7:00 – 3:30 ASRC MKT
- My home box has become very slow. 41 seconds to do a full recompile of GPM, while it takes 3 sec on a nearly identical machine at work. This may help?
- Working on terms
- Working on slides
- Attending talk on Big Data, Security and Privacy – 11 am to 12 pm at ITE 459
- Bhavani Thiraisingham
- Big data management and analytics emphasizing GANs and deep learning<- the new hotness
- How do you detect attacks?
- UMBC has real time analytics in cyber? IOCRC
- Example systems
- Cloud centric assured information sharing
- Research challenges:
- dynamically adapting and evolving policies to maintain privacy under a changing environment
- Deep learning to detect attacks tat were previously not detectable
- GANs or attacker and defender?
- Scaleabe is a big problem, e.g. policies within Hadoop operatinos
- How much information is being lost by not sharing data?
- Fine grained access control with
HiveRDF? - Distributed Search over Encrypted Big Data
- Data Security & Privacy
- Honypatching – Kevin xxx on software deception
- Novel Class detection – novel class embodied in novel malware. There are malware repositories?
- Lifecycle for IoT
- Trustworthy analytics
- Intel SGX
- Adversarial SVM
- This resembles hyperparameter tuning. What is the gradient that’s being descended?
- Binary retrofitting. Some kind of binary man-in-the-middle?
- Two body problem cybersecurity
- Question –
- discuss how a system might recognize an individual from session to session while being unable to identify the individual
- What about multiple combinatorial attacks
- What about generating credible false information to attackers, that also has steganographic components for identifying the attacker?
- I had managed to not commit the embedding xml and the programs that made them, so first I had to install gensim and lxml at home. After that it’s pretty straightforward to recompute with what I currently have.
- Moving ARFF and XLSX output to the menu choices. – done
- Get started on rendering
- Got the data read in and rendering, but it’s very brute force:
if(getCurrentEmbeddings().loadSuccess){ double posScalar = ResizableCanvas.DEFAULT_SCALAR/2.0; List<WordEmbedding> weList = currentEmbeddings.getEmbeddings(); for (WordEmbedding we : weList){ double size = 10.0 * we.getCount(); SmartShape ss = new SmartShape(we.getEntry(), Color.WHITE, Color.BLACK); ss.setPos(we.getCoordinate(0)*posScalar, we.getCoordinate(1)*posScalar); ss.setSize(size, size); ss.setAngle(0); ss.setType(SmartShape.SHAPE_TYPE.OVAL); canvas.addShape(ss); } }
It took a while to remember how shapes and agents work together. Next steps:
- Extend SmartShape to SourceShape. It should be a stripped down version of FlockingShape
- Extend BaseCA to SourceCA, again, it should be a stripped down version of FlockingBeliefCA
- Add a sourceShapeList for FlockingAgentManager that then passes that to the FlockingShapes
- Got the data read in and rendering, but it’s very brute force: