Abstract--As the primary standard protocol for modern cars, the Controller Area Network (CAN) is a critical research target for automotive cybersecurity threats and autonomous applications. The Controller Area Network OBD-II diagnostic data is easy to access via the OBD-II port, (CAN) protocol was firstly developed by Bosch in the as all modern cars are equipped with the OBD-II diagnostic 1980s [1] and serves as the de facto standard protocol for connecting system. OBD-II diagnostic data can be converted into humanreadable ECUs embedded in cars [3]-[5]. The standard structure accurate vehicle data with public formulas to be used of the CAN frame is composed of the start of frame, arbitration in the matching process for associating semantic meanings field, control field, data field, CRC field, ACK field and end with CAN signals. Both OBD-II diagnostic data and regular of frame, as shown in Figure 1. While the CAN protocol has CAN frames can be collected from the OBD-II port. The a standardized frame structure, understanding the protocol's RE systems can leverage both CAN and OBD-II diagnostic utilization for signal transmission remains challenging. This data to create a comprehensive dataset for reverse engineering is because Original Equipment Manufacturers (OEMs) encode purposes, eliminating the need for additional measurement the signals within the CAN frames' data fields (data payloads) equipment like IMUs. in proprietary ways that vary among OEMs, vehicle models, The primary objective of a CAN RE system is to identify the and years [6]. CAN messages frames is the first step to extracting the essential information are structured into frames, and the CAN frames of different to develop autonomous applications or explore automotive CAN IDs have different lengths of the data payload.

A controversial bill that seeks to protect Californians from artificial intelligence-driven catastrophes has caused uproar in the tech industry. This week, the legislation passed a key committee but with amendments to make it more palatable to Silicon Valley. SB 1047, from state Sen. Scott Wiener (D-San Francisco), is set to go to the state Assembly floor later this month. If it passes the Legislature, Gov. Gavin Newsom will have to decide whether to sign or veto the groundbreaking legislation. The bill's backers say it will create guardrails to prevent rapidly advancing AI models from causing disastrous incidents, such as shutting down the power grid without warning.

OpenAI said on Friday it had taken down accounts of an Iranian group for using its ChatGPT chatbot to generate content meant for influencing the US presidential election and other issues. The operation, identified as Storm-2035, used ChatGPT to generate content focused on topics such as commentary on the candidates on both sides in the US elections, the conflict in Gaza and Israel's presence at the Olympic Games and then shared it via social media accounts and websites, Open AI said. Investigation by the Microsoft-backed AI company showed ChatGPT was used for generating long-form articles and shorter social media comments. OpenAI said the operation did not appear to have achieved meaningful audience engagement. The majority of the identified social media posts received few or no likes, shares or comments and the company did not see indications of web articles being shared across social media.

OpenAI said on Friday that it thwarted an Iranian influence campaign that used ChatGPT to generate fake news stories and social posts aimed at Americans. The company said it identified and banned accounts generating content for five websites (in English and Spanish) pretending to be news outlets, spreading "polarizing messages" on issues like the US presidential campaign, LGBTQ rights and the war in Gaza. The operation was identified as "Storm-2035," part of a series of influence campaigns Microsoft identified last week as "connected with the Iranian government." In addition to the news posts, it included "a dozen accounts on X and one on Instagram" connected to the operation. OpenAI said the op didn't appear to have gained any meaningful traction.

San Francisco City Attorney David Chiu announced he intended to shut down 16 of the most popular AI "undressing" sites at a press conference on Thursday. The Verge reported that the City Attorney is accusing these sites of violating federal laws regarding revenge pornography, deepfake pornography and child pornography. Chiu's office also accused the sites of violating the state of California's unfair competition law because "the harm they cause to consumers greatly outweighs any benefits associated with those practices," according to the complaint for injunctive relief filed in a California superior court. The complaint focuses on a total of 50 defendants Chiu intends to prosecute for operating undressing websites. Some of the defendants' and websites' names were redacted but it also publicly identifies a few companies that operate "some of the world's most popular websites that offer to nudify images of women and girls" such as Sol Ecom located in Florida, Briver in New Mexico and the UK-based Itai Tech Ltd.

