Biblio

Automated teller machines are affected by two kinds of attacks: physical and logical. It is common for most banks to look for zero-day protection for their devices. The most secure solutions available are based on complex security policies that are extremely hard to configure. The goal of this article is to present a concept of using the modified MajorClust algorithm for generating a sandbox-based security policy based on ATM usage data. The results obtained from the research prove the effectiveness of the used techniques and confirm that it is possible to create a division into sandboxes in an automated way.

Large scale deployment of IoT devices has made Bluetooth Protocol (IEEE 802.15.1) the wireless protocol of choice for close-range communications. Devices such as keyboards, smartwatches, headphones, computer mouse, and various wearable connecting devices use Bluetooth network for communication. Moreover, Bluetooth networks are widely used in medical devices like heart monitors, blood glucose monitors, asthma inhalers, and pulse oximeters. Also, Bluetooth has replaced cables for wire-free equipment in a surgical environment. In hospitals, devices communicate with one another, sharing sensitive and critical information over Bluetooth scatter-networks. Thus, it is imperative to secure the Bluetooth networks against attacks like Man in the Middle attack (MITM), eavesdropping attacks, and Denial of Service (DoS) attacks. This paper presents a Multi-Level Bluetooth Intrusion Detection System (ML-BIDS) to detect malicious attacks against Bluetooth devices. In the ML-IDS framework, we perform continuous device identification and authorization in Bluetooth networks following the zero-trust principle [ref]. The ML-BIDS framework includes an anomaly-based intrusion detection system (ABIDS) to detect attacks on the Bluetooth protocol. The ABIDS tracks the normal behavior of the Bluetooth protocol by comparing it with the Bluetooth protocol state machine. Bluetooth frame flows consisting of Bluetooth frames received over 10 seconds are split into n-grams to track the current state of the protocol in the state machine. We evaluated the performance of several machine learning algorithms like C4.5, Adaboost, SVM, Naive Bayes, Jrip, and Bagging to classify normal Bluetooth protocol flows from abnormal Bluetooth protocol flows. The ABIDS detects attacks on Bluetooth protocols with a precision of up to 99.6% and recall up to 99.6%. The ML-BIDS framework also performs whitelisting of the devices on the Bluetooth network to prevent unauthorized devices from connecting to the network. ML-BIDS uses a combination of the Bluetooth Address, mac address, and IP address to uniquely identify a Bluetooth device connecting to the network, and hence ensuring only authorized devices can connect to the Bluetooth network.

Application whitelisting software allows only examined and trusted applications to run on user's machine. Since many malicious files don't require administrative privileges in order for them to be executed, whitelisting can be the only way to block the execution of unauthorized applications in enterprise environment and thus prevent infection or data breach. In order to assess the current state of such solutions, the access to three whitelisting solution licenses was obtained with the purpose to test their effectiveness against different modern types of ransomware found in the wild. To conduct this study a virtual environment was used with Windows Server and Enterprise editions installed. The objective of this paper is not to evaluate each vendor or make recommendations of purchasing specific software but rather to assess the ability of application control solutions to block execution of ransomware files, as well as assess the potential for future research. The results of the research show the promise and effectiveness of whitelisting solutions.

In a world of ever-increasing systems interdependence, effective cybersecurity policy design seems to be one of the most critically understudied elements of our national security strategy. Enterprise cyber technologies are often implemented without much regard to the interactions that occur between humans and the new technology. Furthermore, the interactions that occur between individuals can often have an impact on the newly employed technology as well. Without a rigorous, evidence-based approach to ground an employment strategy and elucidate the emergent organizational needs that will come with the fielding of new cyber capabilities, one is left to speculate on the impact that novel technologies will have on the aggregate functioning of the enterprise. In this paper, we will explore a scenario in which a hypothetical government agency applies a complexity science perspective, supported by agent-based modeling, to more fully understand the impacts of strategic policy decisions. We present a model to explore the socio-technical dynamics of these systems, discuss lessons using this platform, and suggest further research and development.