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  1. This paper presents an experience report on using an interactive program visualization tool — Dynamic, Interactive Stack-Smashing Attack Visualization (DISSAV) — and a complementary active-learning exercise to teach stack smashing, a key software security attack. The visualization tool and active-learning exercise work synergistically to guide the student through challenging, abstract concepts in the advanced cybersecurity area. DISSAV and the exercise are deployed within the software security module of an undergraduate cybersecurity course that introduces a broad range of security topics. A study is designed that collects and evaluates student perceptions on the user interface of DISSAV and the effectiveness of the two resources in improving student learning and engagement. The study finds that over 80% of responses to user interface questions, 66% of responses to student learning questions and 64% of responses to student engagement questions are positive, suggesting that the resources improve student learning and engagement in general. The study does not find discernible patterns of difference in responses from students of different ages and varying levels of prior experience with stack smashing attacks, program visualization tools and C programming.

     
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  2. In the recent past, there has been a rapid increase in attacks on consumer Internet-of-Things (IoT) devices. Several attacks currently focus on easy targets for exploitation, such as weak configurations (weak default passwords). However, with governments, industries, and organizations proposing new laws and regulations to reduce and prevent such easy targets in the IoT space, attackers will move to more subtle exploits in these devices. Memory corruption vulnerabilities are a significant class of vulnerabilities in software security through which attackers can gain control of the entire system. Numerous memory corruption vulnerabilities have been found in IoT firmware already deployed in the consumer market. This paper presents an approach for exploiting stack-based buffer-overflow attacks in IoT firmware, to hijack the device remotely. To show the feasibility of this approach, we demonstrate exploiting a common network software application, Connman, used widely in IoT firmware such as Samsung smart TVs. A series of experiments are reported on, including: crashing and executing arbitrary code in the targeted software application in a controlled environment, adopting the attacks in uncontrolled environments (with standard software defenses such as W⊕X and ASLR enabled), and installing publicly available IoT firmware that uses this software application on a Raspberry Pi. The presented exploits demonstrate the ease in which an adversary can control IoT devices. 
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