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Boladji Vinny Adjibi

Publications and source records attributed to Boladji Vinny Adjibi.

2 recordsLinked to original sources

XNET: Intelligent Dynamic Sampling for High-Speed Network Security Monitoring

Growing network speeds, with 100GbE line rates becoming common in modern enterprise networks, pose challenges to operators and security applications, as they struggle to scale their operational efficiency accordingly, without relying on costly hardware, excessive sampling, or complex distributed deployments. Unintentional loss due to stochastic packet sampling often produces low-quality traffic, further risking missed detection of critical security incidents, particularly those hidden in typically low-rate traffic, such as APT/malware command-and-control communications. In this paper, we introduce XNET, a system that monitors traffic at line rate using commodity hardware and applies dynamic sampling to amplify the visibility of high security value traffic. XNET leverages Linux's XDP technology to process packets efficiently, classify them based on their security value, and sample them as per configured policies. The outcome is a reduced packet stream in which the security-relevant portion of the traffic is amplified at the expense of less interesting traffic segments. XNET is a highly flexible, scalable and dynamic system that can be adapted based on a network's needs. We deployed XNET in a large real-world network using only commodity hardware, where our results show that XNET can achieve up to 84% traffic reduction with no packet loss while increasing the visibility of otherwise negligible traffic fivefold. With controlled stress tests, we further demonstrate XNET's scalability up to 100Gbps. Additionally, we show that XNET sampling led to a detection rate of 99.6% in an IDS application.

cs.CR↗

SoK: An Essential Guide For Using Malware Sandboxes In Security Applications: Challenges, Pitfalls, and Lessons Learned

Malware sandboxes provide many benefits for security applications, but they are complex. These complexities can overwhelm new users in different research areas and make it difficult to select, configure, and use sandboxes. Even worse, incorrectly using sandboxes can have a negative impact on security applications. In this paper, we address this knowledge gap by systematizing 84 representative papers for using x86/64 malware sandboxes in the academic literature. We propose a novel framework to simplify sandbox components and organize the literature to derive practical guidelines for using sandboxes. We evaluate the proposed guidelines systematically using three common security applications and demonstrate that the choice of different sandboxes can significantly impact the results. Specifically, our results show that the proposed guidelines improve the sandbox observable activities by at least 1.6x and up to 11.3x. Furthermore, we observe a roughly 25% improvement in accuracy, precision, and recall when using the guidelines to help with a malware family classification task. We conclude by affirming that there is no "silver bullet" sandbox deployment that generalizes, and we recommend that users apply our framework to define a scope for their analysis, a threat model, and derive context about how the sandbox artifacts will influence their intended use case. Finally, it is important that users document their experiment, limitations, and potential solutions for reproducibility

cs.CR↗