Tag: ransomware

  • Ransomware Shuts Down Company

    Ransomware Shuts Down Company

    [et_pb_section bb_built=”1″ admin_label=”section”][et_pb_row admin_label=”row” background_position=”top_left” background_repeat=”repeat” background_size=”initial”][et_pb_column type=”4_4″][et_pb_text admin_label=”Ransomware as a service (RaaS)” background_position=”top_left” background_repeat=”repeat” background_size=”initial” _builder_version=”3.7.1″] Ransomware as a Service (RaaS) Ransomware as a Service (RaaS) continues to threaten enterprises of all sizes. RaaS is provided by organized crime for other criminals to use. The primary software creator is responsible for fixing bugs, evolving […]

  • Ransomware Deja Vu – Louisiana Declares State Emergency After Cyberattacks on Schools

    Ransomware Deja Vu – Louisiana Declares State Emergency After Cyberattacks on Schools

    On 24 July, 2019 the State of Louisiana actually had to declare a state of emergency over what appears to have been a ransomware attack against at least three of the school districts within the state. So far, the districts impacted include Sabine, Ouachita, and Morehouse parishes. The attacks seemed to impact various information technology […]

  • Using Deception to effectively fight Ransomware

    Using Deception to effectively fight Ransomware

    Deception could be a game changer in terms of detecting Ransomware.

  • Technical Analysis of Samsam Ransomware.

    Technical Analysis of Samsam Ransomware.

    Ransomware continues to represent the most critical threat facing organizations in 2018. In the latest breaches at Hancock Memorial Hospital, Adams Memorial Hospital, and Allscripts, SamSam ransomware was used to encrypt the files. In this blog, we dive into the technical details of the SamSam ransomware [1]. The blog then shares how the Samsam ransomware […]

  • Ransomware Command and Control Detection using Machine Learning

    Ransomware Command and Control Detection using Machine Learning

    Authors: Deepak Gujraniya, Mohammad Waseem, Balamurali AR, and Satnam Singh Since the first attack in 1989 [1], ransomware attacks have gained popularity. Especially in 2017, it has created havoc in every possible industry, including the government offices, public-sector departments, and hospitals. Apart from the financial strain that ransomware can bring, it also affects everyday aspects […]

  • Lateral Movement analysis of Zealot Campaign and its detection by Distributed Deception Architecture.

    Lateral Movement analysis of Zealot Campaign and its detection by Distributed Deception Architecture.

    Acalvio Threat Research Labs Web Servers are becoming one of the entry vectors in breaches. In theB last blog,B I had shared the details of deception based architecture to prevent breaches involving web server as an entry vector. In this blog, we takeB Zealot campaign as a case study to show the effectiveness of deception […]

  • Deception Centric Defense Against Ransomware

    Team Acalvio It is estimated that in 2017, damages due to the ransomware will exceed $5 billion.[8]B When successful, ransomware can not only infect the endpoint, it can also spread across the network extending its exploit. The initial versions of ransomware like CryptoWall, CryptoFortess, DMA-Locker, CryptoLuck used mapped and unmapped drive for lateral movement. B […]

  • Donbt be a sitting duck. Make your BreadCrumbs & Lures Dynamic!

    Donbt be a sitting duck. Make your BreadCrumbs & Lures Dynamic!

    BreadCrumbs and Lures are very critical components of any deception based architecture. As the name suggests, breadcrumbs and lures aid to divert a threat a threat actor (an individual or malware) to deception sensors. The moment the deception sensor gets tripped, instead of blocking the multi-stage threat, the threat actor is allowed to execute its […]

  • Ransomware: Catch me if you can.

    Ransomware: Catch me if you can.

    Ransomware demand in 2016 was around a billion dollars[1]. B WannaCry[3] was the recent ransomware campaign that spread across 150 countries affecting 200,000 users. It is estimated that in 2017[2], damages due to ransomware will exceed $5 billion. Modern defenses make use of virtualized environments or machine learning algorithms to ensnare the threat actor.B This […]