5G networks are increasingly exposed to Distributed Denial of Service (DDoS) attacks launched by mobile botnets exploiting user equipment (UE). Existing studies have treated malware propagation and DDoS impact separately, with no prior simulation work having modeled how Device-to-Device (D2D) malware spreads and translates into network-wide DDoS stress. This paper introduces DDoSimu5G, an extension of Simu5G [33] integrated with the ONE simulator [24], which enables, for the first time, the combined modeling of D2D malware propagation, UE mobility, and botnet-driven traffic loads on 5G infrastructure. The framework is aligned with 3GPP Proximity based Services (ProSe) in the 5G System (5GS) TS 23.304 [1] specifications, supports configurable attack scenarios, and generates diverse datasets, including infection logs, mobility traces, and PCAP traffic. By linking propagation dynamics with DDoS effects, DDoSimu5G provides the research community with a reproducible open-source tool for studying and mitigating emerging D2D-driven threats in 5G networks.

The automotive domain is becoming increasingly complex through the integration of new technologies. As a result, cybersecurity is recognized as a pressing issue. This study focuses on the ISO/SAE 21434:2021 standard for road vehicles cybersecurity engineering, evaluating the effectiveness of the standard’s risk assessment approach. The standard suggests a set of assessment steps, and previous research has shown that practitioners often face challenges during assessment execution. The absence of clear, structured guidelines within the standard leads to different interpretations, resulting in inconsistent assessment approaches. This inconsistency makes it difficult to compare and measure the quality of the assessments. Our study uses design science methodology to create a new cybersecurity risk assessment ontology in the automotive domain, describing the relationships and interdependencies between cybersecurity risk assessment activities, stakeholders, and work packages. The ontology model is evaluated in a case study at a leading automotive systems supplier to validate the model’s suitability for developing a cybersecurity risk assessment method. The findings indicate that the ontology model provides an improved understanding of the underlying risk assessment activities and allows for a structured method for extracting procedural steps according to the standard. This systematic approach increases the cybersecurity risk assessment conformity and the consistency of assessment results. In conclusion, this paper gives valuable insights and actionable recommendations for stakeholders, researchers, and organizations seeking to improve the cybersecurity risk assessment process in the automotive domain. • Knowledge representation of cybersecurity risk assessment in the automotive domain. • Formal ontology model for the ISO/SAE 21434:2021 standard risk assessment activities. • Defining inherent dependencies to process documentation and stakeholder roles. • Applying ontology model structures to develop risk assessment method in a case study. • Workflow diagrams are applied to improve the consistency of the analysis activities.

Malicious coresidency is a precursor to side-channel attacks that target information leakage. In this paper, we seek to understand the interactions between a defender (the cloud service provider) who tries to detect malicious coresidency by an attacker, who in turn attempts to co-reside its VM with a victim VM on the same physical machine by exploiting the VM allocation policy employed by the cloud service provider while at the same time, trying to evade detection. The problem is modeled as a two-player game. Specifically, the attacker chooses how long to keep its VM operational before terminating and relaunching it to increase its odds of success. On the other hand, the defender attempts to detect and penalize malicious VMs based on their activity in a given time window. The defender estimates a maliciousness measure for all active VMs which then modulates the likelihood of a specific VM being migrated to a different physical machine. We study the equilibrium strategies for both players for different ranges of environment parameters and show the non-existence of equilibrium with pure strategies. Subsequently, we characterize the equilibrium of the game with mixed strategies.

We have recently seen an increasing number of attacks that are distributed, and span an entire wide area network (WAN). Today, typically, intrusion detection systems (IDSs) are deployed at enterprise scale and cannot handle attacks that cover a WAN. Moreover, such IDSs are implemented at a single entity that expects to look at all packets to determine an intrusion. Transferring copies of raw packets to centralized engines for analysis in a WAN can significantly impact both network performance and detection accuracy. In this paper, we propose Jaal, a framework for achieving accurate network intrusion detection at scale. The key idea in Jaal is to monitor traffic and construct in-network packet summaries. The summaries are then processed centrally to detect attacks with high accuracy. The main challenges that we address are (a) creating summaries that are concise, but sufficient to draw highly accurate inferences and (b) transforming traditional IDS rules to handle summaries instead of raw packets. We implement Jaal on a large scale SDN testbed. We show that on average Jaal yields a detection accuracy of about 98%, which is the highest reported for ISP scale network intrusion detection. At the same time, the overhead associated with transferring summaries to the central inference engine is only about 35% of what is consumed if raw packets are transferred.

