--The escalating complexity and volume of cyber-attacks demand proactive detection strategies that go beyond traditional rule-based systems. This paper presents a phase-aware, multi-model machine learning framework that emulates adversarial behavior across the seven phases of the Cyber Kill Chain using the MITRE A TT&CK Enterprise dataset. T ech-niques are semantically mapped to phases via A TT ACK-BERT, producing seven phase-specific datasets. We evaluate LightGBM, a custom Transformer encoder, fine-tuned BERT, and a Graph Neural Network (GNN), integrating their outputs through a weighted soft voting ensemble. Inter-phase dependencies are modeled using directed graphs to capture attacker movement from reconnaissance to objectives. The ensemble consistently achieved the highest scores, with F1-scores ranging from 97.47% to 99.83%, surpassing GNN performance (97.36% to 99.81%) by 0.03%-0.20% This graph-driven, ensemble-based approach enables interpretable attack path forecasting and strengthens proactive cyber defense.

With AI-based software becoming widely available, the risk of exploiting its capabilities, such as high automation and complex pattern recognition, could significantly increase. An AI used offensively to attack non-AI assets is referred to as offensive AI. Current research explores how offensive AI can be utilized and how its usage can be classified. Additionally, methods for threat modeling are being developed for AI-based assets within organizations. However, there are gaps that need to be addressed. Firstly, there is a need to quantify the factors contributing to the AI threat. Secondly, there is a requirement to create threat models that analyze the risk of being attacked by AI for vulnerability assessment across all assets of an organization. This is particularly crucial and challenging in cloud environments, where sophisticated infrastructure and access control landscapes are prevalent. The ability to quantify and further analyze the threat posed by offensive AI enables analysts to rank vulnerabilities and prioritize the implementation of proactive countermeasures. To address these gaps, this paper introduces the Graph of Effort, an intuitive, flexible, and effective threat modeling method for analyzing the effort required to use offensive AI for vulnerability exploitation by an adversary. While the threat model is functional and provides valuable support, its design choices need further empirical validation in future work.

The security convoy turned on to Tehran's Imam Khomeini Boulevard at around 3:30pm on 27 November 2020. The VIP was the Iranian scientist Mohsen Fakhrizadeh, widely regarded as the head of Iran's secret nuclear weapons programme. He was driving his wife to their country property, flanked by bodyguards in other vehicles. They were close to home when the assassin struck. A number of shots rang out, smashing into Fakhrizadeh's black Nissan and bringing it to a halt.

White House press secretary Karine Jean-Pierre told reporters during an audio-only gaggle Friday that the U.S. has no indication that Russia plans to use nuclear weapons, after President Biden warned of "Armageddon." The war in Ukraine has revealed how the digital age is leveling the playing field between great powers and smaller countries. Ukraine has skillfully deployed precision munitions, drone technology and sophisticated encrypted software to gain the upper hand against Russia's invading conventional military, but Russian President Vladimir Putin's most recent remarks, and his move to illegally annex portions of Ukraine, make it clear that digital warfare will also unleash a second nuclear age. Western technology, including encrypted command and control, the High Mobility Artillery Rocket System (HIMARS), drone and counter-drone systems, combined with Ukrainian savvy and resolve have arrested Russian advances and recently rolled back Russian gains. Chips and software have proven more potent than tanks and soldiers.

It may sound like something out of a science-fiction flick, but the US Air Force recently announced that it has now embedded artificial intelligence (AI) into its targeting operations – and that's not a drill. According to Frank Kendall, Secretary of the Air Force, AI algorithms were deployed into a live operational kill chain. Kendall, however, did not disclose whether this was done by a human pilot or a remote-controlled drone. Likewise, nothing was mentioned regarding the possible loss of human life. It's a development that is raising some serious questions regarding the ethical merits and moral consequences of using technology in warfare. A kill chain is, essentially, the structure of an attack.

The U.S. Air Force revealed recently that it had used artificial intelligence to aid targeting decisions for the first time. It turns out that this was not simply a test: AI is embedded in the Air Force's targeting operation, raising serious questions. Secretary of the Air Force Frank Kendall told the Air Force Association's Air, Space & Cyber Conference in National Harbor, Maryland on Sept. 20, that the Air Force had "deployed AI algorithms for the first time to a live operational kill chain." He did not give details of the strike, whether it was by a drone or piloted aircraft, and if there were civilian casualties. The "kill chain" is the entire province in which data gathered by various sensors is analyzed, targets selected and strikes planned and ordered and the results evaluated. AI takes some of the burden off human analysts, who spend thousands of hours searching through video footage trying to find, locate and positively identify targets.

At the outbreak of World War I, the French army was mobilised in the fashion of Napoleonic times. On horseback and equipped with swords, the cuirassiers wore bright tricolour uniforms topped with feathers--the same get-up as when they swept through Europe a hundred years earlier. Vast fields were filled with trenches, barbed wire, poison gas and machine gun fire--plunging the ill-equipped soldiers into a violent hellscape of industrial-scale slaughter. Only three decades after the first World War I bayonet charge across no man's land, the US was able to incinerate entire cities with a single (nuclear) bomb blast. And since the destruction of Hiroshima and Nagasaki in 1945, our rulers' methods of war have been made yet more deadly and "efficient".

The soldier who is a split second quicker on the draw may walk away from a firefight unscathed; the ship that sinks an enemy vessel first may spare itself a volley of missiles. In cases where humans can't keep up with the pace of modern conflict, machines step in. When a rocket-propelled grenade is streaking toward an armored ground vehicle, an automated system onboard the vehicle identifies the threat, tracks it, and fires a countermeasure to intercept it, all before the crew inside is even aware. Similarly, US Navy ships equipped with the Aegis combat system can switch on Auto-Special mode, which automatically swats down incoming warheads according to carefully programmed rules. These kinds of defensive systems have been around for decades, and at least 30 countries now use them.

Fox News Flash top headlines for Oct. 15 are here. Check out what's clicking on Foxnews.com They can form swarms of hundreds of mini, precision-guided explosives, overwhelm radar or simply blanket an area with targeting sensors. They can paint or light up air, ground or sea targets for enemy fighters, missiles or armored vehicles, massively increasing warzone vulnerability. The can instantly emerge from behind mountains to fire missiles at Army convoys, infantry on the move or even mechanized armored columns.

There will be a series of tutorials and sessions on tools and methods for managing and analyzing graphs and time-series data at the Strata Data Conference in San Jose, March 5-8,2018. Subscribe to the O'Reilly Data Show Podcast to explore the opportunities and techniques driving big data, data science, and AI. Find us on Stitcher, TuneIn, iTunes, SoundCloud, RSS. In this episode of the Data Show, I spoke with Leo Meyerovich, co-founder and CEO of Graphistry. Graphs have always been part of the big data revolution (think of the large graphs generated by the early social media startups). In recent months, I've come across companies releasing and using new tools for creating, storing, and (most importantly) analyzing large graphs.