US Federal Agency Cybersecurity

In 2019, a significant US Federal Agency cybersecurity breach came to light. Hackers infiltrated the facility, stealing 500 megabytes of data from 23 files. Following the breach, external entities disconnected from the agency’s network. An extensive audit, conducted over several months, revealed an unauthorized Raspberry Pi device connected to the agency’s network.

The compromise of an external user’s account played a pivotal role in the US Federal Agency cybersecurity Breach. Furthermore, the network was shared, not a segmented environment. Which allowed the attackers to move freely between the various systems within the network, causing further damage.

Tools Used in the US Federal Agency Cybersecurity Breach

Hackers exploited a Raspberry Pi device, which can be easily purchased on Amazon for as little as $25, adding a concerning layer to the US Federal Agency Cybersecurity incident. Originally designed as an affordable educational tool, this compact, credit card-sized computer has been repurposed by hackers for malicious activities. The device supports a range of hacking tools, particularly those used for surreptitious data collection from targeted networks.

The Raspberry Pi supports a variety of payloads and scripts. Once mounted on, the device can perform Network Packet sniffing. Used mainly for reconnaissance purposes. Some more advance payloads include an easy to use 802.1x bypassing module which helps the attacker overcome various MAC authentication procedures used by some of the NAC vendors. Exfiltration of data from the Raspberry Pi can easily be done by connecting a mass storage device to it, use its on board WiFi capabilities, or for more covert operations, a dedicated USB-Wireless Dongle (non-WiFi) can be used, making its detection more difficult.

The Federal Agency Security Breach Audit

The audit uncovered that the US Federal Agency Cybersecurity Breach significantly reduced the agency’s visibility into connected devices, limiting its ability to secure those networks effectively. The depth of the attack provided hackers with access to several sensitive operations. When news of the breach broke, multiple connected agencies immediately disengaged from the network to contain the damage.

Hardware Asset Visibility and Policy Enforcement to Mitigate Federal Agency Security Breaches

Many times, enterprises’ IT and security teams struggle in providing complete and accurate visibility into their hardware assets. Especially in today’s extremely challenging IT/OT/IoT environment. This challenge has been further exacerbated by the US Federal Agency Cybersecurity Breach, which exposed the consequences of insufficient hardware visibility. Which leads to a weakened policy enforcement of hardware access. This may result in security accidents, such as ransomware attacks, data leakage, etc.

To address the challenge of Federal Agency Security Breach, it’s essential to maintain a comprehensive awareness of your hardware assets. Regardless of their characteristics and the interface utilized for connections by potential attackers. Moreover, it is important to be practical and adjust to the dynamic Cyber security defenses put in place to block them, as well as take advantage of the “blind” spots. Mainly through USB Human Interface Device (HID) emulating devices or Physical layer network implants.

In addition to the deep visibility layer, a comprehensive policy enforcement mechanism recommends on best practice policy and allows the administrator to define a strict, or more granular policies, set of rules for the system to enforce.

Sepio’s Solution for US Federal Agency Cybersecurity

Sepio is the leader in the Rogue Device Mitigation (RDM) market. And is disrupting the cybersecurity industry by uncovering hidden hardware-based attacks operating over network and USB interfaces, addressing the challenge of US Federal Agencies Cybersecurity.

Sepio is the only company in the world to undertake physical-layer visibility fingerprinting. Sepio’s Asset Risk Management (ARM) Hardware Access Control, calculates a digital fingerprint using the device descriptors of all connected peripherals and compares them against a known set of malicious devices, automatically blocking any attacks. With Machine Learning, the software analyses device behavior to identify abnormalities, such as a mouse acting as a keyboard.