A wireless camera network can provide situation awareness information (e.g., humans in distress) in scenarios such as disaster recovery. If such camera sensors are battery operated, sending raw video feeds back to a central controller can be expensive in terms of energy consumption. Further, if all cameras were to use the optimal processing algorithm for object decision, they may also expend unnecessary energy. Stated otherwise, cameras that capture the same objects may not all have to use the optimal algorithm to achieve a desired accuracy, and this can save processing energy costs. In this paper, our objective is to design and implement a framework that can support coordination among cameras to deliver highly accurate detection of objects in an energy efficient way. The framework, which we call EECS (for energy efficient camera sensors), estimates the detection accuracy and energy costs incurred (both the processing and communication costs are taken into account) with each detection algorithm for each camera, and comes up with a choice of cameras for sending information pertaining to the object of interest. This set of cameras and the video processing algorithms that they must use, are chosen so as to minimize the energy expenditures, given a desired detection accuracy. We implement EECS on a camera network built with smartphones, and demonstrate that it reduces the energy consumption by up to 40% while ensuring a object detection accuracy of over 86%.

We study optimal monitor placement for intrusion detection in networks with persistent attackers. The problem is modeled as a stochastic game in which the attacker attempts to control targets by delivering malicious packets while the defender tries to detect such attempts. The state of the game is determined by the target end-systems in the network, each of which can be in either a healthy or a compromised state. Compromised targets are controlled by the attacker and may be used to inject malicious packets into the network to attack healthy targets. In addition, a random re-imaging process is deployed on all targets to regain control of compromised targets. We find the game value and the equilibrium strategies for both players under different assumptions on the knowledge of the state at the defender.

We investigate the delay limited secrecy capacity of the flat fading channel under two different assumptions on the available transmitter channel state information (CSI). The first scenario assumes perfect prior knowledge of both the main and eavesdropper channel gains. Here, upper and lower bounds on the delay limited secrecy capacity are derived, and shown to be tight in the high signal-to-noise ratio (SNR) regime. In the second scenario, only the main channel CSI is assumed to be available at the transmitter where, remarkably, we establish the achievability of a non-zero delay-limited secure rate, for a wide class of channel distributions, with a high probability. In the two cases, our achievability arguments are based on a novel two-stage key-sharing approach that overcomes the secrecy outage phenomenon observed in earlier works.

In this paper, optimal resource allocation policies are characterized for wireless cognitive networks under the spectrum leasing model. We propose cooperative schemes in which secondary users share the time-slot with primary users in return for cooperation. Cooperation is feasible only if the primary system's performance is improved over the non-cooperative case. First, we investigate a scheduling problem where secondary users are interested in immediate rewards. Here, we consider both infinite and finite backlog cases. Then, we formulate another problem where the secondary users are guaranteed a portion of the primary utility, on a long-term basis, in return for cooperation. Finally, we present a power allocation problem where the goal is to maximize the expected net benefit defined as utility minus cost of energy. Our proposed scheduling policies are shown to outperform non-cooperative scheduling policies, in terms of expected utility and net benefit, for a given set of feasible constraints. Based on Lyapunov optimization techniques, we show that our schemes are arbitrarily close to the optimal performance at the price of reduced convergence rate.

Optimal transmission scheduling in wireless cognitive networks is considered under the spectrum leasing model. We propose a cooperative scheme in which secondary nodes share the time slot with primary nodes in return for cooperation. Cooperation is feasible only if the system's performance is improved over the non-cooperative case. First, we investigate a scenario where secondary users are interested in immediate rewards. Then, we formulate another problem where the secondary users are guaranteed a portion of the primary utility, on a long term basis, in return for cooperation. In both scenarios, our proposed schemes are shown to outperform non-cooperative scheduling schemes, in terms of both individual and total expected utility, for a given set of feasible constraints. Based on Lyapunov Optimization techniques, we show that our schemes are arbitrarily close to the optimal performance at the price of reduced convergence rate.

In this paper, the delay limited secrecy capacity of the flat fading channel is investigated under two different assumptions on the available transmitter channel state information (CSI). The first scenario assumes perfect prior knowledge of both the main and eavesdropper channel gains. Here, upper and lower bounds on the secure delay limited capacity are derived and shown to be tight in the high signal-to-noise ratio (SNR) regime (for a wide class of channel distributions). In the second scenario, only the main channel CSI is assumed to be available at the transmitter. Remarkably, under this assumption, we establish the achievability of non-zero secure rate (for a wide class of channel distributions) under a strict delay constraint. In the two cases, our achievability arguments are based on a novel two-stage approach that overcomes the secrecy outage phenomenon observed in earlier works.