one of the most effective means for adversaries to inflict damage. To overcome limitations of existing blacklists and whitelists, our research focuses to develop a dynamic reputation scoring model for sessions based on a variety of observable and derived attributes of network traffic. Here we propose a technique to greylist sessions using observables like IP, Domain, URL and File Hash by scoring them numerically based on the events in the session. This enables automatic labeling of possible malicious hosts or users that can help in enriching the existing whitelists or blacklists.


Agniva Banerjee on Managing Privacy Policies through Blockchain

October 16th, 2017

Link before you Share: Managing Privacy Policies through Blockchain

Agniva Banerjee

11:00am Monday, 16 October 2017

An automated access-control and audit mechanism that enforces users’ data privacy policies when sharing their data across third parties, by utilizing privacy policy ontology instances with the properties of blockchain.


talk: Penetration Testing a Simulated Automotive Ethernet Environment

October 15th, 2017

Penetration Testing a Simulated Automotive Ethernet Environment

Kenneth Truex

11:00am Monday, 9 October 2017, ITE 346

The capabilities of modern day automobiles have far exceeded what Robert Bosch GmbH could have imagined when it proposed the Controller Area Network (CAN) bus back in 1986. Over time, drivers wanted more functionality, comfort, and safety in their automobiles creating a burden for automotive manufacturers. With these driver demands came many innovations to the in-vehicle network core protocol. Modern automobiles that have a video based infotainment system or any type of camera assisted functionality such as an Advanced Driver Assistance System (ADAS) use ethernet as their network backbone. This is because the original CAN specification only allowed for up to eight bytes of data per message on a bus rated at 1 Mbps. This is far less than the requirements of more advanced video-based automotive systems. The ethernet protocol allows for 1500 bytes of data per packet on a network rated for up to 100 Mbps. This led the automotive industry to adopt ethernet as the core protocol, overcoming most of the limitations posed by the CAN protocol. By adopting ethernet as the protocol for automotive networks, certain attack vectors are now available for black hat hackers to exploit in order to put the vehicle in an unsafe condition. This thesis will create a simulated automotive ethernet environment using the CANoe network simulation platform created by Vector. Then, a penetration test will be conducted on the simulated environment in order to discover attacks that pose a threat to automotive ethernet networks. These attacks will be from the perspective of an attacker will full access to the vehicle under test, and will cover all three sides of the Confidentiality, Integrity, Availability (CIA) triad. In conclusion, this thesis will propose several ethernet specific defense mechanisms that can be implemented in an automotive taxonomy to reduce the attack surface and allow for a safer end user experience.


talk: K. Mayes on Attacks on Smart Cards, RFIDs and Embedded System, 10am 10/10

October 8th, 2017

Attacks on Smart Cards, RFIDs and Embedded Systems

Prof. Keith Mayes
Royal Holloway University of London

10-11:00am Tuesday, 10 October 2017, ITE 325, UMBC

Smart Cards and RFIDs exist with a range of capabilities and are used in their billions throughout the world. The simpler devices have poor security, however, for many years, high-end smart cards have successfully been used in a range of systems such as banking, passports, mobile communication, satellite TV etc. Fundamental to their success is a specialist design to offer remarkable resistance to a wide range of attacks, including physical, side-channel and fault. This talk describes a range of known attacks and the countermeasures that are employed to defeat them.

Prof. Keith Mayes is the Head of the School of Mathematics and Information Security at Royal Holloway University of London. He received his BSc (Hons) in Electronic Engineering in 1983 from the University of Bath, and his PhD degree in Digital Image Processing in 1987. He is an active researcher/author with 100+ publications in numerous conferences, books and journals. His interests include the design of secure protocols, communications architectures and security tokens as well as associated attacks/countermeasures. He is a Fellow of the Institution of Engineering and Technology, a Founder Associate Member of the Institute of Information Security Professionals, a Member of the Licensing Executives Society and a member of the editorial board of the Journal of Theoretical and Applied Electronic Commerce Research (JTAER).


Dissertation: Context-Dependent Privacy and Security Management on Mobile Devices

September 10th, 2017

Context-Dependent Privacy and Security Management on Mobile Devices

Prajit Kumar Das, Context-Dependent Privacy and Security Management on Mobile Devices, Ph.D. Dissertation, University of Maryland, Baltimore County, September 2017.

