The proliferation of the Internet of Things (IoT) has heightened the vulnerability to cyber threats, making it imperative to develop Anomaly Detection Systems (ADSs) capable of adapting to emerging or novel attacks. Prior research has predominantly concentrated on offline unsupervised learning techniques to protect ADSs, which are impractical for real-world applications. Furthermore, these studies often rely heavily on the assumption of known legitimate behaviors and fall short of meeting the interpretability requirements in security contexts, thereby hindering their practical adoption. In response, this paper introduces Adaptive NAD, a comprehensive framework aimed at enhancing and interpreting online unsupervised anomaly detection within security domains. We propose an interpretable two-layer anomaly detection approach that generates dependable, high-confidence pseudo-labels. Subsequently, we incorporate an online learning mechanism that updates Adaptive NAD using an innovative threshold adjustment method to accommodate new threats. Experimental findings reveal that Adaptive NAD surpasses state-of-the-art solutions by achieving improvements of over 5.4% and 23.0% in SPAUC on the CIC-Darknet2020 and CIC-DoHBrw-2020 datasets, respectively. The code for Adaptive NAD is publicly available at https://github.com/MyLearnCodeSpace/Adaptive-NAD.

Advances in AI will enable new capabilities and make others far more affordable – not only to the U.S., but to adversaries as well, raising the stakes as the United States seeks to preserve its hard-won strategic overmatch in the air, land, sea, space and cyberspace domains. The Pentagon's Third Offset Strategy seeks to leverage AI and related technologies in a variety of ways, according to Robert Work, former deputy secretary of defense and one of the strategy's architects. In a forward to a new report from the market analytics firm Govini, Work says the strategy "seeks to exploit advances in AI and autonomous systems to improve the performance of Joint Force guided munitions battle networks" through: "By exploiting advances in AI and autonomous systems to improve the warfighting potential and performance of the U.S. military," Work says, "the strategy aims to restore the Joint Force's eroding conventional overmatch versus any potential adversary, thereby strengthening conventional deterrence." Spending is growing, Govini reports, with AI and related defense program spending increasing at a compound annual rate of 14.5 percent from 2012 to 2017, and poised to grow substantially faster in coming years as advanced computing technologies come on line, driving down computational costs. But in practical terms, what does that mean?

A Harvard Belfer Center study commissioned by the Intelligence Advanced Research Projects Agency (IARPA), Artificial Intelligence and National Security, predicted last May that AI will be as transformative to national defense as nuclear weapons, aircraft, computers and biotech. Advances in AI will enable new capabilities and make others far more affordable – not only to the U.S., but to adversaries as well, raising the stakes as the United States seeks to preserve its hard-won strategic overmatch in the air, land, sea, space and cyberspace domains. The Pentagon's Third Offset Strategy seeks to leverage AI and related technologies in a variety of ways, according to Robert Work, former deputy secretary of defense and one of the strategy's architects. In a forward to a new report from the market analytics firm Govini, Work says the strategy "seeks to exploit advances in AI and autonomous systems to improve the performance of Joint Force guided munitions battle networks" through: "By exploiting advances in AI and autonomous systems to improve the warfighting potential and performance of the U.S. military," Work says, "the strategy aims to restore the Joint Force's eroding conventional overmatch versus any potential adversary, thereby strengthening conventional deterrence." Spending is growing, Govini reports, with AI and related defense program spending increasing at a compound annual rate of 14.5 percent from 2012 to 2017, and poised to grow substantially faster in coming years as advanced computing technologies come on line, driving down computational costs.