Ransomware has become a significant global threat with the ransomware-as-a-service model enabling easy availability and deployment, and the potential for high revenues creating a viable criminal business model. Individuals, private companies or public service providers e.g. healthcare or utilities companies can all become victims of ransomware attacks and consequently suffer severe disruption and financial loss. Although machine learning algorithms are already being used to detect ransomware, variants are being developed to specifically evade detection when using dynamic machine learning techniques. In this paper we introduce NetConverse, a machine learning analysis of Windows ransomware network traffic to achieve a high, consistent detection rate. Using a dataset created from conversation-based network traffic features we achieved a true positive detection rate of 97.1% using the Decision Tree (J48) classifier.

Emergence of cloud computing technologies have changed the way we store, retrieve, and archive our data. With the promise of unlimited, reliable and always-available storage, a lot of private and confidential data are now stored on different cloud platforms. Being such a gold mine of data, cloud platforms are among the most valuable targets for attackers. Therefore, many forensics investigators have tried to develop tools, tactics and procedures to collect, preserve, analyse and report evidences of attackers' activities on different cloud platforms. Despite the number of published articles there isn't a bibliometric study that presents cloud forensics research trends. This paper aims to address this problem by providing a comprehensive assessment of cloud forensics research trends between 2009 -2016. Moreover, we provide a classification of cloud forensics process to detect the most profound research areas and highlight remaining challenges.

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DepthK is a source-to-source transformation tool that employs bounded model checking (BMC) to verify and falsify safety properties in single- and multi-threaded C programs, without manual annotation of loop invariants. Here, we describe and evaluate a proof-by-induction algorithm that combines k-induction with invariant inference to prove and refute safety properties. We apply two invariant generators to produce program invariants and feed these into a k-induction-based verification algorithm implemented in DepthK, which uses the efficient SMT-based context-bounded model checker (ESBMC) as sequential verification back-end. A set of C benchmarks from the International Competition on Software Verification (SV-COMP) and embedded-system applications extracted from the available literature are used to evaluate the effectiveness of the proposed approach. Experimental results show that k-induction with invariants can handle a wide variety of safety properties, in typical programs with loops and embedded software applications from the telecommunications, control systems, and medical domains. The results of our comparative evaluation extend the knowledge about approaches that rely on both BMC and k-induction for software verification, in the following ways. (1) The proposed method outperforms the existing implementations that use k-induction with an interval-invariant generator (e.g., 2LS and ESBMC), in the category ConcurrencySafety, and overcame, in others categories, such as SoftwareSystems, other software verifiers that use plain BMC (e.g., CBMC). Also, (2) it is more precise than other verifiers based on the property-directed reachability (PDR) algorithm (i.e., SeaHorn, Vvt and CPAchecker-CTIGAR). This way, our methodology demonstrated improvement over existing BMC and k-induction-based approaches.

The proliferation of Unmanned Aerial Vehicles (UAVs) embedded with vulnerable monolithic software, involving concurrency and fragile communication links, has recently raised serious concerns about their security. Recent studies show that a 2kg UAV can cause a critical damage to a passenger jet windscreen. However, verifying security in UAV software based on traditional testing remains an open challenge mainly due to scalability and deployment issue. Here we investigate the application of software verification techniques; in particular, existing software analyzers and verifiers, which implement fuzzing and bounded model checking techniques, to detect security vulnerabilities in typical UAVs. We also investigate fragility aspects related to the UAV communication link since all remaining UAV components (e.g., position, velocity and attitude control) heavily depend on it. Our preliminary results show real cyber-threats with the possibility of exploiting further security vulnerabilities in real-world UAV software in the foreseeable future.

The proliferation of Unmanned Aerial Vehicles (UAVs) embedded with vulnerable monolithic software, involving concurrency and fragile communication links, has recently raised serious concerns about their security. Recent studies show that a 2kg UAV can cause a critical damage to a passenger jet windscreen. However, verifying security in UAV software based on traditional testing remains an open challenge mainly due to scalability and deployment issue. Here we investigate the application of software verification techniques; in particular, existing software analyzers and verifiers, which implement fuzzing and bounded model checking techniques, to detect security vulnerabilities in typical UAVs. We also investigate fragility aspects related to the UAV communication link since all remaining UAV components (e.g., position, velocity and attitude control) heavily depend on it. Our preliminary results show real cyber-threats with the possibility of exploiting further security vulnerabilities in real-world UAV software in the foreseeable future.