Request pattern change-based cache pollution attack detection and defense in edge computing

Through caching popular contents at the network edge,wireless edge caching can greatly reduce both the content request latency at mobile devices and the traffic burden at the core network.However,popularity-based caching strategies are vulnerable to Cache Pollution Attacks(CPAs)due to the weak security protection at both edge nodes and mobile devices.In CPAs,through initiating a large number of requests for unpopular contents,malicious users can pollute the edge caching space and degrade the caching efficiency.This paper firstly integrates the dynamic nature of content request and mobile devices into the edge caching framework,and introduces an eavesdroppingbased CPA strategy.Then,an edge caching mechanism,which contains a Request Pattern Change-based Cache Pollution Detection(RPC2PD)algorithm and an Attack-aware Cache Defense(ACD)algorithm,is proposed to defend against CPAs.Simulation results show that the proposed mechanism could effectively suppress the effects of CPAs on the caching performance and improve the cache hit ratio.

Adaptive multilevel fuzzy-based authentication framework to mitigate Cache side channel attack in cloud computing

Cloud computing supports multitenancy to satisfy the users’demands for accessing resources and simultaneously it increases revenue for cloud providers.Cloud providers adapt multitenancy by virtualizing the resources,like CPU,network interfaces,peripherals,hard drives and memory using hypervisor to fulfill the demand.In a virtualized environment,many virtual machines(VMs)can run on the same core with the help of the hypervisor by sharing the resources.The VMs running on the same core are the target for the malicious or abnormal attacks like side channel attacks.Among various side channel attacks in cloud computing,cache-based side channel attack is one that leaks private information of the users based on the shared resources.Here,as the shared resource is the cache,a process can utilize the cache usage of another by cache contention.Cache sharing provides a way for the attackers to gain considerable information so that the key used for encryption can be inferred.Discovering this side channel attack is a challenging task.This requires identification of a feature that influences the attack.Even though there are various techniques available in the literature to mitigate such attacks,an effective solution to reduce the cache-based side channel attack is still an issue.Therefore,a novel fuzzy rule-based mechanism is integrated to detect the cache side channel attackers by monitoring the cache data access(CDA).The factor that determines the attack is CDA in a log file created by the framework during authorization.The proposed framework also utilizes certain security properties including ECC and hashing for the privacy preservation and the decision is made with the aid of a fuzzy logic system.

Security Attacks in Named Data Networking: A Review and Research Directions

Contents such as audios,videos,and images,contribute most of the Internet traffic in the current paradigm.Secure content sharing is a tedious issue.The existing security solutions do not secure data but secure the communicating endpoints.Named data networking(NDN)secures the data by enforcing the data publisher to sign the data.Any user can verify the data by using the public key of the publisher.NDN is resilient to most of the probable security attacks in the TCP/IP model due to its new architecture.However,new types of attacks are possible in NDN.This article surveys the most significant security attacks in NDN such as interest flooding attacks,cache privacy attacks,cache pollution attacks,and content poisoning attacks.Each attack is classified according to their behavior and discussed for their detection techniques,countermeasures,and the affected parameters.The article is an attempt to help new researchers in this area to gather the domain knowledge of NDN.The article also provides open research issues that could be addressed by researchers.

Cache Pollution Prevention Mechanism Based on Deep Reinforcement Learning in NDN

As a representative architecture of contentcentric paradigms for the future Internet,named data networking(NDN)enables consumers to retrieve content duplicates from either the original server or intermediate routers.Each node of NDN is equipped with cache that buffers but not validates the data,making it vulnerable to various attacks.Cache pollution,one of the specific attacks in NDN,fraudulently alters the cached contents by excessively requesting worthless information,squeezing the space of real popular contents and thus degrading the experience of normal users.In order to address the issue,this paper proposes a defense scheme based on deep reinforcement learning(DRL)against cache pollution attack,in which whether a data packet is to be cached is decided by a trained intelligent agent,that is adaptive to dynamic network states and following long term rewards,the accumulative data-requesting delays.Finally,the DRL-based scheme is evaluated and compared to two other existing schemes.Experimental results show that the proposed defense mechanism outperforms the others significantly,and is proved to be effective against cache pollution attacks.

Secure Speculation via Speculative Secret Flow Tracking

Speculative execution attacks can leak arbitrary program data under malicious speculation,presenting a severe security threat.Based on two key observations,this paper presents a software-transparent defense mechanism called speculative secret flow tracking(SSFT),which is capable of defending against all cache-based speculative execution attacks with a low performance overhead.First,we observe that the attacker must use array or pointer variables in the victim code to access arbitrary memory data.Therefore,we propose a strict definition of secret data to reduce the amount of data to be protected.Second,if the load is not data-dependent and control-dependent on secrets,its speculative execution will not leak any secrets.Thus,this paper introduces the concept of speculative secret flow to analyze how secret data are obtained and propagated during speculative execution.By tracking speculative secret flow in hardware,SSFT can identify all unsafe speculative loads(USLs)that are dependent on secrets.Moreover,SSFT exploits three different methods to constrain USLs’speculative execution and prevent them from leaking secrets into the cache and translation lookaside buffer(TLB)states.This paper evaluates the performance of SSFT on the SPEC CPU 2006 workloads,and the results show that SSFT is effective and its performance overhead is very low.To defend against all speculative execution attack variants,SSFT only incurs an average slowdown of 4.5%(Delay USL-L1Miss)or 3.8%(Invisible USLs)compared to a non-secure processor.Our analysis also shows that SSFT maintains a low hardware overhead.