There are ongoing security and privacy concerns regarding mobile platforms that are being used by a growing number of citizens. Security and privacy models typically used by mobile platforms use one-time permission acquisition mechanisms. However, modifying access rights after initial authorization in mobile systems is often too tedious and complicated for users. User studies show that a typical user does not understand permissions requested by applications or are too eager to use the applications to care to understand the permission implications. For example, the Brightest Flashlight application was reported to have logged precise locations and unique user identifiers, which have nothing to do with a flashlight application’s intended functionality, but more than 50 million users used a version of this application which would have forced them to allow this permission. Given the penetration of mobile devices into our lives, a fine-grained context-dependent security and privacy control approach needs to be created.

We have created Mithril as an end-to-end mobile access control framework that allows us to capture access control needs for specific users, by observing violations of known policies. The framework studies mobile application executables to better inform users of the risks associated with using certain applications. The policy capture process involves an iterative user feedback process that captures policy modifications required to mediate observed violations. Precision of policy is used to determine convergence of the policy capture process. Policy rules in the system are written using Semantic Web technologies and the Platys ontology to define a hierarchical notion of context. Policy rule antecedents are comprised of context elements derived using the Platys ontology employing a query engine, an inference mechanism and mobile sensors. We performed a user study that proves the feasibility of using our violation driven policy capture process to gather user-specific policy modifications.

We contribute to the static and dynamic study of mobile applications by defining “application behavior” as a possible way of understanding mobile applications and creating access control policies for them. Our user study also shows that unlike our behavior-based policy, a “deny by default” mechanism hampers usability of access control systems. We also show that inclusion of crowd-sourced policies leads to further reduction in user burden and need for engagement while capturing context-based access control policy. We enrich knowledge about mobile “application behavior” and expose this knowledge through the Mobipedia knowledge-base. We also extend context synthesis for semantic presence detection on mobile devices by combining Bluetooth, low energy beacons and Nearby Messaging services from Google.


DC-Area Anonymity, Privacy, and Security Seminar

June 10th, 2017

 

The DC-Area Anonymity, Privacy, and Security Seminar (DCAPS) is a seminar for research on computer and communications anonymity, privacy, and security in the D.C. area. DCAPS meets to promote collaboration and improve awareness of work in the community. Seminars occur three times a year. It meets at different locations and has been hosted in the past by George Mason University, Georgetown University, George Washington University, University of Maryland, College park and UMBC. DCAPS meetings are free and open to anybody interested. To join the seminar mailing list, contact the organizer, Aaron Johnson, at aaron.m.johnson AT nrl.navy.mil.


Modeling and Extracting information about Cybersecurity Events from Text

May 15th, 2017

Ph.D. Dissertation Proposal

Modeling and Extracting information about Cybersecurity Events from Text

Taneeya Satyapanich

Tuesday, 16 May 2017, ITE 325, UMBC

People rely on the Internet to carry out much of the their daily activities such as banking, ordering food and socializing with their family and friends. The technology facilitates our lives, but also comes with many problems, including cybercrimes, stolen data and identity theft. With the large and increasing number of transaction done every day, the frequency of cybercrime events is also increasing. Since the number of security-related events is too high for manual review and monitoring, we need to train machines to be able to detect and gather data about potential cybersecurity threats. To support machines that can identify and understand threats, we need standard models to store the cybersecurity information and information extraction systems that can collect information to populate the models with data from text.

This dissertation will make two major contributions. The first is to extend our current cyber security ontologies with better models for relevant events, from atomic events like a login attempt, to an extended but related series of events that make up a campaign, to generalized events, such as an increase in denial-of-service attacks originating from a particular region of the world targeted at U.S. financial institutions. The second is the design and implementation of a event extraction system that can extract information about cybersecurity events from text and populated a knowledge graph using our cybersecurity event ontology. We will extend our previous work on event extraction that detected human activity events from news and discussion forums. A new set of features and learning algorithms will be introduced to improve the performance and adapt the system to cybersecurity domain. We believe that this dissertation will be useful for cybersecurity management in the future. It will quickly extract cybersecurity events from text and fill in the event ontology.

Committee: Drs. Tim Finin (chair), Anupam Joshi, Tim Oates and Karuna Joshi