WIRED has been writing about Elon Musk--he of the electric cars, space rockets, tunnel-boring machines, implantable brain interfaces, Mars mission, and internet shitposting--for a long time. And yet the most shocking part of his two-hour interview with Republican presidential nominee Donald Trump, broadcast live on X earlier this week, may just have been what Musk didn't say. It happened around the 50-minute mark, during a very Trumpian discussion of gas and electricity prices. They were up nationally, Trump said, but "when that comes down and [sic] we're going to drill, baby, drill." And Musk, he of the--I'm going to say it again--electric cars and "saving the world" schtick, didn't pipe up until a full two minutes later, when he suggested that Trump set up a "government efficiency commission" to curb government spending.

These developments raise a number of questions: Are drones safe enough to be flown in dense neighborhoods and cities? Is it a violation of people's privacy for police to fly drones overhead at an event or protest? Who decides what level of drone autonomy is acceptable in a war zone? Those questions are no longer hypothetical. Advancements in drone technology and sensors, falling prices, and easing regulations are making drones cheaper, faster, and more capable than ever.

This full paper in the research track evaluates the usage of data logged from cybersecurity exercises in order to predict students who are potentially at risk of performing poorly. Hands-on exercises are essential for learning since they enable students to practice their skills. In cybersecurity, hands-on exercises are often complex and require knowledge of many topics. Therefore, students may miss solutions due to gaps in their knowledge and become frustrated, which impedes their learning. Targeted aid by the instructor helps, but since the instructor's time is limited, efficient ways to detect struggling students are needed. This paper develops automated tools to predict when a student is having difficulty. We formed a dataset with the actions of 313 students from two countries and two learning environments: KYPO CRP and EDURange. These data are used in machine learning algorithms to predict the success of students in exercises deployed in these environments. After extracting features from the data, we trained and cross-validated eight classifiers for predicting the exercise outcome and evaluated their predictive power. The contribution of this paper is comparing two approaches to feature engineering, modeling, and classification performance on data from two learning environments. Using the features from either learning environment, we were able to detect and distinguish between successful and struggling students. A decision tree classifier achieved the highest balanced accuracy and sensitivity with data from both learning environments. The results show that activity data from cybersecurity exercises are suitable for predicting student success. In a potential application, such models can aid instructors in detecting struggling students and providing targeted help. We publish data and code for building these models so that others can adopt or adapt them.

Sociotechnical requirements shape the governance of artificially intelligent (AI) systems. In an era where embodied AI technologies are rapidly reshaping various facets of contemporary society, their inherent dynamic adaptability presents a unique blend of opportunities and challenges. Traditional regulatory mechanisms, often designed for static -- or slower-paced -- technologies, find themselves at a crossroads when faced with the fluid and evolving nature of AI systems. Moreover, typical problems in AI, for example, the frequent opacity and unpredictability of the behaviour of the systems, add additional sociotechnical challenges. To address these interconnected issues, we introduce the concept of dynamic certification, an adaptive regulatory framework specifically crafted to keep pace with the continuous evolution of AI systems. The complexity of these challenges requires common progress in multiple domains: technical, socio-governmental, and regulatory. Our proposed transdisciplinary approach is designed to ensure the safe, ethical, and practical deployment of AI systems, aligning them bidirectionally with the real-world contexts in which they operate. By doing so, we aim to bridge the gap between rapid technological advancement and effective regulatory oversight, ensuring that AI systems not only achieve their intended goals but also adhere to ethical standards and societal values.

Specifying an Operational Design Domain (ODD) is crucial for safeguarding automated vehicle systems against conditions that exceed their capabilities. Yet, prior definitions of ODD have relied on ambiguous and unclear terms, resulting in numerous misunderstandings and misconceptions. This paper introduces a formal approach to clearly define the Operational Domain (OD) and ODD for automated vehicles. Furthermore, the absence of essential terms, such as the OD, has resulted in the creation of numerous terms that have made things more complicated and confusing. This level of complexity is unacceptable when it comes to developing safety-critical systems, where any uncertainty can lead to significant risks. This study addresses these deficiencies by providing a precise mathematical model of OD and clarifying its relationship with other terms. Also, by formalizing these terms, this work establishes a foundation for developing further concepts such as ODD specification and ODD monitoring, which are explained in this paper.