Finding Trojan Message Vulnerabilities in Distributed Systems

Trojan messages are messages that seem correct to the receiver but cannot be generated by any correct sender. Such messages constitute major vulnerability points of a distributed system---they constitute ideal targets for a malicious actor and facilitate failure propagation across nodes. We describe Achilles, a tool that searches for Trojan messages in a distributed system. Achilles uses dynamic white-box analysis on the distributed system binaries in order to infer the predicate that defines messages parsed by receiver nodes and generated by sender nodes, respectively, and then computes Trojan messages as the difference between the two. We apply Achilles on implementations of real distributed systems: FSP, a file transfer application, and PBFT, a Byzantine-fault-tolerant state machine replication library. Achilles discovered a new bug in FSP and rediscovered a previously known vulnerability in PBFT. In our evaluation we demonstrate that our approach can perform orders of magnitude better than general approaches based on regular fuzzing and symbolic execution

A Survey of Techniques for Improving Security of GPUs

Graphics processing unit (GPU), although a powerful performance-booster, also has many security vulnerabilities. Due to these, the GPU can act as a safe-haven for stealthy malware and the weakest `link' in the security `chain'. In this paper, we present a survey of techniques for analyzing and improving GPU security. We classify the works on key attributes to highlight their similarities and differences. More than informing users and researchers about GPU security techniques, this survey aims to increase their awareness about GPU security vulnerabilities and potential countermeasures

Refining the PoinTER “human firewall” pentesting framework

PurposePenetration tests have become a valuable tool in the cyber security defence strategy, in terms of detecting vulnerabilities. Although penetration testing has traditionally focused on technical aspects, the field has started to realise the importance of the human in the organisation, and the need to ensure that humans are resistant to cyber-attacks. To achieve this, some organisations “pentest” their employees, testing their resilience and ability to detect and repel human-targeted attacks. In a previous paper we reported on PoinTER (Prepare TEst Remediate), a human pentesting framework, tailored to the needs of SMEs. In this paper, we propose improvements to refine our framework. The improvements are based on a derived set of ethical principles that have been subjected to ethical scrutiny.MethodologyWe conducted a systematic literature review of academic research, a review of actual hacker techniques, industry recommendations and official body advice related to social engineering techniques. To meet our requirements to have an ethical human pentesting framework, we compiled a list of ethical principles from the research literature which we used to filter out techniques deemed unethical.FindingsDrawing on social engineering techniques from academic research, reported by the hacker community, industry recommendations and official body advice and subjecting each technique to ethical inspection, using a comprehensive list of ethical principles, we propose the refined GDPR compliant and privacy respecting PoinTER Framework. The list of ethical principles, we suggest, could also inform ethical technical pentests.OriginalityPrevious work has considered penetration testing humans, but few have produced a comprehensive framework such as PoinTER. PoinTER has been rigorously derived from multiple sources and ethically scrutinised through inspection, using a comprehensive list of ethical principles derived from the research literature

Threats and countermeasures for network security

In the late 1980's, the traditional threat of anonymous break-ins to networked computers was joined by viruses and worms, multiplicative surrogates that carry out the bidding of their authors. Technologies for authentication and secrecy, supplemented by good management practices, are the principal countermeasures. Four articles on these subjects are presented

Implementation vulnerabilities in general quantum cryptography

Quantum cryptography is information-theoretically secure owing to its solid basis in quantum mechanics. However, generally, initial implementations with practical imperfections might open loopholes, allowing an eavesdropper to compromise the security of a quantum cryptographic system. This has been shown to happen for quantum key distribution (QKD). Here we apply experience from implementation security of QKD to several other quantum cryptographic primitives. We survey quantum digital signatures, quantum secret sharing, source-independent quantum random number generation, quantum secure direct communication, and blind quantum computing. We propose how the eavesdropper could in principle exploit the loopholes to violate assumptions in these protocols, breaking their security properties. Applicable countermeasures are also discussed. It is important to consider potential implementation security issues early in protocol design, to shorten the path to future applications.Comment: 13 pages, 8 figure

The Australian Cyber Security Centre threat report 2015

Introduction: The number, type and sophistication of cyber security threats to Australia and Australians are increasing. Due to the varied nature of motivations for cyber adversaries targeting Australian organisations, organisations could be a target for malicious activities even if they do not think the information held on their networks is valuable, or that their business would be of interest to cyber adversaries. This first unclassified report by the ACSC describes the range of cyber adversaries targeting Australian networks, explains their motivations, the malicious activities they are conducting and their impact, and provides specific examples of activity targeting Australian networks during 2014. This report also offers mitigation advice on how organisations can defend against these activities. The ACSC’s ability to detect and defend against sophisticated cyber threats continues to improve. But cyber adversaries are constantly improving their tradecraft in their attempts to defeat our network defences and exploit the new technologies we embrace. There are gaps in our understanding of the extent and nature of malicious activity, particularly against the business sector. The ACSC is reaching out to industry to build partnerships to improve our collective understanding. Future iterations of the Threat Report will benefit from these partnerships and help to close gaps in our knowledge

A Multi Agent System for Flow-Based Intrusion Detection

The detection and elimination of threats to cyber security is essential for system functionality, protection of valuable information, and preventing costly destruction of assets. This thesis presents a Mobile Multi-Agent Flow-Based IDS called MFIREv3 that provides network anomaly detection of intrusions and automated defense. This version of the MFIRE system includes the development and testing of a Multi-Objective Evolutionary Algorithm (MOEA) for feature selection that provides agents with the optimal set of features for classifying the state of the network. Feature selection provides separable data points for the selected attacks: Worm, Distributed Denial of Service, Man-in-the-Middle, Scan, and Trojan. This investigation develops three techniques of self-organization for multiple distributed agents in an intrusion detection system: Reputation, Stochastic, and Maximum Cover. These three movement models are tested for effectiveness in locating good agent vantage points within the network to classify the state of the network. MFIREv3 also introduces the design of defensive measures to limit the effects of network attacks. Defensive measures included in this research are rate-limiting and elimination of infected nodes. The results of this research provide an optimistic outlook for flow-based multi-agent systems for cyber security. The impact of this research illustrates how feature selection in cooperation with movement models for multi agent systems provides excellent attack detection and classification