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 Hive RDF?
    • 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