Information Security Threats and Working from Home Culture: Taxonomy, Risk Assessment and Solutions.

During the COVID-19 pandemic, most organizations were forced to implement a work-from-home policy, and in many cases, employees have not been expected to return to the office on a full-time basis. This sudden shift in the work culture was accompanied by an increase in the number of information security-related threats which organizations were unprepared for. The ability to effectively address these threats relies on a comprehensive threat analysis and risk assessment and the creation of relevant asset and threat taxonomies for the new work-from-home culture. In response to this need, we built the required taxonomies and performed a thorough analysis of the threats associated with this new work culture. In this paper, we present our taxonomies and the results of our analysis. We also examine the impact of each threat, indicate when it is expected to occur, describe the various prevention methods available commercially or proposed in academic research, and present specific use cases.

RadArnomaly: Protecting Radar Systems from Data Manipulation Attacks.

Radar systems are mainly used for tracking aircraft, missiles, satellites, and watercraft. In many cases, information regarding the objects detected by a radar system is sent to, and used by, a peripheral consuming system, such as a missile system or a graphical user interface used by an operator. Those systems process the data stream and make real-time operational decisions based on the data received. Given this, the reliability and availability of information provided by radar systems have grown in importance. Although the field of cyber security has been continuously evolving, no prior research has focused on anomaly detection in radar systems. In this paper, we present an unsupervised deep-learning-based method for detecting anomalies in radar system data streams; we take into consideration the fact that a data stream created by a radar system is heterogeneous, i.e., it contains both numerical and categorical features with non-linear and complex relationships. We propose a novel technique that learns the correlation between numerical features and an embedding representation of categorical features in an unsupervised manner. The proposed technique, which allows for the detection of the malicious manipulation of critical fields in a data stream, is complemented by a timing-interval anomaly-detection mechanism proposed for the detection of message-dropping attempts. Real radar system data were used to evaluate the proposed method. Our experiments demonstrated the method's high detection accuracy on a variety of data-stream manipulation attacks (an average detection rate of 88% with a false -alarm rate of 1.59%) and message-dropping attacks (an average detection rate of 92% with a false-alarm rate of 2.2%).

The Security of IP-Based Video Surveillance Systems.

Over the last decade, video surveillance systems have become a part of the Internet of Things (IoT). These IP-based surveillance systems now protect industrial facilities, railways, gas stations, and even one's own home. Unfortunately, like other IoT systems, there are inherent security risks which can lead to significant violations of a user's privacy. In this review, we explore the attack surface of modern surveillance systems and enumerate the various ways they can be compromised with real examples. We also identify the threat agents, their attack goals, attack vectors, and the resulting consequences of successful attacks. Finally, we present current countermeasures and best practices and discuss the threat horizon. The purpose of this review is to provide researchers and engineers with a better understanding of a modern surveillance systems' security, to harden existing systems and develop improved security solutions.

Spoofing Attack on Ultrasonic Distance Sensors Using a Continuous Signal.

Ultrasonic distance sensors use an ultrasonic pulse's time of flight to calculate the distance to the reflecting object. Widely used in industry, these sensors are an important component in autonomous vehicles, where they are used for such tasks as object avoidance and altitude measurement. The proper operation of such autonomous vehicles relies on sensor measurements; therefore, an adversary that has the ability to undermine the sensor's reliability can pose a major risk to the vehicle. Previous attempts to alter the measurements of this sensor using an external signal succeeded in performing a denial-of-service (DoS) attack, in which the sensor's reading showed a constant value, and a spoofing attack, in which the attacker could control the measurement to some extent. However, these attacks require precise knowledge of the sensor and its operation (e.g., timing of the ultrasonic pulse sent by the sensor). In this paper, we present an attack on ultrasonic distance sensors in which the measured distance can be altered (i.e., spoofing attack). The attack exploits a vulnerability discovered in the ultrasonic sensor's receiver that results in a fake pulse that is produced by a constant noise in the input. A major advantage of the proposed attack is that, unlike previous attacks, a constant signal is used, and therefore, no prior knowledge of the sensor's relative location or its timing behavior is required. We demonstrate the attack in both a lab setup (testbed) and a real setup involving a drone to demonstrate its feasibility. Our experiments show that the attack can interfere with the proper operation of the vehicle. In addition to the risk that the attack poses to autonomous vehicles, it can also be used as an effective defensive tool for restricting the movement of unauthorized autonomous vehicles within a protected area.

P4UIoT: Pay-Per-Piece Patch Update Delivery for IoT Using Gradual Release.

P 4 UIoT-pay-per-piece patch update delivery for IoT using gradual release-introduces a distributed framework for delivering patch updates to IoT devices. The framework facilitates distribution via peer-to-peer delivery networks and incentivizes the distribution operation. The peer-to-peer delivery network reduces load by delegating the patch distribution to the nodes of the network, thereby protecting against a single point of failure and reducing costs. Distributed file-sharing solutions currently available in the literature are limited to sharing popular files among peers. In contrast, the proposed protocol incentivizes peers to distribute patch updates, which might be relevant only to IoT devices, using a blockchain-based lightning network. A manufacturer/owner named vendor of the IoT device commits a bid on the blockchain, which can be publicly verified by the members of the network. The nodes, called distributors, interested in delivering the patch update, compete among each other to exchange a piece of patch update with cryptocurrency payment. The pay-per-piece payments protocol addresses the problem of misbehavior between IoT devices and distributors as either of them may try to take advantage of the other. The pay-per-piece protocol is a form of a gradual release of a commodity like a patch update, where the commodity can be divided into small pieces and exchanged between the sender and the receiver building trust at each step as the transactions progress into rounds. The permissionless nature of the framework enables the proposal to scale as it incentivizes the participation of individual distributors. Thus, compared to the previous solutions, the proposed framework can scale better without any overhead and with reduced costs. A combination of the Bitcoin lightning network for cryptocurrency incentives with the BitTorrent delivery network is used to present a prototype of the proposed framework. Finally, a financial and scalability evaluation of the proposed framework is presented.

BENN: Bias Estimation Using a Deep Neural Network.

Utilizing existing methods for bias detection in machine learning (ML) models is challenging since each method: 1) explores a different ethical aspect of bias, which may result in contradictory output among the different methods; 2) provides output in a different range/scale and therefore cannot be compared with other methods; and 3) requires different input, thereby requiring a human expert's involvement to adjust each method according to the model examined. In this article, we present BENN, a novel bias estimation method that uses a pretrained unsupervised deep neural network. Given an ML model and data samples, BENN provides a bias estimation for every feature based on the examined model's predictions. We evaluated BENN using three benchmark datasets, one proprietary churn prediction model used by a European telecommunications company, and a synthetic dataset that includes both a biased feature and a fair one. BENN's results were compared with an ensemble of 21 existing bias estimation methods. The evaluation results show that BENN provides bias estimations that are aligned with those of the ensemble while offering significant advantages, including the fact that it is a generic approach (i.e., can be applied to any ML model) and does not require a domain expert.