A blockchain future for internet of things security: a position paper

A survey on technical threat intelligence in the age of sophisticated cyber attacks

To subvert recent advances in perimeter and host security, the attacker community has developed and employed various attack vectors to make a malware much stealthier than before to penetrate the target system and prolong its presence. Advanced malware or “stealthy malware” makes use of various techniques to impersonate or abuse benign applications and legitimate system tools to minimize its footprints in the target system. Thus, it is difficult for traditional detection tools, such as malware scanners, to detect it, as the malware normally does not expose its malicious payload in a file and hides its malicious behaviors among the benign behaviors of the processes. In this paper, we present PROVDETECTOR, a provenancebased approach for detecting stealthy malware. Our insight behind the PROVDETECTOR approach is that although stealthy malware attempts to blend into benign processes, the malicious behaviors inevitably interact with the underlying operating system (OS), which will be exposed to and captured by provenance monitoring. Based on this intuition, PROVDETECTOR first employs a novel selection algorithm to identify possible malicious parts in the OS-level provenance data of a process. It then applies a neural embedding and machine learning pipeline to automatically detect any behavior that deviates significantly from normal behaviors. We evaluate our approach on a large provenance dataset from an enterprise network and demonstrate that it achieves very high detection performance of stealthy malware (an average F1 score of 0.974). Further, we conduct thorough interpretability studies to understand the internals of the learned machine learning models.

You Are What You Do: Hunting Stealthy Malware via Data Provenance Analysis.

Today threat landscape evolving at the rapid rate with many organization continuously face complex and malicious cyber threats. Cybercriminal equipped by better skill, organized and well-funded than before. Cyber Threat Intelligence (CTI) has become a hot topic and being under consideration for many organization to counter the rise of cyber-attacks. The aim of this paper is to review the existing research related to CTI. Through the literature review process, the most basic question of what CTI is examines by comparing existing definitions to find common ground or disagreements. It is found that both organization and vendors lack a complete understanding of what information is considered to be CTI, hence more research is needed in order to define CTI. This paper also identified current CTI product and services that include threat intelligence data feeds, threat intelligence standards and tools that being used in CTI. There is an effort by specific industry to shared only relevance threat intelligence data feeds such as Financial Services Information Sharing and Analysis Center (FS-ISAC) that collaborate on critical security threats facing by global financial services sector only. While research and development center such as MITRE working in developing a standards format (e.g.; STIX, TAXII, CybOX) for threat intelligence sharing to solve interoperability issue between threat sharing peers. Based on the review for CTI definition, standards and tools, this paper identifies four research challenges in cyber threat intelligence and analyses contemporary work carried out in each. With an organization flooded with voluminous of threat data, the requirement for qualified threat data analyst to fully utilize CTI and turn the data into actionable intelligence become more important than ever. The data quality is not a new issue but with the growing adoption of CTI, further research in this area is needed.

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Cyber Threat Intelligence – Issue and Challenges

To be prepared against cyberattacks, most organizations resort to security information and event management systems to monitor their infrastructures. These systems depend on the timeliness and relevance of the latest updates, patches and threats provided by cyberthreat intelligence feeds. Open source intelligence platforms, namely social media networks such as Twitter, are capable of aggregating a vast amount of cybersecurity-related sources. To process such information streams, we require scalable and efficient tools capable of identifying and summarizing relevant information for specified assets. This paper presents the processing pipeline of a novel tool that uses deep neural networks to process cybersecurity information received from Twitter. A convolutional neural network identifies tweets containing security-related information relevant to assets in an IT infrastructure. Then, a bidirectional long short-term memory network extracts named entities from these tweets to form a security alert or to fill an indicator of compromise. The proposed pipeline achieves an average 94% true positive rate and 91% true negative rate for the classification task and an average F1-score of 92% for the named entity recognition task, across three case study infrastructures.

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Cyberthreat Detection from Twitter using Deep Neural Networks

The IT community is confronted with incidents of all kinds and nature, new threats appear on a daily basis. Fighting these security incidents individually is almost impossible. Sharing information about threats among the community has become a key element in incident response to stay on top of the attackers. Reliable information resources, providing credible information, are therefore essential to the IT community, or even at broader scale, to intelligence communities or fraud detection groups. This paper presents the Malware Information Sharing Platform (MISP) and threat sharing project, a trusted platform, that allows the collection and sharing of important indicators of compromise (IoC) of targeted attacks, but also threat information like vulnerabilities or financial indicators used in fraud cases. The aim of MISP is to help in setting up preventive actions and counter-measures used against targeted attacks. Enable detection via collaborative-knowledge-sharing about existing malware and other threats.

MISP: The Design and Implementation of a Collaborative Threat Intelligence Sharing Platform

The steady increase in the volume of indicators of compromise (IoC) as well as their volatile nature makes their processing challenging. Once compromised infrastructures are cleaned up, threat actors are moving to on to other target infrastructures or simply changing attack strategies. To ease the evaluation of IoCs as well as to harness the combined analysis capabilities, threat intelligence sharing platforms were introduced in order to foster collaboration on a community level. In this paper, the open-source threat intelligence platform MISP is used to implement and showcase a generic scoring model for decaying IoCs shared within MISP communities matching their heterogeneous objectives. The model takes into account existing meta-information shared along with indicators of compromise,facilitating the decision making process for machines in regards to the validity of the shared indicator of compromise. The model is applied on common use-cases that are normally encountered during incident response.

Decaying Indicators of Compromise.

IT security community is recently facing a change of trend from closed to open working groups and from restrictive information to full information disclosure and sharing. One major feature for this trend change is the number of incidents and various Indicators of compromise (IoC) that appear on a daily base, which can only be faced and solved in a collaborative way. Sharing information is key to stay on top of the threats. 
To cover the needs of having a medium for information sharing, different initiatives were taken such as the Open Source Threat Intelligence and Sharing Platform called MISP. At current state, this sharing and collection platform has become far more than a malware information sharing platform. It includes all kind of IoCs, malware and vulnerabilities, but also financial threat or fraud information. Hence, the volume of information is increasing and evolving. 
In this paper we present implemented distributed data interaction methods for MISP followed by a generic scoring model for decaying information that is shared within MISP communities. As the MISP community members do not have the same objectives, use cases and implementations of the scoring model are discussed. A commonly encountered use case in practice is the detection of indicators of compromise in operational networks.

Taxonomy driven indicator scoring in MISP threat intelligence platforms.

This document describes the MISP taxonomy format which describes a
simple JSON format to represent machine tags (also called triple tags)
vocabularies. A public directory of common vocabularies called MISP
taxonomies is available and relies on the MISP taxonomy format. MISP
taxonomies are used to classify cyber security events, threats,
suspicious events, or indicators.

MISP taxonomy format

This document describes the MISP core format used to exchange
indicators and threat information between MISP (Malware Information
and threat Sharing Platform) instances. The JSON format includes the
overall structure along with the semantic associated for each
respective key. The format is described to support other
implementations which reuse the format and ensuring an
interoperability with existing MISP [MISP-P] software and other Threat
Intelligence Platforms.

Andras Iklody

Papers

MISP: The Design and Implementation of a Collaborative Threat Intelligence Sharing Platform

Decaying Indicators of Compromise.

Taxonomy driven indicator scoring in MISP threat intelligence platforms.

MISP taxonomy format

MISP core format