IronNetInjector: Weaponizing .NET Dynamic Language Runtime Engines

As adversaries evolve their Tactics, Techniques, and Procedures (TTPs) to stay ahead of defenders, Microsoft’s .NET Framework emerges as a common component found in the tradecraft of many contemporary Advanced Persistent Threats (APTs), whether through PowerShell or C#. Because of .NET’s ease of use and availability on every recent Windows system, it is at the forefront of modern TTPs and is a primary means of exploitation. This article considers the .NET Dynamic Language Runtime as an attack vector, and how APTs have utilized it for offensive purposes. The technique under scrutiny is Bring Your Own Interpreter (BYOI), which is the ability of developers to embed dynamic languages into .NET using an engine. The focus of this analysis is an adversarial use case in which APT Turla utilized BYOI as an evasion technique, using an IronPython .NET Injector named IronNetInjector. This research analyzes IronNetInjector and how it was used to reflectively load .NET assemblies. It also evaluates the role of Antimalware Scan Interface (AMSI) in defending Windows. Due to AMSI being at the core of Windows malware mitigation, this article further evaluates the memory patching bypass technique by demonstrating a novel AMSI bypass method in IronPython using Platform Invoke (P/Invoke)

DDoS-Capable IoT Malwares: comparative analysis and Mirai Investigation

The Internet of Things (IoT) revolution has not only carried the astonishing promise to interconnect a whole generation of traditionally “dumb” devices, but also brought to the Internet the menace of billions of badly protected and easily hackable objects. Not surprisingly, this sudden flooding of fresh and insecure devices fueled older threats, such as Distributed Denial of Service (DDoS) attacks. In this paper, we first propose an updated and comprehensive taxonomy of DDoS attacks, together with a number of examples on how this classification maps to real-world attacks. Then, we outline the current situation of DDoS-enabled malwares in IoT networks, highlighting how recent data support our concerns about the growing in popularity of these malwares. Finally, we give a detailed analysis of the general framework and the operating principles of Mirai, the most disruptive DDoS-capable IoT malware seen so far

An analysis of malware evasion techniques against modern AV engines

This research empirically tested the response of antivirus applications to binaries that use virus-like evasion techniques. In order to achieve this, a number of binaries are processed using a number of evasion methods and are then deployed against several antivirus engines. The research also documents the process of setting up an environment for testing antivirus engines, including building the evasion techniques used in the tests. The results of the empirical tests illustrate that an attacker can evade multiple antivirus engines without much effort using well-known evasion techniques. Furthermore, some antivirus engines may respond to the occurrence of an evasion technique instead of the presence of any malicious code. In practical terms, this shows that while antivirus applications are useful for protecting against known threats, their effectiveness against unknown or modified threats is limited

Storytelling Security: User-Intention Based Traffic Sanitization

Malicious software (malware) with decentralized communication infrastructure, such as peer-to-peer botnets, is difficult to detect. In this paper, we describe a traffic-sanitization method for identifying malware-triggered outbound connections from a personal computer. Our solution correlates user activities with the content of outbound traffic. Our key observation is that user-initiated outbound traffic typically has corresponding human inputs, i.e., keystroke or mouse clicks. Our analysis on the causal relations between user inputs and packet payload enables the efficient enforcement of the inter-packet dependency at the application level. We formalize our approach within the framework of protocol-state machine. We define new application-level traffic-sanitization policies that enforce the inter-packet dependencies. The dependency is derived from the transitions among protocol states that involve both user actions and network events. We refer to our methodology as storytelling security. We demonstrate a concrete realization of our methodology in the context of peer-to-peer file-sharing application, describe its use in blocking traffic of P2P bots on a host. We implement and evaluate our prototype in Windows operating system in both online and offline deployment settings. Our experimental evaluation along with case studies of real-world P2P applications demonstrates the feasibility of verifying the inter-packet dependencies. Our deep packet inspection incurs overhead on the outbound network flow. Our solution can also be used as an offline collect-and-analyze tool

Dynamic Application Level Security Sensors

The battle for cyber supremacy is a cat and mouse game: evolving threats from internal and external sources make it difficult to protect critical systems. With the diverse and high risk nature of these threats, there is a need for robust techniques that can quickly adapt and address this evolution. Existing tools such as Splunk, Snort, and Bro help IT administrators defend their networks by actively parsing through network traffic or system log data. These tools have been thoroughly developed and have proven to be a formidable defense against many cyberattacks. However, they are vulnerable to zero-day attacks, slow attacks, and attacks that originate from within. Should an attacker or some form of malware make it through these barriers and onto a system, the next layer of defense lies on the host. Host level defenses include system integrity verifiers, virus scanners, and event log parsers. Many of these tools work by seeking specific attack signatures or looking for anomalous events. The defenses at the network and host level are similar in nature. First, sensors collect data from the security domain. Second, the data is processed, and third, a response is crafted based on the processing. The application level security domain lacks this three step process. Application level defenses focus on secure coding practices and vulnerability patching, which is ineffective. The work presented in this thesis uses a technique that is commonly employed by malware, dynamic-link library (DLL) injection, to develop dynamic application level security sensors that can extract fine-grain data at runtime. This data can then be processed to provide stronger application level defense by shrinking the vulnerability window. Chapters 5 and 6 give proof of concept sensors and describe the process of developing the sensors in detail

A Dynamic Security Model for Addressing Hacking Risk Factors

Communication technologies have a significant influence on the business industry. Exchanging information, storing and retrieving data, and cutting communication costs are prime reasons for relying heavily on these technologies. However, these technologies are significantly affected by hacking. Due to neglecting the behaviour of hackers during the initial design stage of common security solutions, including firewalls, Intrusion Detection Systems, Intrusion Detection and Prevention Systems, Honeypot and Honeynet, successful hacking attempts still exist. This paper aims to investigate pre-hacking steps (footprinting, scanning, and enumeration) and to highlight the risk factors that are not considered during the development of current security solutions. These risk factors are the common causes of the failures of current security solutions against many hacking attempts. Moreover, this paper proposes a dynamic security model to guide security researchers towards proposing security countermeasures that address these risk factors, which eventually lead to minimising hacking risks

A Typology Of Social Engineering Attacks – An Information Science Perspective

Hackers are increasingly exploiting the social movement on the Internet, which is responsible for domestication of the web and its associated technologies, by using novel methods of online social engineering. However, there is not enough support in the form of published research that can help us gain a holistic understanding of human vulnerabilities that are central to online social engineering attacks. This paper extends prior published classifications and presents a new typology of online social engineering methods that manifest during the various information seeking contexts that users engage while online. Concepts borrowed from the field of information science hel p us to build this typology that groups attack vectors with different human information seeking modes. The typology can be readily used as educational material to improve end user awareness about online social engineering. In addition, the typology can be used as a conceptual starting point for future empirical research on human vulnerabilities in different information seeking contexts which in turn can informsystems designers to design more effective solutions that can help mitigate the effects of such attacks